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Rashed, Noor A., Yossra H. Ali, and Tarik A. Rashid. "Advancements in Optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based Algorithms." Algorithms 17, no. 9 (2024): 416. http://dx.doi.org/10.3390/a17090416.
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The research work on optimization has witnessed significant growth in the past few years, particularly within multi- and single-objective optimization algorithm areas. This study provides a comprehensive overview and critical evaluation of a wide range of optimization algorithms from conventional methods to innovative metaheuristic techniques. The methods used for analysis include bibliometric analysis, keyword analysis, and content analysis, focusing on studies from the period 2000–2023. Databases such as IEEE Xplore, SpringerLink, and ScienceDirect were extensively utilized. Our analysis reveals that while traditional algorithms like evolutionary optimization (EO) and particle swarm optimization (PSO) remain popular, newer methods like the fitness-dependent optimizer (FDO) and learner performance-based behavior (LPBB) are gaining attraction due to their adaptability and efficiency. The main conclusion emphasizes the importance of algorithmic diversity, benchmarking standards, and performance evaluation metrics, highlighting future research paths including the exploration of hybrid algorithms, use of domain-specific knowledge, and addressing scalability issues in multi-objective optimization.
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Banbhrani, Santosh Kumar, Bo Xu, Haifeng Liu, and Hongfei Lin. "SC-Political ResNet: Hashtag Recommendation from Tweets Using Hybrid Optimization-Based Deep Residual Network." Information 12, no. 10 (2021): 389. http://dx.doi.org/10.3390/info12100389.
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Hashtags are considered important in various real-world applications, including tweet mining, query expansion, and sentiment analysis. Hence, recommending hashtags from tagged tweets has been considered significant by the research community. However, while many hashtag recommendation methods have been developed, finding the features from dictionary and thematic words has not yet been effectively achieved. Therefore, we developed an effective method to perform hashtag recommendations, using the proposed Sine Cosine Political Optimization-based Deep Residual Network (SC-Political ResNet) classifier. The developed SCPO is designed by integrating the Sine Cosine Algorithm (SCA) with the Political Optimizer (PO) algorithm. Employing the parametric features from both, optimization can enable the acquisition of the global best solution, by training the weights of classifier. The hybrid features acquired from the keyword set can effectively find the information of words associated with dictionary, thematic, and more relevant keywords. Extensive experiments are conducted on the Apple Twitter Sentiment and Twitter datasets. Our empirical results demonstrate that the proposed model can significantly outperform state-of-the-art methods in hashtag recommendation tasks.
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Sahu, Sai Shaktimayee, Suresh Chandra Satapathy, and Anima Naik. "Hybridization of Social Group Optimization and Differential Evolution Algorithm for Solving Speed Reducer Design Problem." Indian Journal Of Science And Technology 17, no. 23 (2024): 2455–62. http://dx.doi.org/10.17485/ijst/v17i23.965.
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Objectives: The objective of this study is to present a hybrid approach named SGO-DE, which combines the Social Group Optimization (SGO) algorithm and differential evolution (DE), aiming to balance exploration and exploitation capacities to improve the accuracy of the optimization algorithm in finding optimal solution for speed reducer design problem. This hybrid approach is simulated for a speed reducer mechanical engineering design problem and the results are compared to several other state-of-the-art optimization algorithms. Method: To improve the exploration and exploitation of SGO, in its acquiring phase Differential Evolution (DE) is introduced. The individual candidate solutions derived from the Improving phase of SGO tries to acquire better values using DE. This helps in striking a better balance between exploration and exploitation, there by achieving improved optimal values and not getting trapped in local optima. The performance of the SGO-DE method is then evaluated and compared to other optimization algorithms through experimentation on the speed reducer design challenge. Findings: The findings of this study indicate that the SGO-DE hybrid approach outperforms other state-of-the-art algorithms by a significant margin in terms of optimization results. The numbers of function evaluations (FEs) significantly go low as less as 6000 compared to other state-of-the algorithms. The comparison demonstrates the efficacy of the SGO-DE method in enhancing solution quality and speeding up execution. Novelty: The novelty of this study lies in the development a hybrid approach of SGO and DE which is efficient in achieving competitive performance in less numbers of function evaluation in speed reducer design problem. This hybridization can strike a better balance between exploration and exploitations. Keywords: SGO, DE, Hybridization, Nature-inspired, Optimization algorithm
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Marzieh, Eskandari, and Hassani Zeinab. "Intelligent application for Heart disease detection using Hybrid Optimization algorithm." Journal of Algorithms and Computation, no. 1 (January 1, 2019): 15–27. https://doi.org/10.5281/zenodo.4823916.
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Prediction of heart disease is very important because it is one of the causes of death around the world. More- over, heart disease prediction in the early stage plays a main role in the treatment and recovery disease and reduces costs of diagnosis disease and side effects it. Ma- chine learning algorithms are able to identify an effective pattern for diagnosis and treatment of the disease and identify effective factors in the disease. this paper is in- vestigated a new hybrid algorithm of Whale Optimiza- tion and Dragonfly algorithm using a machine learning algorithm. the hybrid algorithm employs a Support Vec- tor Machine algorithm for effective Prediction of heart disease. Proposed method is evaluated by Cleveland standard heart disease dataset. The experimental re- sult indicates that the SVM accuracy of 88.89 % and nine features are selected in this respect.
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Sai, Shaktimayee Sahu, Chandra Satapathy Suresh, and Naik Anima. "Hybridization of Social Group Optimization and Differential Evolution Algorithm for Solving Speed Reducer Design Problem." Indian Journal of Science and Technology 17, no. 23 (2024): 2455–62. https://doi.org/10.17485/IJST/v17i23.965.
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Abstract <strong>Objectives:</strong> The objective of this study is to present a hybrid approach named SGO-DE, which combines the Social Group Optimization (SGO) algorithm and differential evolution (DE), aiming to balance exploration and exploitation capacities to improve the accuracy of the optimization algorithm in finding optimal solution for speed reducer design problem. This hybrid approach is simulated for a speed reducer mechanical engineering design problem and the results are compared to several other state-of-the-art optimization algorithms. <strong>Method:</strong> To improve the exploration and exploitation of SGO, in its acquiring phase Differential Evolution (DE) is introduced. The individual candidate solutions derived from the Improving phase of SGO tries to acquire better values using DE. This helps in striking a better balance between exploration and exploitation, there by achieving improved optimal values and not getting trapped in local optima. The performance of the SGO-DE method is then evaluated and compared to other optimization algorithms through experimentation on the speed reducer design challenge.<strong> Findings:</strong> The findings of this study indicate that the SGO-DE hybrid approach outperforms other state-of-the-art algorithms by a significant margin in terms of optimization results. The numbers of function evaluations (FEs) significantly go low as less as 6000 compared to other state-of-the algorithms. The comparison demonstrates the efficacy of the SGO-DE method in enhancing solution quality and speeding up execution. <strong>Novelty:</strong> The novelty of this study lies in the development a hybrid approach of SGO and DE which is efficient in achieving competitive performance in less numbers of function evaluation in speed reducer design problem. This hybridization can strike a better balance between exploration and exploitations. <strong>Keywords:</strong> SGO, DE, Hybridization, Nature-inspired, Optimization algorithm
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Donetsk, National Technical University, Iaroslav Dorohyi, and Olena Doroha-Ivaniuk. "REQUIREMENT PRIORITIZATION IN THE DEVELOPMENT OF SOFTWARE PROJECTS FOR CRITICAL INFRASTRUCTURE OBJECTS." All-Ukrainian scientific collection "Scientific works of the Donetsk National Technical University. Series: "Computing technology and automation" 1, no. 1 (2024): 9–25. https://doi.org/10.5281/zenodo.10804944.
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The objective of the study is to develop an algorithm for prioritizing requirements in the development of software for critical infrastructure object projects. Requirement development is a fundamental phase in any software project, as this phase involves the identification, processing, and manipulation of requirements. The primary source of these requirements is project stakeholders, taking into account project constraints and limits. The number of requirements varies for each software project for a critical infrastructure object, hence the term requirement prioritization pertains to determining the priority order of executing software requirements based on considerations and decisions of stakeholders. Various proposed optimization algorithms are employed to address optimization tasks. This paper presents the main stages of basic optimization algorithms, some of their modifications aimed at enhancing their efficiency in solving such types of problems. Additionally, a hybrid approach based on WOA and GWO optimization algorithms is proposed, combining the advantages of each algorithm to determine the priority of requirements for critical infrastructure object software. Furthermore, a dataset from the SKUDA project is provided, utilized in this research, meeting the requirements of a real software project for evaluating the proposed method. The scientific novelty lies in the modification, application, and combination of results from well-known GWO and WOA algorithms to address the requirement prioritization task for critical infrastructure object software projects. The proposed algorithm achieves an accuracy of 92% for the proposed set of requirements. <strong><em>Keywords: </em></strong><em>requirement prioritization, WOA (Whale Optimization Algorithm), GWO (Grey Wolf Optimization), critical infrastructure object, CI (Critical Infrastructure), hybrid approach.</em>
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Emima, A., and D. I. George Amalarethinam. "A Hybrid Model of Enhanced Teacher Learner Based Optimization (ETLBO) with Particle Swarm Optimization (PSO) Algorithm for Predicting Academic Student Performance." Indian Journal Of Science And Technology 18, no. 10 (2025): 772–83. https://doi.org/10.17485/ijst/v18i10.240.
