Relevant bibliographies by topics / Keyword: Hybrid Optimization Algorithm

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Academic literature on the topic 'Keyword: Hybrid Optimization Algorithm'

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Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Keyword: Hybrid Optimization Algorithm"

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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>&nbsp;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.&nbsp;<strong>Method:</strong>&nbsp;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>&nbsp;Findings:</strong>&nbsp;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.&nbsp;<strong>Novelty:</strong>&nbsp;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:&nbsp;</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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Dissertations / Theses on the topic "Keyword: Hybrid Optimization Algorithm"

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Uskay, Selim Onur. "Route Optimization For Solid Waste Transportation Using Parallel Hybrid Genetic Algorithms." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612942/index.pdf.

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The transportation phase of solid waste management is highly critical as it may constitute approximately 60 to 75 percent of the total cost. Therefore, even a small amount of improvement in the collection operation can result in a significant saving in the overall cost. Despite the fact that there exist a considerable amount of studies on Vehicle Routing Problem (VRP), a vast majority of the existing studies are not integrated with GIS and hence they do not consider the path constraints of real road networks for waste collection such as one-way roads and U-Turns. This study involves the development of computer software that optimizes the waste collection routes for solid waste transportation considering the path constraints and road gradients. In this study, two different routing models are proposed. The aim of the first model is to minimize the total distance travelled whereas that of the second model is to minimize the total fuel consumption that depends on the loading conditions of the truck and the road gradient. A comparison is made between these two approaches. It is expected that the two approaches generate routes having different characteristics. The obtained results are satisfactory. The distance optimization model generates routes that are shorter in length whereas the fuel consumption optimization model generates routes that are slightly higher in length but provides waste collection on steeply inclined roads with lower truck load. The resultant routes are demonstrated on a 3D terrain view.
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Wong, Yin-cheung Eugene, and 黃彥璋. "A hybrid evolutionary algorithm for optimization of maritime logisticsoperations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44526763.

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Inclan, Eric. "The Development of a Hybrid Optimization Algorithm for the Evaluation and Optimization of the Asynchronous Pulse Unit." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1582.

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The effectiveness of an optimization algorithm can be reduced to its ability to navigate an objective function’s topology. Hybrid optimization algorithms combine various optimization algorithms using a single meta-heuristic so that the hybrid algorithm is more robust, computationally efficient, and/or accurate than the individual algorithms it is made of. This thesis proposes a novel meta-heuristic that uses search vectors to select the constituent algorithm that is appropriate for a given objective function. The hybrid is shown to perform competitively against several existing hybrid and non-hybrid optimization algorithms over a set of three hundred test cases. This thesis also proposes a general framework for evaluating the effectiveness of hybrid optimization algorithms. Finally, this thesis presents an improved Method of Characteristics Code with novel boundary conditions, which better characterizes pipelines than previous codes. This code is coupled with the hybrid optimization algorithm in order to optimize the operation of real-world piston pumps.
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Jaber, Ahmed. "Hybrid Algorithm for Multi-objective Mixed-integer Non-convex Mechanical Design Optimization Problems." Thesis, Troyes, 2021. http://www.theses.fr/2021TROY0034.