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Objectives: A hybrid ETLBO-PSO model is developed to improve student performance predictions. It assesses intellectual, social, and economic background of students to increase accuracy of students performance predictions. The model optimizes selecting features, which reduces redundancy and increases efficiency. The efficacy is compared with existing Educational Data Mining techniques. Methods : This study integrates Enhanced Teachers Learners Based Optimization (ETLBO) and Particle Swarm Optimization (PSO) algorithm for optimal feature selection. The suggested technique is utilized as an algorithm for selecting features to identify the most significant elements for predicting student academic performance. The efficacy of the proposed feature selection technique is evaluated using three machine learning classifiers: Extreme Gradient Boosting (XGB), Light Gradient Boosting (LightGB), and Category Gradient Boosting (CatGB) in Student achievement Dataset in secondary education for Mathematics. Findings: The experimental results of ETLBO-PSO provides sustained excellent model performance while reducing accuracy decline. The Meta-Class model of ETLBO-PSO has an F1-score of 82.43%, which makes it an increasingly robust and reliable strategy. Furthermore, an innovative visual and intuitive method is employed to identify the aspects that most significantly impact the score, facilitating the interpretation and comprehension of the complete model. Novelty: ETLBO_PSO is integrated with SHAP (SHapley Additive exPlanations), and Meta-class Model are used to optimize student performance predictions with higher accuracy. Unlike traditional approaches, it continuously refines selecting features throughout training, solving high-dimensional data issues. SHAP's approach assures precise feature attribution, hence improving accessibility and making decisions. Keywords: Feature Selection, Enhance Teacher Learner based Optimization, Particle Swarm Optimization, Academic Student Performance, Classification Algorithm, Optimization Techniques, XGBoost, LGBoost, CATBoost
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M, Dr Gokuldhev, and ,Jyoti Kale. "A Hybrid Approach to Multi-Objective Task Scheduling in Cloud Computing: Merging Estimation of Distribution and Genetic Algorithms." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40743.
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In the evolving landscape of cloud computing, efficient task scheduling plays a pivotal role in optimizing resource utilization and enhancing performance. This paper presents a novel hybrid algorithm that combines the strengths of Estimation of Distribution Algorithms (EDA) and Genetic Algorithms (GA) to address multi-objective task scheduling challenges. The proposed approach aims to balance conflicting objectives such as minimizing execution time, energy consumption, and cost while maximizing resource utilization. By leveraging the probabilistic modeling capabilities of EDA and the evolutionary search efficiency of GA, the hybrid algorithm achieves a more diverse solution set and faster convergence compared to traditional methods. Extensive simulations demonstrate the effectiveness of the proposed algorithm across various cloud environments and task complexities. The results highlight significant improvements in achieving Pareto-optimal solutions, offering a robust framework for cloud service providers to enhance scheduling efficiency. This study underscores the potential of hybrid metaheuristic techniques in addressing the dynamic and complex nature of cloud computing task scheduling, paving the way for more resilient and adaptive cloud infrastructures. Keywords: Cloud Computing, Task Scheduling, Estimation of Distribution Algorithm, Genetic Algorithm, Multi-Objective Optimization, Load Balancing, Resource Utilization.
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Hulianytskyi, Leonid, and Oleg Rybalchenko. "Formalization of the Problem of Optimization of Base Places and Routes of the UAV Group." Cybernetics and Computer Technologies, no. 4 (December 30, 2021): 12–26. http://dx.doi.org/10.34229/2707-451x.21.4.2.
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Introduction. The problem of planning the mission of a set of heterogeneous unmanned aerial vehicles (UAVs)is considered, which is to survey and/or service a given set of targets in the field. A mathematical model of the problem and algorithms for its solving that is based on deterministic local search, as well as optimization by ant colonies are proposed. The efficiency of algorithms is investigated based on the results of solving problems with real objects in the field. The relative error of the results of each algorithm was obtained, which allowed to compare their efficiency. The purpose of the paper is to solve a routing problem in different ways to reduce overall mission cost and compare the efficiency. The problem statement considers multiple starting points and destinations (depots) for UAVs with determined capacity, so algorithms proposed in the paper are designed to optimize the initial placement. Each UAV has a maximum flight distance because of an energy limit, though vehicles can be recharged by visiting one of previously placed depots. The mission goal is to visit all the given targets while minimizing the overall cost, so fuel consumption over distance, depot placement, and resources needed to survey and/or service of the target by each UAV are considered as components of the final cost metric to be minimized considering a set of specific constraints. Results. To solve the given UAV routing problem, a max-min algorithm of ant systems was developed, which features step-by-step interaction of ants to form solutions, a hybrid taboo search algorithm and a deterministic local search algorithm - the decay vector method. The developed algorithms were tested both on the known travelling salesman problems, and on specially developed problems with multiple depots and additional restrictions. Conclusions. The proposed algorithms which are based on ant colony optimization are compared both in terms of accuracy and computation time. A hybrid algorithm achieved slightly better score, though computation time has increased. Keywords: routing, combinatorial optimization, UAV, local search, ant colony optimization algorithms.
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Nunez, Angel. "Hybrid systems in electrical distribution design with genetic algorithm." minerva 1, no. 1 (2020): 32–42. http://dx.doi.org/10.47460/minerva.v1i1.4.
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The incorporation of hybrid systems based on renewable sources for the optimization of electricity distribution systems and planning of power supply strategies using genetic algorithms (GA) is studied. A series of characteristics of electrical sub-stations was chosen and through simulations, data were obtained for the optimization of the existing infrastructure, which provides reliability, security, economic supply and quality of service. An algorithm was obtained with the optimal configuration of various components: photovoltaic panels, batteries, AC generator, fuel cell and inverter, which in the case of mono-objective optimization, minimized total system costs throughout its useful life. The most appropriate control strategy or combination of control variables was obtained to minimize costs. Keywords: Electrical distribution, Hybrid systems, Genetic algorithms. References [1]D. F. Pinzón, «Diseño óptimo de Sistemas de Distribución,» Universidad Nacional de Colombia, Bogotá, 2014. [2]F. A. Mendoza Lameda, «Diseño multiobjetivo y multietapa de sistemas de distribución de energía eléctrica aplicando algoritmos evolutivos,» Universidad de Zaragoza, Zaragoza, 2010. [3]L. Miró Hernández and R. Vizcón Toledo, «Sistema Hibrido Propuesto Para la Generación de Electricidad en un Policlínico,» Revista Avanzada Científica, vol. 9, nº 2, pp. 50-56, 2006. [4]J. Lagunas M., C. Ortega S. and P. Caratozzolo M., «Control supervisorio para sistemas híbridos de geración eléctrica basado en lógica difusa,» Boletin UE, Monterrey, 2005. [5]J. L. Bernal Agustín, «Aplicación de algoritmos genéticos al diseño óptimo de sistemas de distribución de energía eléctrica,» Universidad de Zaragoza, Zaragoza, 1998. [6]V. MIranda, J. V. Ranito and L. Proenca, «Genetic Algorithms in Optimal Multistage Distribution Network Planning, » IEEE, Porto, 1994. [7]I. Ramirez-Rosado and J. Dominguez-Navarro, «Computer Aided Desing of Power Distribution Systems: Multiobjective Mathematical Simulations» International Journal of Power and Energy Systems, vol. 19, nº 4, pp. 1801-1810, 2004.
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Farooq, Omer, and Jasmeen Gill. "Vegetable Grading and Sorting using Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 13–21. http://dx.doi.org/10.22214/ijraset.2022.40407.
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Abstract: Agriculture and food industry are the backbone of any country. Food industry is the prime contributor in agricultural sector. Thus, automation of vegetable grading and sorting is the need of the hour. Since, artificial neural networks are best suited for automated pattern recognition problems; they are used as a classification tool for this research. Back propagation is the most important algorithm for training neural networks. But, it easily gets trapped in local minima leading to inaccurate solutions. Therefore, some global search and optimization techniques were required to hybridize with artificial neural networks. One such technique is Genetic algorithms that imitate the principle of natural evolution. So, in this article, a hybrid intelligent system is proposed for vegetable grading and sorting in which artificial neural networks are merged with genetic algorithms. Results show that proposed hybrid model outperformed the existing back propagation based system. Keywords: Vegetable grading and sorting; artificial neural networks; Particle Swarm Optimization; Hybrid intelligent system; Pattern recognition
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M.M, Muhammad, B. Muhammad Abdulsalam, U. Musa Bukar, and Dauda Lawan. "Development of Hybrid Crow Search Algorithm and Smell Agent Optimization for Optimal Deployment of Distributed Generators on Radial Distribution Networks to Improve Power Delivery." Development of Hybrid Crow Search Algorithm and Smell Agent Optimization for Optimal Deployment of Distributed Generators on Radial Distribution Networks to Improve Power Delivery 8, no. 12 (2024): 8. https://doi.org/10.5281/zenodo.10464623.
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The optimal location and sizing of Distributed Generation (DG) are crucial factors in integrating DGs into a network to minimize power losses and improve the voltage profile. This paper presents a hybridized solution that combines the Crow Search Algorithm (CSA) and Smell Agent Optimization (SAO) algorithm for determining the optimal location and sizing of DGs. The CSA, SAO, and the proposed CSA-SAO method were modeled and applied to validate their effectiveness using the standard IEEE-33 test bus. The results were compared with the base case scenario, which did not include DGs. For the 33-bus system, the CSA method achieved a 37.54% reduction in system losses and a 40.99% improvement in the overall voltage profile. The SAO method resulted in a 45.43% reduction in losses and a 54.88% improvement in the average voltage profile. The proposed hybrid CSA-SAO method demonstrated even better performance, with a 47.56% reduction in losses and a 63.47% improvement in the average voltage profile. This comparison indicates that the proposed hybrid model is valid for solving optimal DG allocation problems. The results suggest that combining the CSA and SAO algorithms in a hybrid approach produces superior results compared to using the methods independently. Keywords:- Crow Search Algorithm; Smell Agent Optimization; Voltage Profile; Power Losses; Distributed Generation.
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Mittal, Amit Kumar, and Kirti Mathur. "An Efficient Short-Term Solar Power Forecasting by Hybrid WOA-Based LSTM Model in Integrated Energy System." Indian Journal Of Science And Technology 17, no. 5 (2024): 397–408. http://dx.doi.org/10.17485/ijst/v17i5.2020.
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Objectives: Due to the irregular nature of sun irradiation and other meteorological conditions, solar power generation is constantly loaded with risks. When solar radiation data isn't captured and sky imaging equipment isn't available, improving forecasting becomes a more difficult endeavor. So our objective to improve the forecasting accuracy for next year solar power generation data. Methods: Our research used a real numerical solar power dataset of Australia and Germany and a standard approach for preprocessing. The feature selection in this research uses the Whale Optimization Algorithm (WOA). A Long Short-Term Memory (LSTM) method is utilized to determine the accuracy of solar power forecasts. The HHO (Harris Hawks Optimization) technique is also used to improve solar power forecasting accuracy. The performances were analyzed and the proposed method is employed in the python platform. Findings: The findings show that the suggested technique considerably increases the accuracy of short-term solar power forecasts for proposed method is 3.07 in comparison of LSTM and SVM at different data types and 15 min and 60 min interval. Novelty: The key novelties of this research is hybrid strategy for improving the precision of solar power forecasting for short periods of time. Including the Whale Optimization Algorithm (WOA), Long Short-Term Memory (LSTM), and Harris Hawks Optimization (HHO). Keywords: Power generation, Solar power forecasting, Whale optimization algorithm, Long ShortTerm Memory, Harris hawk's optimization
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Hulianytskyi, Leonid, and Oleg Rybalchenko. "Route Optimization in Mission Planning for Hybrid DRONE+VEHICLE Transport Systems." Cybernetics and Computer Technologies, no. 3 (September 29, 2023): 44–58. http://dx.doi.org/10.34229/2707-451x.23.3.4.
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Introduction. In the context of modern technologies and the widespread use of unmanned aerial vehicles (UAVs) in various fields of activity, the study of optimizing their mission planning becomes increasingly relevant. This is particularly true for hybrid systems where UAVs are integrated with ground transportation ("Drone+Vehicle"). The article deals with the aspects of optimizing the mission routes of a drone that can be transported by a specialized vehicle, performing reconnaissance or maintenance missions for the presented targets. A mathematical model has been developed that allows integrating various planning stages, including determining the direction of the vehicle based on the data obtained during the drone's mission. The purpose of the paper is development and application of mathematical and software-algorithmic tools, in particular, based on the ideas of swarm intelligence, in planning operations for the inspection or maintenance of a given set of objects using hybrid systems "Drone+Vehicle". Results. A mathematical model of the problem of routing hybrid systems of the "Drone+Vehicle" type has been formed. Greedy type algorithms, deterministic local search and ant colony optimization (ACO) to solve the problem are proposed, implemented and analyzed. A computational experiment has been conducted to demonstrate the advantages of the AMC algorithm in terms of speed and efficiency, even for problems of high dimensionality. Conclusions. The proposed approach allows to cover several stages of planning the mission of a hybrid "Drone+Vehicle" system with an aggregated mathematical model. The developed mathematical model also covers the problem of choosing the direction of further movement of a vehicle located in a certain place, depending on the analysis of the results of the inspection of specified targets that may contain objects for inspection or maintenance. To solve the formulated combinatorial optimization problem, greedy type, deterministic local search, and OMC algorithms have been developed. The results of the computational experiment demonstrate the superiority of the OMC algorithm over the combined "greedy + deterministic local search" algorithm. An important future direction of research is the development and application of routing models and algorithms that take into account the obstacles present on the ground. The developed mathematical apparatus allows to move on to consider problems in which the locations of the vehicle's base on the route are not specified but are determined depending on the configuration of the targets. Keywords: unmanned aerial vehicles, hybrid systems, mission planning, route optimization, mathematcal modeling, ant colony optimization, logistics.
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Chintada, Sravani. "A Novel Method for Energy Efficient Clustering in Wireless Sensor Networks." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35010.
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Wireless Sensor Networks (WSNs) play a crucial role in various applications, including environmental monitoring, industrial automation, and healthcare. However, optimizing WSNs for efficient resource utilization, energy conservation, and reliable data transmission remains a challenging task due to the dynamic nature of the network environment and resource-constrained sensor nodes. In this study, we propose a Hybrid Firefly Genetic Algorithm (HFGA) for optimizing WSN performance. The HFGA combines the strengths of the firefly algorithm's global search capabilities and the genetic algorithm's local search and optimization efficiency. By integrating these two evolutionary algorithms, the HFGA aims to achieve superior performance in terms of energy efficiency, network coverage, and convergence speed. We evaluate the effectiveness of the proposed HFGA through extensive simulation experiments in various WSN scenarios. The results demonstrate that the HFGA outperforms traditional optimization approaches and baseline algorithms in optimizing WSN performance metrics. Furthermore, we discuss the practical implications and future research directions for deploying the HFGA in real-world WSN applications. Overall, this study contributes to advancing WSN optimization techniques and enhancing the reliability and efficiency of WSN deployments. Keywords: Clustering, Genetic algorithm, Firefly algorithm, FAG algorithm.
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Zhen, Nie Huailin Zhao: SIT Shanghai. "Research on Robot Path Planning Based on Dijkstra and Ant Colony Optimization." Journal of Information and Communication Engineering(JICE) Volume 6, Issue 1 (2020): 321–27. https://doi.org/10.5281/zenodo.4261640.
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This paper studied on the path planning problem in known environments. According to Dijkstra algorithm and ant colony optimization (ACO), a hybrid algorithm to search the path was designed. Based on the environment model, constructed by using visual graph method, Dijkstra algorithm was used for initial path planning. Then the ACO was improved and used to optimize the initial path to minimize the path of the robot. The simulation on MATLAB showed that the path planning algorithm based on Dijkstra-ACO has higher efficiency of path search and good effect of path planning, and the algorithm is effective and feasible. Keywords: Dijkstra Algorithm, Ant Colony Optimization (ACO), Path Planning
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K, Gayathri Devi. "A Hybrid Firefly Algorithm Approach for Job Shop Scheduling Problem." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 1436–44. http://dx.doi.org/10.22214/ijraset.2021.39536.
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Abstract: Job shop scheduling has always been one of the most sought out research problems in Combinatorial optimization. Job Shop Scheduling problems (JSSP) are categorized under NP hard problems. In recent years the meta heuristic algorithms have been proved effective to solve hardcore NP problem. Firefly Algorithm is one of such meta heuristic techniques which is nature inspired from firefly characteristic. Its potential can be enhanced further by hybridizing it with other known evolutionary algorithms and thereby getting improved results in less computational time. In this paper we have proposed a new hybrid technique christened as HyFA, by hybridizing Firefly algorithm(FA) with simulated annealing (SA) and Greedy heuristics approach (GHA). The hybrid technique has the advantages of all three algorithms and are combined in such a way that a quicker and better optimal solution is obtained. Our proposed HyFA is coded in Matlab with an objective to minimize the makespan (Cm). The novel hybrid technique is then used to evaluate 1-25 Lawrence problems taken from literature. The results show the proposed technique is more effective not only in getting optimal results but has significantly reduced computational time. Keywords: Scheduling, Optimisation, Job shop scheduling, Meta-heuristics, Firefly, Simulated Annealing, Greedy heuristics Approach.
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S, Sumathi, and Rajesh R. "A Dynamic BPN-MLP Neural Network DDoS Detection Model Using Hybrid Swarm Intelligent Framework." Indian Journal of Science and Technology 16, no. 43 (2023): 3890–904. https://doi.org/10.17485/IJST/v16i43.1718.
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Abstract <strong>Background/Objectives:</strong> The most untreated and severe cyber security issue in cloud computing is DDoS attack, this is being under research to find novel findings with less complexity and better efficiency to detect and mitigate this issue. In this research article, Artificial Neural Network (ANN) algorithms like Backpropogation neural network (BPN) and Multilayer perceptron (MLP) are implemented and their performance on intrusion detection by utilizing NSL-KDD dataset is demonstrated. <strong>Methods:</strong> Initially, NSL-KDD benchmark dataset construction is carried out in the range of (0-1) using min-max normalization technique. Following this, hybrid Harris Hawks optimization particle swarm optimization (HHO-PSO) is employed to reduce the dataset size by selecting significant features that represents anomaly in network traffic. This hybrid algorithm is also employed to tune the features selected which is assigned as initial weight vectors for both BPN and MLP intrusion detection system (IDS) models. These selected optimally tuned features are trained using 10-fold cross validation technique and the number of hidden neurons is fixed using thumb rule. After training, the hybrid BPN-MLP neural network IDS model is validated on test dataset and its performance is validated using performance metrics such as accuracy, precision, sensitivity, specificity and F1 score. <strong>Findings:</strong> The proposed hybrid HHO-PSO BPN and HHOPSO MLP IDS model has achieved detection accuracy of and with F1 score of 0.9743 and 0.9800 respectively.<strong> Novelty:</strong> In ANN based intrusion detection schemes, the stochastic nature of model parameters is an important problem of concern. To handle this issue, a hybrid swarm intelligent algorithm called Harris hawks optimization particle swarm optimization (HHOPSO) is proposed to tune the model parameters, so that the network performance is enhanced. <strong>Keywords:</strong> Backpropogation Neural Network, Multilayer perceptron, Harris Hawks Optimization, Particle Swarm Optimization, Intrusion Detection System
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Hassan, Mustafa Hamid, Ameer Mohammed Al-obaidi, Sameer Alani, et al. "A Hybrid Bio-Inspired Optimization Scheme for RSU Distribution in Vehicular Ad-Hoc Network." International Journal of Emerging Technology and Advanced Engineering 13, no. 3 (2023): 152–58. http://dx.doi.org/10.46338/ijetae0323_16.
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—Vehicular Ad-Hoc Network (VANETs) is a trending modelthat plays maximum role inall the application of Intelligent Transport Systems (ITS). In general, VANETs are based on two communication types such as among vehicles and vehicles to infrastructure Roadside Units(RSU) based communication. In this paper, HACOGO approach is developedbased on ahybrid bio inspired optimization with the combination of Ant Colony Optimization (ACO) with Grasshopper Optimization Algorithm (GOA). The HACOGO is used to perform stable RSU distribution in VANETs. The ACO algorithm is used to help the vehicle to select the optimal path toward the destination and GOA algorithm highly magnificent vehicle is chosen as RSU. The Performance analysis of HACOGO is done by calculating the common parameters such as packet delivery ratio, end-to-end delay, packet loss and routing overhead. To analysis the effectiveness of the proposed model its results are compared with the earlier works. From the outcome it is proven using the HACOGO approach the end-to-end delay, packet loss and routing overhead is reduced as well as the packet delivery ratio of the network is increased than others. Keywords—Vehicular Ad-Hoc Network,Roadside Units, Ant Colony Optimization.