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Les problèmes d'optimisation sous contraintes non linéaires non convexes multi-objectifs en variables mixtes (discrètes et continues) apparaissent dans de nombreux domaines de l’ingénierie et notamment dans les applications de conception en mécaniques. Cette thèse vise à développer une nouvelle méthode pour résoudre ces problèmes d’optimisation. Notre proposition est une hybridation de l'algorithme multicritère « Branch-and-Bound » (MCBB) avec l’algorithme évolutionnaire de type NSGAII. L'approche proposée est en outre renforcée par de nouvelles stratégies de branchement conçues pour l’algorithme MCBB. Les contraintes du problème d’optimisation sont gérées à l'aide d'une nouvelle technique dédiée aux algorithmes évolutionnaires. Les performances de cette nouvelle approche sont évaluées et comparées à l’existant par une étude statistique sur un ensemble de problèmes tests. Les résultats montrent que les performances de notre algorithme sont compétitives face à l’algorithme NSGAII seul. Nous proposons deux applications de notre algorithme : les applications "Recherche de solutions faisables" et "Recherche de solutions optimales". Celles-ci sont appliquées sur un problème industriel réel d’un réducteur à engrenages à 3 étages formulé comme un problème bi-objectif. Dans ce problème des contraintes sont incluses pour satisfaire aux exigences de normes ISO sur le calcul de la capacité de charge des engrenages<br>Multi-objective mixed-integer non-convex non-linear constrained optimization problems that appears in several fields especially in mechanical applications. This thesis aims to develop a new method to solve such problems. Our proposal is a hybridization of the Multi-Criteria Branch-and-Bound (MCBB) algorithm with the Non-dominated Sorting Genetic Algorithm 2 (NSGAII). The proposed approach is furthermore enhanced by new branching strategies designed for MCBB. The constraints are handled using a new proposed constraint handling technique for evolutionary algorithms. Numerical experiments based on statistical assessment are done in this thesis to examine the performance of the new proposed approach. Results show the competitive performance of our algorithm among NSGAII. We propose two applications of our proposed approach: "Search Feasibility" and "Seek Optimality" applications. Both are applied on a real-world state of art 3 stages reducer problem which is formulated in this thesis to a bi-objective problem to meet the requirement of ISO standards on calculation of load capacity of gears
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Vytla, Veera Venkata Sunil Kumar. "Multidisciplinary Optimization Framework for High Speed Train using Robust Hybrid GA-PSO Algorithm." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1310558511.

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Meyer, Danielle L. "Energy Optimization of a Hybrid Unmanned Aerial Vehicle (UAV)." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523493111005807.

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Franz, Wayne. "Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition." Springer, 2013. http://hdl.handle.net/1993/23842.

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Recent work in metaheuristic algorithms has shown that solution quality may be improved by composing algorithms with orthogonal characteristics. In this thesis, I study multi-population particle swarm optimization (MPSO) and genetic algorithm (GA) hybrid strategies. I begin by investigating the behaviour of MPSO with crossover, mutation, swapping, and all three, and show that the latter is able to solve the most difficult benchmark functions. Because GAs converge slowly and MPSO provides a large degree of parallelism, I also develop several parallel hybrid algorithms. A composite approach executes PSO and GAs simultaneously in different swarms, and shows advantages when arranged in a star topology, particularly with a central GA. A static scheme executes in series, with a GA performing the exploration followed by MPSO for exploitation. Finally, the last approach dynamically alternates between algorithms. Hybrid algorithms are well-suited for parallelization, but exhibit tradeoffs between performance and solution quality.
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Chhabra, Rupanshi. "Control Power Optimization using Artificial Intelligence for Hybrid Wing Body Aircraft." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/56580.

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Traditional methods of control allocation optimization have shown difficulties in exploiting the full potential of controlling a large array of control surfaces. This research investigates the potential of employing artificial intelligence methods like neurocomputing to the control allocation optimization problem of Hybrid Wing Body (HWB) aircraft concepts for minimizing control power, hinge moments, and actuator forces, while keeping the system weights within acceptable limits. The main objective is to develop a proof-of-concept process suitable to demonstrate the potential of using neurocomputing for optimizing actuation power for aircraft featuring multiple independently actuated control surfaces and wing flexibility. An aeroelastic Open Rotor Engine Integration and Optimization (OREIO) model was used to generate a database of hinge moment and actuation power characteristics for an array of control surface deflections. Artificial neural network incorporating a genetic algorithm then performs control allocation optimization for an example aircraft. The results showed that for the half-span model, the optimization results (for the sum of the required hinge moment) are improved by more than 11%, whereas for the full-span model, the same approach improved the result by nearly 14% over the best MSC Nastran solution by using the neural network optimization process. The results were improved by 23% and 27% over the case where only the elevator is used for both half-span and full-span models, respectively. The methods developed and applied here can be used for a wide variety of aircraft configurations.<br>Master of Science
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Amouzgar, Kaveh. "Multi-objective optimization using Genetic Algorithms." Thesis, Högskolan i Jönköping, Tekniska Högskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-19851.