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Fitria, Mahrunissa Azmima Fitria, and Erwin Budi Setiawan. "Hybrid Deep Learning with GloVe and Genetic Algorithm for Sentiment Analysis on X: 2024 Election." Scientific Journal of Informatics 11, no. 3 (2024): 693–704. http://dx.doi.org/10.15294/sji.v11i3.8467.
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Purpose: This research analyzes sentiment on the 2024 Indonesian Presidential Election using data from X, employing a hybrid CNN-GRU model optimized with a Genetic Algorithm (GA) to improve accuracy and efficiency. It also explores GloVe feature expansion for enhanced sentiment classification, aiming for deeper insights into public opinion through advanced deep learning and optimization techniques. Methods: This research employs a deep learning approach that integrates Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) models, Term Frequency-Inverse Document Frequency (TF-IDF), Global Vectors (GloVe), and GA. The dataset comprises 62,955 Indonesian tweets focusing on the 2024 General Election using various keywords. Result: The results indicated that the Genetic Algorithm significantly improved model accuracy. The CNN-GRU + GA model achieved 84.72% accuracy for the Top 10 ranking, a 1.94% increase from the base model. In comparison, the GRU-CNN + GA model achieved 84.69% accuracy for the Top 5 ranking, a 2.76% increase from the base model, demonstrating enhanced performance with GA across configurations. Novelty: This research uses a hybrid CNN-GRU model to introduce a novel sentiment analysis approach for the 2024 Indonesian Presidential Election. The model enhances accuracy by combining CNN's spatial feature extraction with GRU's temporal context capture and GloVe's word semantics. Genetic Algorithm optimization further refines performance. Comprehensive pre-processing ensures high-quality data, and focusing on election-specific keywords adds relevance. This study advances sentiment analysis through its innovative hybrid model, feature expansion, and optimization techniques.
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Shigwan, Mayuri. "Comprehensive Travel Companion: A Hybrid Recommendation and Route Planning System for Globetrotting." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31157.
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This paper presents a comprehensive AI-powered travel companion system designed to enhance the travel experience for globetrotters. Leveraging cutting-edge artificial intelligence (AI) techniques, the system combines image recognition, recommendation algorithms, and route planning functionality to provide personalized travel recommendations and seamless navigation. The integration of collaborative filtering, content-based recommendation, and real-time route optimization algorithms ensures that users receive tailored suggestions and efficient travel routes based on their preferences, interests, and current context. The architecture, implementation details, and evaluation of the proposed system are discussed, highlighting its effectiveness in facilitating immersive and enjoyable travel experiences. Keywords: Travel Companion, Recommendation Systems, Route Planning, Image Recognition, Collaborative Filtering, Content-Based Recommendation, Dijkstra's Algorithm.
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Muhammad-Thani, S., S. O. Olabiyisi, E. O. Omidiora, and R. A. Ganiyu. "A Comparative Analysis of Metaheuristic Algorithms on Hybrid Features for Feature Selection on ECG-Based Arrhythmia Detection." Advances in Multidisciplinary & Scientific Research Journal Publications 12, no. 2 (2024): 1–16. http://dx.doi.org/10.22624/aims/digital/v11n2p1.
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Electrocardiogram (ECG)-based arrhythmia detection is crucial for diagnosing cardiovascular diseases promptly. However, the high-dimensional nature of ECG signals poses challenges for accurate detection. Feature selection plays a vital role in enhancing classification performance by identifying relevant features. This paper presents a comparative analysis of metaheuristic algorithms for feature selection in ECG-based arrhythmia detection using a hybrid feature set based on morphological and heart variability features. We evaluate the performance of Grey Wolf Optimization (GWO), and Firefly Algorithm (FFA) in terms of classification accuracy, sensitivity, Positive precision, and specificity. The experimental results shows that GWO performs FFA better in selecting relevant features for arrhythmia classification by 2.8%. However, both algorithms demonstrate the efficacy of metaheuristic algorithms and hybrid features in improving arrhythmia detection and provide insights into their comparative performance. Keywords: ECG Signal, Arrhythmia, Feature Selection, Metaheuristic Algorithms.
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M, DHARSHINI. "Enhancing Energy Efficiency in Industrial WSNs with a Hybrid Coot-LS Routing Algorithm and LSTM-Based DOM Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42802.
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In industrial Wireless Sensor Networks (WSNs), energy efficiency and reliable data transmission are critical challenges that need to be addressed to ensure sustainable and robust network operations. This paper proposes a novel energy-efficient routing protocol that integrates a Hybrid COOT-LS (Coot-Levy Search) algorithm with Long Short-Term Memory (LSTM)-based Dominant Object Motion (DOM) prediction. The routing protocol leverages the strengths of Hybrid Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to enhance routing efficiency and reduce energy consumption. The Hybrid PSO and ACO algorithms are employed to optimize routing paths by balancing exploration and exploitation, considering multiple factors such as energy levels, node distance, and reliability. The COOT-LS algorithm further refines these paths by incorporating a Levy flight mechanism to enhance the search process. Additionally, the LSTM-based DOM prediction provides accurate forecasts of network conditions, enabling dynamic adjustments to routing strategies in real time. Simulation results demonstrate that the proposed protocol significantly improves network lifetime, reduces energy consumption, and enhances data transmission reliability compared to traditional routing protocols. This approach provides a robust and scalable solution for industrial WSN applications, ensuring efficient and reliable network performance in dynamic and complex industrial environments. Keywords Energy efficiency , Routing efficiency ,Energy consumption , Exploration and exploitation , Node distance ,Network reliability , Levy flight mechanism ,Dynamic routing adjustment .
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Hulianitskyi, Leonid, Vyacheslav Korolyov, and Oleksandr Khodzinskyi. "An Overview of Algorithms for Solving Vehicle Routing Problems in the Quantum-Classical Cloud." Cybernetics and Computer Technologies, no. 2 (July 28, 2023): 23–31. http://dx.doi.org/10.34229/2707-451x.23.2.3.
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Introduction. The hope of solving the problem of the avalanche-like growth of requirements for computing power, essential for solving complex routing problems and other problems of combinatorial optimization, relies on the latest quantum computers, in the development of which governments and corporations invest multi-billion investments. The article examines modern routing algorithms and performs their analysis and verification, if the authors of the algorithm provided appropriate test programs. The purpose of the article is to review the current state of development in the field of development of routing algorithms for hybrid quantum-classical clouds, analyze them and propose a classification of algorithms. Results. Modern quantum computers (QCs) make it possible to find approximate solutions to some of mathematical problems faster than classical computers. The inaccuracy of the solutions obtained by the QC is a consequence of physical and technological limitations: calculation errors are caused by thermal noise, a small number of computational elements - qubits and connections between them, which requires the decomposition of the problem and the use of heuristic algorithms. The analysis of approaches to the solution of optimization problems on QC allows us to single out: quantum response and variational search of eigenvalues based on quantum logic gates as the general directions of development of the vast majority of algorithms for solving routing problems. The considered algorithms reduce the vehicle routing problem to a quadratic unconstrained binary optimization problem, which is isomorphic to the Hamilton-Ising model. In this form, the problem is suitable for embedding in QC, which finds an approximate solution that has the best statistical reliability or corresponds to the quantum state with the lowest energy. As a separate class, vehicle routing algorithms for classical computers that use quantum computing to accelerate problem solving can be distinguished. For example, neural networks that calculate weighting factors using QC or an ant algorithm that calculates a pheromone trail in a hybrid cloud. It should be mentioned the quantum-inspired algorithms, which are based on software tools for the simulation of QC and the corresponding libraries and allow creating an effective class of algorithms for solving problems of vehicle routing. Conclusions. Combining hardware quantum annealing with a number of software tools for calculating optimization problems for classical computers in a hybrid quantum-classical cloud service allows to obtain advantages in speed and accuracy of some types of complex optimization problems of a commercial scale, in particular, routing vehicles, which is already bringing substantial profits to a number of corporations. Keywords: vehicle routing problem, quantum computer, annealing, combinatorial optimization, traveling salesman problem, clustering, qubit.
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Amit, Kumar Mittal, and Mathur Kirti. "An Efficient Short-Term Solar Power Forecasting by Hybrid WOA-Based LSTM Model in Integrated Energy System." Indian Journal of Science and Technology 17, no. 5 (2024): 397–408. https://doi.org/10.17485/IJST/v17i5.2020.
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Abstract <strong>Objectives:</strong> Due to the irregular nature of sun irradiation and other meteorological conditions, solar power generation is constantly loaded with risks. When solar radiation data isn't captured and sky imaging equipment isn't available, improving forecasting becomes a more difficult endeavor. So our objective to improve the forecasting accuracy for next year solar power generation data. <strong>Methods:</strong> Our research used a real numerical solar power dataset of Australia and Germany and a standard approach for preprocessing. The feature selection in this research uses the Whale Optimization Algorithm (WOA). A Long Short-Term Memory (LSTM) method is utilized to determine the accuracy of solar power forecasts. The HHO (Harris Hawks Optimization) technique is also used to improve solar power forecasting accuracy. The performances were analyzed and the proposed method is employed in the python platform. <strong>Findings:</strong> The findings show that the suggested technique considerably increases the accuracy of short-term solar power forecasts for proposed method is 3.07 in comparison of LSTM and SVM at different data types and 15 min and 60 min interval. <strong>Novelty:</strong> The key novelties of this research is hybrid strategy for improving the precision of solar power forecasting for short periods of time. Including the Whale Optimization Algorithm (WOA), Long Short-Term Memory (LSTM), and Harris Hawks Optimization (HHO). <strong>Keywords:</strong> Power generation, Solar power forecasting, Whale optimization algorithm, Long Short­Term Memory, Harris hawk's optimization
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Yadav, Nitesh. "A Review Article on Brain Tumor Detection and Optimization using Hybrid Classification Algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 763–68. http://dx.doi.org/10.22214/ijraset.2021.38903.
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Abstract: This review focuses on different imaging techniques such as MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and timeconsuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Keywords: Brain tumor, data mining techniques, filtering techniques, MRI, classifiers, feature selection.
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Reddy, Tatireddy Subba, Sanjeevaiah K., Sajja Karthik, Mahesh Kumar, and Vivek D. "Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimization Algorithm." International Journal of Software Innovation 11, no. 1 (2023): 1–15. http://dx.doi.org/10.4018/ijsi.315661.
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In the field of digital data management, content-based image retrieval (CBIR) has become one of the most important research areas, and it is used in many fields. This system searches a database of images to retrieve most visually comparable photos to a query image. It is based on features derived directly from the image data, rather than on keywords or annotations. Currently, deep learning approaches have demonstrated a strong interest in picture recognition, particularly in extracting information about the features of the image. Therefore, a Densenet-121 is employed in this work to extract high-level and deep characteristics from the images. Afterwards, the training images are retrieved from the dataset and compared to the query image using a Bidirectional LSTM (BiLSTM) classifier to obtain the relevant images. The investigations are conducted using a publicly available dataset named Corel, and the f-measure, recall, and precision metrics are used for performance assessment. Investigation outcomes show that the proposed technique outperforms the existing image retrieval techniques.
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D, Babu Rajendra Prasad, D. Kulkarni A, and Ananthapadmanabha T. "Operation and Control of the Distributed Energy Resources to Improve the Power Quality in Electrical Distribution System Using Hbmo -Ann - A Novel Control Algorithm." Indian Journal of Science and Technology 15, no. 42 (2022): 2204–18. https://doi.org/10.17485/IJST/v15i42.2439.
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Abstract <strong>Objectives:</strong> To propose a novel hybrid control plan to enhance the power quality (PQ) of distributed energy sources (DERs) such as solar energy (SE), wind energy (WE), and battery energy storage system (BEES) technologies. <strong>Method:</strong> A BEES, a PV farm, and a wind farm are integrated into the grid to supply home loads. The ABMO-ANN control system is a novel method that combines the Hybrid Barnacles Mating Optimization Algorithm (HBMO) and the Artificial Neural Network (ANN) (ANN). The power is balanced by balancing the power consumed by BEES and the power provided by the VSI inverter in this new proposed algorithm.<strong> Findings:</strong> In this HBMO-ANN control model, the proportional integral (PI) gain parameter controller generates the signals based on the load variation to manage the hybrid RES energy sources optimally. The assumed variables as system parameters in this suggested prediction approach are direct current (DC) voltage and active with reactive power. <strong>Novelty:</strong> The new HBMO-ANN method creates optimal control, aiming to improve power system damping and sustain line voltage with the reactive power compensation provided. However, when compared to existing control methods, the performance of the HBMO-ANN approach with a PI controller is also justified. The suggested methodology is compared to existing methods in MATLAB/Simulink. <strong>Keywords:</strong> Power quality improvement; Renewable energy sources; Hybrid Barnacles Mating Optimization Algorithm; Reactive power; Power system
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Pande, Abhijit S., and Prakash G. Burade. "Intelligent Power Dispatch Optimization: A Genetic Algorithm Approach with Unified Power Flow Controller." Indian Journal Of Science And Technology 18, no. 11 (2025): 891–903. https://doi.org/10.17485/ijst/v18i11.3709.
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Objective: The goal of this project is to provide an advanced optimization approach for Dynamic Power Dispatch (DPD) by combining a Unified Power Flow Controller (UPFC) with a Genetic Algorithm (GA). The main objective is to improve power flow management and grid performance in complicated power networks. Methodology: The suggested method takes advantage of the capabilities of the UPFC, a versatile component of the Flexible AC Transmission System (FACTS), and combines them with the optimization power of GA. The UPFC provides close oversight over power flow, voltage ranges, and phase angles, while GA, known for its ability to solve multi-objective and nonlinear problems, improves the UPFC’s installation and operational settings. The effectiveness of this hybrid technique is proven by simulations using the IEEE 14-bus test system. Findings: Simulation results demonstrate that the integration of GA with UPFC yields significant improvements in power flow stability, reduces operational costs and transmission losses, and enhances voltage profiles compared to conventional DPD techniques. Furthermore, the approach increases grid flexibility and stability, addressing the evolving demands of modern power networks. Novelty: This study introduces a unique combination of UPFC and GA to address the challenges in DPD. The approach merges the real-time control capabilities of UPFC with the evolutionary optimization strengths of GA, presenting a scalable and adaptive solution for power dispatch optimization. Applications: The proposed technique is especially useful for improving the efficiency and reliability of current power grids, including those that use renewable energy sources. Its use in smart grid scenarios can help accelerate the transition to more sustainable and resilient energy systems. Keywords: Dynamic power dispatch; Unified Power Flow Controller; Genetic Algorithm; power system optimization; FACTS devices; and an IEEE 14bus testing system
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Kayest, Mamta, and Sanjay Kumar Jain. "An incremental learning approach for the text categorization using hybrid optimization." International Journal of Intelligent Computing and Cybernetics 12, no. 3 (2019): 333–51. http://dx.doi.org/10.1108/ijicc-12-2018-0170.
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Purpose Document retrieval has become a hot research topic over the past few years, and has been paid more attention in browsing and synthesizing information from different documents. The purpose of this paper is to develop an effective document retrieval method, which focuses on reducing the time needed for the navigator to evoke the whole document based on contents, themes and concepts of documents. Design/methodology/approach This paper introduces an incremental learning approach for text categorization using Monarch Butterfly optimization–FireFly optimization based Neural Network (MB–FF based NN). Initially, the feature extraction is carried out on the pre-processed data using Term Frequency–Inverse Document Frequency (TF–IDF) and holoentropy to find the keywords of the document. Then, cluster-based indexing is performed using MB–FF algorithm, and finally, by matching process with the modified Bhattacharya distance measure, the document retrieval is done. In MB–FF based NN, the weights in the NN are chosen using MB–FF algorithm. Findings The effectiveness of the proposed MB–FF based NN is proven with an improved precision value of 0.8769, recall value of 0.7957, F-measure of 0.8143 and accuracy of 0.7815, respectively. Originality/value The experimental results show that the proposed MB–FF based NN is useful to companies, which have a large workforce across the country.
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Dewinter, Maite, Christophe Vandeviver, Tom Vander Beken, and Frank Witlox. "Analysing the Police Patrol Routing Problem: A Review." ISPRS International Journal of Geo-Information 9, no. 3 (2020): 157. http://dx.doi.org/10.3390/ijgi9030157.
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Police patrol is a complex process. While on patrol, police officers must balance many intersecting responsibilities. Most notably, police must proactively patrol and prevent offenders from committing crimes but must also reactively respond to real-time incidents. Efficient patrol strategies are crucial to manage scarce police resources and minimize emergency response times. The objective of this review paper is to discuss solution methods that can be used to solve the so-called police patrol routing problem (PPRP). The starting point of the review is the existing literature on the dynamic vehicle routing problem (DVRP). A keyword search resulted in 30 articles that focus on the DVRP with a link to police. Although the articles refer to policing, there is no specific focus on the PPRP; hence, there is a knowledge gap. A diversity of approaches is put forward ranging from more convenient solution methods such as a (hybrid) Genetic Algorithm (GA), linear programming and routing policies, to more complex Markov Decision Processes and Online Stochastic Combinatorial Optimization. Given the objectives, characteristics, advantages and limitations, the (hybrid) GA, routing policies and local search seem the most valuable solution methods for solving the PPRP.
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Fefelova, I. M., V. I. Lytvynenko, and A. O. Fefelov. "Prediction of the tertiary structure of a protein on a two-dimensional triangular lattice by a hybrid evolutionary algorithm." Ukrainian Journal of Information Technology 3, no. 2 (2021): 27–32. http://dx.doi.org/10.23939/ujit2021.02.027.
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This work discusses the problem of forecasting the tertiary structure of a protein, based on its primary sequence. The problem is that science, with all its computing power and a set of experimental data, has not learned to build models that describe the process of protein molecule coagulation and predict the tertiary structure of a protein, based on its primary structure. However, it is wrong to assume that nothing is happening in this field of science. The regularities of folding (convolution) of the protein are known, methods for its modelling have been developed. Analysis of the current state of research in the field of these problems indicates the presence of shortcomings associated with the accuracy of forecasting and the time necessary to obtain the optimal solution. Consequently, the development of new computational methods, deprived of these shortcomings, seems relevant. In this work, the authors focused on the lattice model, which is a special case of the known hydrophobic-polar dill. protein conformation according to the chosen model, hybrid algorithms of cloning selection, differential are proposed. Since the processes of protein coagulation have not been fully understood, the researchers proposed several simplified models based on the physical properties of molecules and which leads to problems of combinatorial optimization. A hydrophobic-polar simplified model on the planar triangular lattice is chosen as a protein model. From the point of view of the optimization problem, the problem of protein folding comes down to finding a conformation with minimal energy. In lattice models, the conformation is represented as a non-self-cutting pathway. A hybrid artificial immune system in the form of a combination of clonal selection and differential evolution algorithms is proposed to solve this problem. The paper proposes a hybrid method and algorithm to solve the protein folding problem using the HP model on a planar triangular lattice. In this paper, a hybrid method and algorithm for solving the protein folding problem using the HP model on a planar triangular lattice are proposed. The developed hybrid algorithm uses special methods for encoding and decoding individuals, as well as the affinity function, which allows reducing the number of incorrect conformations (self-cutting solutions). Experimental studies on test hp-sequences were conducted to verify the effectiveness of the algorithm. The results of these experiments showed some advantages of the developed algorithm over other known methods. Experiments have been taught to verify the effectiveness of the proposed approach. The results labelled "Best" show the minimum energy values achieved over 30 runs, while the results labelled "Medium" show the robustness of the algorithm to achieve minima. Regarding robustness, the hybrid algorithm also offers an advantage, showing higher results. A comparative analysis of the performance results of the proposed algorithm on test sequences with similar results of other published methods allows us to conclude the high efficiency of the developed method. In particular, the result is more stable, and, in some cases, conformations with lower energy are obtained. Keywords: protein folding; hydrophobic-polar model; clonal selection; differential evolution; artificial immune systems; hydrophobic-polar model.
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J, Vishwesh, and Raviraj P. "Improved Differential Evolution with Stacked Auto Encoder for EEG Motor Imagery Classification." Indian Journal of Science and Technology 16, no. 6 (2023): 391–400. https://doi.org/10.17485/IJST/v16i6.2076.
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ABSTRACT <strong>Objectives:</strong> To develop an improved version of Differential Evolution (DE) algorithm to overcome the complexity in extracting the features from the Electroencephalogram (EEG) based Brain-Computer Interfaces (BCI) systems; To develop a Stacked Auto Encoder (SAE) for classifying motor imagery signals into left, right, feet and tongue movements, respectively. <strong>Methods:</strong> Improved Differential Evolution Optimization Algorithm (IDEOA) is proposed for the selection of features which is extracted by the hybrid CSP-CNN feature extraction model. Extracted features will undergo the classification process by using SAE. <strong>Findings:</strong> The proposed IDEOA has an accuracy of 97.34% compared to the existing Sinc-based convolutional neural networks that obtained 75.39% and TSGL-EEG-Net of 81.34%. <strong>Novelty:</strong> The proposed IDEOA improves the mutation strategy results in improved convergence effect. <strong>Keywords:</strong> BrainComputer Interfaces; Convolutional Neural Networks; Electroencephalogram; Improved Differential Evolution Optimization Algorithm; Stacked Auto Encoder
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J, Jenifer, and Jemima Priyadarsini R. "Improved Mayfly Optimization and LightGBM Classifier for Smart City Traffic Prediction." Indian Journal of Science and Technology 15, no. 40 (2022): 2085–92. https://doi.org/10.17485/IJST/v15i40.1155.
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Abstract <strong>Objectives:</strong> This research work focuses on predicting traffic for the Smart City. <strong>Methods:</strong> Current research methods for traffic prediction are based on machine learning (ML) model. This article presents two contributions related to it. First, it provides feature engineering that includes feature extraction and a nature inspired optimization algorithm for selecting the best features. The mayfly optimization algorithm is improved by using the mode-based ranking method to select the best feature. Second, it uses the light-weight boosting method to train the datasets for better accuracy.<strong>Findings:</strong> The proposed Improved MayFly Optimization with LightGBM (IMFO-LGBM) is experimented with popular smart city datasets which is available in Kaggle website. IMFOLGBM shows an improvement in the prediction accuracy when compared with the baseline methods. It shows 2% of increase in the overall accuracy. <strong>Novelty:</strong>Nature inspired Mayfly optimization is improved and used to find the best feature for prediction. The selected features are then trained with the light weight boosting algorithm (i.e., Light Gradient Boosting Model). The hybrid of improved mayfly optimization and light GBM outperformed well. <strong>Keywords:</strong> IoT; Smartcity; Mayfly optimization; Machine learning and LightGBM
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Wangsupphaphol, Aree, Sotdhipong Phichaisawat, Nik Rumzi Nik Idris, Awang Jusoh, Nik Din Muhamad, and Raweewan Lengkayan. "A Systematic Review of Energy Management Systems for Battery/Supercapacitor Electric Vehicle Applications." Sustainability 15, no. 14 (2023): 11200. http://dx.doi.org/10.3390/su151411200.
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The purpose of this research is to present a thorough evaluation of energy management systems that consist of hybrid energy storage systems and their control algorithms, which may be used in electric vehicles. This paper outlines the characteristics of electric vehicles, research methods, an analysis of the hybrid energy storage system architecture, the converter topology, and energy management techniques. The strength and co-occurrence of keywords over the past ten years are shown in this study using a systematic research framework for performing a literature review and using keyword analysis techniques. This study reveals a pattern of recently and frequently used terms in works of literature. Consequently, their suitability, benefits, and drawbacks are assessed. In this study, the hybrid energy storage system and converter circuit architecture are evaluated and rated. A non-isolated DC-DC converter connected to an SC is a suitable configuration for the hybrid converter because it is simple to build, is reliable, and has minimal loss/weight/cost, which all improve vehicle performance. In terms of the application of control strategies, it is shown that deterministic and fuzzy-rule-based control techniques are successfully assessed using real-scale vehicle experiments and can be selected for manufacturing. On the other hand, real-time optimization-based energy management techniques have been effectively shown in lab-scale tests and may be used in a future real-scale vehicle.
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Prashant, Mathur, and Singh Sudhanshu. "Advanced Anaerobic Digestion With Optimization Techniques Using Genetic Algorithm and Fuzzy Logic." Indian Journal of Science and Technology 16, no. 22 (2023): 1624–34. https://doi.org/10.17485/IJST/v16i22.2195.
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Abstract <strong>Objectives:</strong> The primary aim of this study is to enhance the anaerobic digestion process’s efficacy by utilizing advanced optimization techniques, specifically genetic algorithms and fuzzy logic. The overarching objective is to employ these methods to optimize the model’s performance, resulting in improved anaerobic digestion outcomes. <strong>Methods:</strong> The Anaerobic Digestion process is a widely adopted technique for treating organic waste, which involves decomposing organic material by microorganisms without oxygen. However, the effectiveness of this process can be significantly influenced by various factors, such as pH, temperature, and nutrient levels. Given this process’s uncertain and imprecise nature, we propose the integration of fuzzy logic to simulate the associated uncertainties. Furthermore, we also employ genetic algorithm techniques to optimize the model’s parameters and improve its overall performance. The proposed methodology could enhance the efficiency and reliability of the Anaerobic Digestion process while minimizing its environmental impact. <strong>Findings:</strong> The study introduces an advanced anaerobic digestion model for efficiently treating organic waste. The biological methane potential was significantly improved by employing optimization techniques such as genetic algorithms and fuzzy logic. The findings demonstrate a 23.5% increase in methane production, indicating the potential for this approach to enhance the performance and efficiency of anaerobic digestion processes. Overall, the results suggest that the proposed model can contribute to developing sustainable waste management practices. <strong>Novelty:</strong> This study presents a pioneering approach by integrating genetic algorithms and fuzzy logic to optimize the anaerobic digestion process in advanced anaerobic digestion systems. To the best of our knowledge, this is the first research work that employs a hybrid control technique to consider multiple optimization methods. The proposed methodology could improve the efficiency of anaerobic digestion processes and reduce operational costs. This research advances sustainable waste management practices by applying advanced optimization techniques. <strong>Keywords:</strong> Anaerobic Digestion; Fuzzy Logic; Optimization; Renewable Energy; Genetic Algorithm
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Zhao, Ying, Pohsun Wang, and Yafeng Lai. "Route Generation and Built Environment Behavioral Mechanisms of Generation Z Tourists: A Case Study of Macau." Buildings 15, no. 11 (2025): 1947. https://doi.org/10.3390/buildings15111947.
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Personalized travel experiences have become a growing priority for tourists, while the built environment increasingly shapes tourists’ behavior. However, limited research has integrated behavioral drivers with algorithmic travel route optimization, particularly in the context of Generation Z tourists. To address this gap, this study proposes a hybrid framework that combines behavioral modeling with enhanced algorithmic techniques to generate customized travel itineraries for Generation Z. A behavioral influencing factors model is first constructed based on the Theory of Planned Behavior (TPB) and Social Influence Theory (SIT), identifying media influence (MI), subjective norms (SNs), and perceived built environment (PBE) as potential determinants of travel behavioral intention (BI). A Structural Equation Model (SEM) is then applied to empirically validate the hypothesized relationships. Results reveal that all three factors have a significant and positive impact on BI (p < 0.05). Building on this behavioral mechanism, an interest-based Ant Colony Optimization (ACO) algorithm is implemented by incorporating point-of-interest (POI) preferences and distance matrices to improve personalized route generation. Comparative analysis using social media keyword data demonstrates that the proposed method outperforms conventional travel route planning approaches in terms of route relevance and overall path satisfaction. This study offers a novel integration of psychological theory and computational optimization, providing both theoretical insights and practical implications for urban tourism planning and the development of smart tourism services.
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Ding, Xiaoe, Minrui Zheng, and Xinqi Zheng. "The Application of Genetic Algorithm in Land Use Optimization Research: A Review." Land 10, no. 5 (2021): 526. http://dx.doi.org/10.3390/land10050526.
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Land use optimization (LUO) first considers which types of land use should exist in a certain area, and secondly, how to allocate these land use types to specific land grid units. As an intelligent global optimization search algorithm, the Genetic Algorithm (GA) has been widely used in this field. However, there are no comprehensive reviews concerning the development process for the application of the Genetic Algorithm in land use optimization (GA-LUO). This article used a bibliometric analysis method to explore current state and development trends for GA-LUO from 1154 relevant documents published over the past 25 years from Web of Science. We also displayed a visualization network from the aspects of core authors, research institutions, and highly cited literature. The results show the following: (1) The countries that published the most articles are the United States and China, and the Chinese Academy of Sciences is the research institution that publishes the most articles. (2) The top 10 cited articles focused on describing how to build GA models for multi-objective LUO. (3) According to the number of keywords that appear for the first time in each time period, we divided the process of GA-LUO into four stages: the presentation and improvement of methods stage (1995–2004), the optimization stage (2005–2008), the hybrid application of multiple models stage (2009–2016), and the introduction of the latest method stage (after 2017). Furthermore, future research trends are mainly manifested in integrating together algorithms with GA and deepening existing research results. This review could help researchers know this research domain well and provide effective solutions for land use problems to ensure the sustainable use of land resources.
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Bangoria, Bhoomi Mansukhlal, Sweta S. Panchal, Sandipkumar R. Panchal, and Tusharkumar Mansukhbhai Bangoria. "A Novel Machine Learning Approach for Multidimensional Dynamic Destination Recommender S earch System Employing Clustering using Optimization Techniques." Indian Journal Of Science And Technology 17, no. 44 (2024): 4679–93. https://doi.org/10.17485/ijst/v17i44.3525.
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Objectives: To find the most suitable destinations according to a selection of various dimensions by the user. Optimization techniques are applied to clustering with the help of various objective functions, to find optimal clusters for better recommendations. Methods: This approach uses a hybrid filtering system for recommendation with a weighted K-means clustering algorithm and uses optimization techniques to improve cluster formation recommendation accuracy. This study used a data set from Kaggle, which considers different city names, types, and significance. According to city names, using a geolocator object in Python gets latitude and longitude for precise clustering. Findings: The elbow method utilizes the K-means and weighted K-means clustering algorithm to determine the number of clusters. We use optimization techniques such as the Artificial Bee Colony (ABC) algorithm and Particle Swarm Optimisation (PSO) to improve the cluster formation recommendation accuracy, as demonstrated by the following results: The accuracy of K-means with ABC and PSO is respectively 85% and 88%, while weighted K-means with ABC and PSO is respectively 90% and 93%. Novelty: This study highlights how advanced optimization techniques like ABC and PSO enhance K-means and weighted K-means clustering accuracy, precision, and recall. The combination of weighted K-means and PSO further enhances performance, making it the perfect choice for tasks that demand high-quality clustering and recommendation systems. Compared to the K-means clustering algorithm with PSO optimization, the ratio of successfully recommended relevant destinations is 7% higher. Keywords: Recommender System, Clustering, Destination Recommender System (DRS), Machine Learning, Weighted Kmeans clustering Algorithm, ABC, PSO
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A, Emima, and I. George Amalarethinam D. "A Hybrid Model of Enhanced Teacher Learner Based Optimization (ETLBO) with Particle Swarm Optimization (PSO) Algorithm for Predicting Academic Student Performance." Indian Journal of Science and Technology 18, no. 10 (2025): 772–83. https://doi.org/10.17485/IJST/v18i10.240.
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Abstract <strong>Objectives: </strong> A hybrid ETLBO-PSO model is developed to improve student performance predictions. It assesses intellectual, social, and economic background of students to increase accuracy of students performance predictions. The model optimizes selecting features, which reduces redundancy and increases efficiency. The efficacy is compared with existing Educational Data Mining techniques. <strong>Methods :</strong> This study integrates Enhanced Teachers Learners Based Optimization (ETLBO) and Particle Swarm Optimization (PSO) algorithm for optimal feature selection. The suggested technique is utilized as an algorithm for selecting features to identify the most significant elements for predicting student academic performance. The efficacy of the proposed feature selection technique is evaluated using three machine learning classifiers: Extreme Gradient Boosting (XGB), Light Gradient Boosting (LightGB), and Category Gradient Boosting (CatGB) in Student achievement Dataset in secondary education for Mathematics. <strong>Findings:</strong> The experimental results of ETLBO-PSO provides sustained excellent model performance while reducing accuracy decline. The Meta-Class model of ETLBO-PSO has an F1-score of 82.43%, which makes it an increasingly robust and reliable strategy. Furthermore, an innovative visual and intuitive method is employed to identify the aspects that most significantly impact the score, facilitating the interpretation and comprehension of the complete model.<strong> Novelty:</strong> ETLBO_PSO is integrated with SHAP (SHapley Additive exPlanations), and Meta-class Model are used to optimize student performance predictions with higher accuracy. Unlike traditional approaches, it continuously refines selecting features throughout training, solving high-dimensional data issues. SHAP's approach assures precise feature attribution, hence improving accessibility and making decisions. <strong>Keywords:</strong> Feature Selection, Enhance Teacher Learner based Optimization, Particle Swarm Optimization, Academic Student Performance, Classification Algorithm, Optimization Techniques, XGBoost, LGBoost, CATBoost
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Anant Manish Singh. "Integrating Deep Learning Techniques in Information Retrieval: A Hybrid Approach to Relevance Optimization." Journal of Information Systems Engineering and Management 10, no. 53s (2025): 311–16. https://doi.org/10.52783/jisem.v10i53s.10876.
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Traditional keyword-based information retrieval (IR) systems while effective for exact term matching, often fail to capture semantic meaning, leading to suboptimal relevance especially for complex queries. Studies show that conventional models typically achieve 85–90% accuracy whereas deep learning methods like BERT and DeepCT have reached up to 98.6% accuracy in text retrieval tasks. However, many current implementations do not fully exploit the complementary strengths of neural and lexical techniques. This research addresses that gap by proposing a hybrid IR framework that integrates BM25 with neural embeddings using transformer models and contextual weighting. Using MS-MARCO and TREC-CAR datasets, the methodology includes training neural ranking models, implementing Learning to Rank (LTR) and pseudo-relevance feedback (PRF) and evaluating performance via metrics such as mean average precision (MAP), nDCG and MRR. The hybrid system outperformed traditional models with a 25–30% improvement in recall and a 12% gain in MAP; user satisfaction scores were also 15–20% higher particularly for ambiguous or domain-specific queries. These findings suggest that combining lexical and semantic signals significantly enhances retrieval relevance and user experience. The model's applicability spans enterprise, academic and web search contexts with systems like Vertex AI and Elasticsearch already demonstrating similar performance gains. Future work will explore reducing model complexity for real-time scalability, enhancing interpretability and developing adaptive algorithms that incorporate continuous user feedback for iterative optimization.
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Barna, Thomas Lass,, Samson Isaac, Amina Isa, Zara Sakanau, and Hamidatu Abdulkadir. "Particle Swamp Optimized -Physics-Informed Deep Learning for Heterogeneous Oil and Gas Underground Reservoir Pressure Management." Advances in Multidisciplinary and scientific Research Journal Publication 36 (April 23, 2023): 13–24. http://dx.doi.org/10.22624/aims-/accrabespoke2023p2.
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In recent years, Particle Swarm Optimization (PSO) has been integrated with machine learning algorithms, such as deep learning, to create powerful hybrid methods that can tackle complex optimization problems more effectively. In the domain of oil and gas reservoir management, underground pressure management is crucial to maximize the yield and efficiency of the reservoir. However, the heterogeneity of the reservoir, along with uncertainties in its properties, makes pressure management a complex and challenging task. To address this issue, researchers have proposed Physics-Informed Deep Learning (PIDL) techniques that incorporate domain-specific knowledge, such as the governing physical equations, into the deep learning framework. Particle Swarm Optimized-Physics-Informed Deep Learning (PSO-PIDL) is a novel hybrid approach that combines PSO with PIDL to optimize the pressure management of heterogeneous oil and gas underground reservoirs. In this approach, the PSO algorithm is used to find the optimal solution for the PIDL-based model that incorporates the governing physical equations of the reservoir. PSO-PIDL can effectively handle the uncertainties and heterogeneity of the reservoir, while also incorporating the physical constraints of the problem. Overall, PSO-PIDL is a promising approach for optimizing the pressure management of oil and gas reservoirs. It can help reduce the operational costs and improve the efficiency of the reservoir, while also ensuring the sustainable use of natural resources. Keywords: Physics-Informed Deep Learning, Particle Swarm Optimization, Bidirectional Long-Short-Term-Memory, Heterogeneous Reservoir, DuPont Finite Element Heat and Mass Transfer Code
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Lass, Barna Thomas, Samson Isaac, Amina Isa, Zara Sakanau, and Hamidatu Abdulkadir. "Particle Swamp Optimized -Physics-Informed Deep Learning for Heterogeneous Oil and Gas Underground Reservoir Pressure Management." Advances in Multidisciplinary and scientific Research Journal Publication 36 (April 23, 2023): 13–24. http://dx.doi.org/10.22624/aims/accrabespoke2023p2.
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In recent years, Particle Swarm Optimization (PSO) has been integrated with machine learning algorithms, such as deep learning, to create powerful hybrid methods that can tackle complex optimization problems more effectively. In the domain of oil and gas reservoir management, underground pressure management is crucial to maximize the yield and efficiency of the reservoir. However, the heterogeneity of the reservoir, along with uncertainties in its properties, makes pressure management a complex and challenging task. To address this issue, researchers have proposed Physics-Informed Deep Learning (PIDL) techniques that incorporate domain-specific knowledge, such as the governing physical equations, into the deep learning framework. Particle Swarm Optimized-Physics-Informed Deep Learning (PSO-PIDL) is a novel hybrid approach that combines PSO with PIDL to optimize the pressure management of heterogeneous oil and gas underground reservoirs. In this approach, the PSO algorithm is used to find the optimal solution for the PIDL-based model that incorporates the governing physical equations of the reservoir. PSO-PIDL can effectively handle the uncertainties and heterogeneity of the reservoir, while also incorporating the physical constraints of the problem. Overall, PSO-PIDL is a promising approach for optimizing the pressure management of oil and gas reservoirs. It can help reduce the operational costs and improve the efficiency of the reservoir, while also ensuring the sustainable use of natural resources. Keywords: Physics-Informed Deep Learning, Particle Swarm Optimization, Bidirectional Long-Short-Term-Memory, Heterogeneous Reservoir, DuPont Finite Element Heat and Mass Transfer Code
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Radhakrishna, Laishetty, V. S. Hariharan, Banothu Srinivas, et al. "Performance Evaluation of ML-Based Algorithm and Taguchi Algorithm of the Hardness Value of the Friction Stir Welded AA6262 Joints at a Nugget Joint." E3S Web of Conferences 430 (2023): 01249. http://dx.doi.org/10.1051/e3sconf/202343001249.
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Nowadays, industry 4.0 plays a tremendous role in the manufacturing industries for increasing the amount of data and accuracy in modern manufacturing systems. Thanks to artificial intelligence, particularly machine learning, big data analytics have dramatically amended, and manufacturers easily exploit organized and unorganized data. This study utilized hybrid optimization algorithms to find friction stir welding and optimal hardness value at the nugget zone. A similar AA 6262 material was used and welded in a butt joint configuration. Tool rotational speed (RPM), tool traverse speed (mm/min), and the plane depth (mm) are used as controllable parameters and optimized using Taguchi L9, Random Forest, and XG Boost machine learning tools. Analysis of variance was also conducted at a 95% confidence interval for identifying the significant parameters. The result indicated that the coefficient of determination from Taguchi L9 orthogonal array is 0.91 obtained while Random Forest and XG Boost algorithm imparted 0.62 and 0.65 respectively. Keywords: Friction Stir Welding; Taguchi; Machine Learning; Hardness; Nugget Zone and Random Forest.
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Bhoomi, Mansukhlal Bangoria, S. Panchal Sweta, R. Panchal Sandipkumar, and Mansukhbhai Bangoria Tusharkumar. "A Novel Machine Learning Approach for Multidimensional Dynamic Destination Recommender S earch System Employing Clustering using Optimization Techniques." Indian Journal of Science and Technology 17, no. 44 (2024): 4679–93. https://doi.org/10.17485/IJST/v17i44.3525.
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Abstract <strong>Objectives:</strong> To find the most suitable destinations according to a selection of various dimensions by the user. Optimization techniques are applied to clustering with the help of various objective functions, to find optimal clusters for better recommendations. <strong>Methods:</strong> This approach uses a hybrid filtering system for recommendation with a weighted K-means clustering algorithm and uses optimization techniques to improve cluster formation recommendation accuracy. This study used a data set from Kaggle, which considers different city names, types, and significance. According to city names, using a geolocator object in Python gets latitude and longitude for precise clustering. <strong>Findings:</strong> The elbow method utilizes the K-means and weighted K-means clustering algorithm to determine the number of clusters. We use optimization techniques such as the Artificial Bee Colony (ABC) algorithm and Particle Swarm Optimisation (PSO) to improve the cluster formation recommendation accuracy, as demonstrated by the following results: The accuracy of K-means with ABC and PSO is respectively 85% and 88%, while weighted K-means with ABC and PSO is respectively 90% and 93%. <strong>Novelty:</strong> This study highlights how advanced optimization techniques like ABC and PSO enhance K-means and weighted K-means clustering accuracy, precision, and recall. The combination of weighted K-means and PSO further enhances performance, making it the perfect choice for tasks that demand high-quality clustering and recommendation systems. Compared to the K-means clustering algorithm with PSO optimization, the ratio of successfully recommended relevant destinations is 7% higher. <strong>Keywords:</strong> Recommender System, Clustering, Destination Recommender System (DRS), Machine Learning, Weighted K­means clustering Algorithm, ABC, PSO
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Li, Keqing, Wenxing Lu, Changyong Liang, and Binyou Wang. "Intelligence in Tourism Management: A Hybrid FOA-BP Method on Daily Tourism Demand Forecasting with Web Search Data." Mathematics 7, no. 6 (2019): 531. http://dx.doi.org/10.3390/math7060531.
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The Chinese tourism industry has been developing rapidly for the past several years, and the number of people traveling has been increasing year by year. However, many problems still beset current tourism management. Lack of effective management has caused numerous problems, such as tourists stranded during tourist season and the declining service quality of scenic spots, which have become the focus of tourists’ attention. Network search data can intuitively reflect the attention of most users through the combination of the network search index and the back propagation (BP) neural network model. This study predicts the daily tourism demand in the Huangshan scenic spot in China. The filtered keyword in the Baidu index is added to the hybrid neural network, and a BP neural network model optimized by a fruit fly optimization algorithm (FOA) based on the web search data is established in this study. Different forecasting methods are compared in this paper; the results prove that compared with other prediction models, higher accuracy can be obtained when it comes to the peak season using the FOA-BP method that includes web search data, which is a sustainable means of practically solving the tourism management problem by a more accurate prediction of tourism demand of scenic spots.
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Kunjumon, Nithu, Yatin Kumar Shukla, and Dr Amit Vajpayee. "A Hybrid Model of Particle Swarm Optimization for Wind Energy and Wind Power Through RNN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40377.
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The increasing demand for sustainable energy sources has led to a great deal of study on optimizing wind energy systems. This study proposes a hybrid model that combines recurrent neural networks (RNN) and particle swarm optimization (PSO) to increase the efficiency of wind power forecasting and energy generation. Using previous data and meteorological conditions, the RNN is used to anticipate wind power output. The RNN's parameters are optimized via the PSO algorithm. The suggested model addresses the variable and irregular character of wind patterns in an effort to maximize energy output and enhance wind power forecasts through the integration of many methodologies. The hybrid model's performance is evaluated using actual wind farm data; the findings demonstrate a significant improvement in forecast accuracy and computing efficiency over traditional methods. This work demonstrates how advanced computational techniques and machine learning models can be used to enhance renewable energy systems and contribute to the development of safer and more effective wind energy solutions. Keywords - Particle Swarm Optimization, Recurrent Neural Networks, Wind Energy, Wind Power Forecasting, Renewable Energy
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Abhijit, S. Pande, and G. Burade Prakash. "Intelligent Power Dispatch Optimization: A Genetic Algorithm Approach with Unified Power Flow Controller." Indian Journal of Science and Technology 18, no. 11 (2025): 891–903. https://doi.org/10.17485/IJST/v18i11.3709.
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Abstract <strong>Objective:</strong> The goal of this project is to provide an advanced optimization approach for Dynamic Power Dispatch (DPD) by combining a Unified Power Flow Controller (UPFC) with a Genetic Algorithm (GA). The main objective is to improve power flow management and grid performance in complicated power networks. <strong>Methodology:</strong> The suggested method takes advantage of the capabilities of the UPFC, a versatile component of the Flexible AC Transmission System (FACTS), and combines them with the optimization power of GA. The UPFC provides close oversight over power flow, voltage ranges, and phase angles, while GA, known for its ability to solve multi-objective and nonlinear problems, improves the UPFC’s installation and operational settings. The effectiveness of this hybrid technique is proven by simulations using the IEEE 14-bus test system. <strong>Findings:</strong> Simulation results demonstrate that the integration of GA with UPFC yields significant improvements in power flow stability, reduces operational costs and transmission losses, and enhances voltage profiles compared to conventional DPD techniques. Furthermore, the approach increases grid flexibility and stability, addressing the evolving demands of modern power networks. <strong>Novelty:</strong> This study introduces a unique combination of UPFC and GA to address the challenges in DPD. The approach merges the real-time control capabilities of UPFC with the evolutionary optimization strengths of GA, presenting a scalable and adaptive solution for power dispatch optimization. <strong>Applications:</strong> The proposed technique is especially useful for improving the efficiency and reliability of current power grids, including those that use renewable energy sources. Its use in smart grid scenarios can help accelerate the transition to more sustainable and resilient energy systems. <strong>Keywords:</strong> Dynamic power dispatch; Unified Power Flow Controller; Genetic Algorithm; power system optimization; FACTS devices; and an IEEE 14bus testing system
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Anthony, E. Umoru, A. Oluwadare Samuel, and M. Dahunsi Folasade. "A HYBRID GENETIC ALGORITHM-BASED MODEL FOR JOBS SCHEDULING IN CLOUD COMPUTING." International Journal of Recent Research in Mathematics Computer Science and Information Technology 10, no. 2 (2023): 11–19. https://doi.org/10.5281/zenodo.10300111.
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<strong>Abstract:</strong> Cloud computing is a platform that provide users with computing resources via the Internet on a pay-as-you-use basis. In cloud computing paradigm, resources are shared; hence, there is a need to utilize the resources appropriately to achieve the quality of service stipulated by the user and yield maximum profit for the service provider. This requirement of resources sharing informs the need for efficient jobs scheduling. Jobs Scheduling means the set of policies used to control the order of work to be done by a computer system. The jobs scheduling problem in cloud computing focuses mainly on efficiently using computing resources to benefit both the service provider and the user. In this study a new algorithm is proposed that minimize the completion time and cost of cloudlets on resources and improve the overall experience of both service providers and customers. The hybrid genetic algorithm uses the output of max-min and first come first serve methods. Two physical machines, twenty five virtual machines and two thousand cloudlets were used to test run the algorithm. The results showed that the proposed algorithm achieved the optimal solutions for the two metrics of completion time and cost.<strong>Keywords:</strong> Cloud computing, Completion time, Hybrid Genetic algorithm, Job scheduling, Max-min, FCFS, Optimization. <strong>Title:</strong> A HYBRID GENETIC ALGORITHM-BASED MODEL FOR JOBS SCHEDULING IN CLOUD COMPUTING<strong>Author:</strong> Anthony E. Umoru, Samuel A. Oluwadare, Folasade M. Dahunsi<strong>International Journal of Recent Research in Mathematics Computer Science and Information Technology</strong><strong>ISSN 2350-1022</strong><strong>Vol. 10, Issue 2, October 2023 - March 2024</strong><strong>Page No: 11-19</strong><strong>Paper Publications</strong><strong>Website: www.paperpublications.org</strong><strong>Published Date: 08-December-2023</strong><strong>DOI: </strong><strong>https://doi.org/10.5281/zenodo.10300111</strong><strong>Paper Download Link (Source)</strong><strong>https://www.paperpublications.org/upload/book/A%20HYBRID%20GENETIC%20ALGORITHM-BASED-08122023-2.pdf</strong>
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Kumar, Ram, Kuldeep Narayan Tripathi, and Subhash Chander Sharma. "Optimal Query Expansion Based on Hybrid Group Mean Enhanced Chimp Optimization Using Iterative Deep Learning." Electronics 11, no. 10 (2022): 1556. http://dx.doi.org/10.3390/electronics11101556.
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The internet is surrounded by uncertain information which necessitates the usage of natural language processing and soft computing techniques to extract the relevant documents. The relevant results are retrieved using the query expansion technique which is mainly formulated using the machine learning or deep learning concepts in the existing literature. This paper presents a hybrid group mean-based optimizer-enhanced chimp optimization (GMBO-ECO) algorithm for pseudo-relevance-based query expansion, whereby the actual queries are expanded with their related keywords. The hybrid GMBO-ECO algorithm mainly expands the query based on the terms that have a strong interrelationship with the actual query. To generate the word embeddings, a Word2Vec paradigm is used which learns the word association from large text corpora. The useful context in the text is identified using the improved iterative deep learning framework which determines the user’s intent for the current web search. This step reduces the mismatch of the words and improves the performance of query retrieval. The weak terms are eliminated and the candidate query terms for optimal query expansion are improved via an Okapi measure and cosine similarity techniques. The proposed methodology has been compared to the state-of-the-art methods with and without a query expansion approach. Moreover, the proposed optimal query expansion technique has shown a substantial improvement in terms of a normalized discounted cumulative gain of 0.87, a mean average precision of 0.35, and a mean reciprocal rank of 0.95. The experimental results show the efficiency of the proposed methodology in retrieving the appropriate response for information retrieval. The most common applications for the proposed method are search engines.