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In this thesis, the basic principles and concepts of single and multi-objective Genetic Algorithms (GA) are reviewed. Two algorithms, one for single objective and the other for multi-objective problems, which are believed to be more efficient are described in details. The algorithms are coded with MATLAB and applied on several test functions. The results are compared with the existing solutions in literatures and shows promising results. Obtained pareto-fronts are exactly similar to the true pareto-fronts with a good spread of solution throughout the optimal region. Constraint handling techniques are studied and applied in the two algorithms. Constrained benchmarks are optimized and the outcomes show the ability of algorithm in maintaining solutions in the entire pareto-optimal region. In the end, a hybrid method based on the combination of the two algorithms is introduced and the performance is discussed. It is concluded that no significant strength is observed within the approach and more research is required on this topic. For further investigation on the performance of the proposed techniques, implementation on real-world engineering applications are recommended.
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Cai, Xinye. "A multi-objective GP-PSO hybrid algorithm for gene regulatory network modeling." Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1492.

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Books on the topic "Keyword: Hybrid Optimization Algorithm"

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Christian, Blum, ed. Hybrid metaheuristics: An emerging approach to optimization. Springer, 2008.

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Li, Ying. A hybrid global estimation algorithm for feedforward neural networks. 1992.

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Blum, Christian, and Günther R. Raidl. Hybrid Metaheuristics: Powerful Tools for Optimization. Springer, 2016.

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Blum, Christian, and Günther R. Raidl. Hybrid Metaheuristics: Powerful Tools for Optimization. Springer, 2018.

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Hybrid Metaheuristics: 9th International Workshop, HM 2014, Hamburg, Germany, June 11-13, 2014, Proceedings. Springer, 2014.

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Bäck, Thomas. Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099713.001.0001.

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This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.
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Book chapters on the topic "Keyword: Hybrid Optimization Algorithm"

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Strumberger, Ivana, Nebojsa Bacanin, and Milan Tuba. "Hybridized Elephant Herding Optimization Algorithm for Constrained Optimization." In Hybrid Intelligent Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76351-4_16.

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Pan, Jinkun, Dongsheng Li, and Liming Li. "An Efficient Packing Algorithm for Spatial Keyword Queries." In Convergence and Hybrid Information Technology. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32645-5_70.

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Sae-Dan, Weerapan, Marie-Eléonore Kessaci, Nadarajen Veerapen, and Laetitia Jourdan. "Automatic Algorithm Multi-Configuration Applied to an Optimization Algorithm." In Hybrid Intelligent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96305-7_15.

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Kim, D. H., Ajith Abraham, and K. Hirota. "Hybrid Genetic: Particle Swarm Optimization Algorithm." In Hybrid Evolutionary Algorithms. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_7.

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Jiang, Chao, Xu Han, and Huichao Xie. "Interval Optimization Based on Hybrid Optimization Algorithm." In Nonlinear Interval Optimization for Uncertain Problems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8546-3_4.

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da Cruz, A. V. Abs, M. M. B. R. Vellasco, and M. A. C. Pacheco. "Quantum-Inspired Evolutionary Algorithm for Numerical Optimization." In Hybrid Evolutionary Algorithms. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_2.

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Alcântara, J. P. M., J. B. Monteiro-Filho, I. M. C. Albuquerque, J. L. Villar-Dias, M. G. P. Lacerda, and F. B. Lima-Neto. "Fish School Search Algorithm for Constrained Optimization." In Hybrid Intelligent Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27409-1_37.

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Davendra, Donald David, Ivan Zelinka, and Godfrey Onwubolu. "Hybrid Self Organising Migrating – Scatter Search Algorithm." In Handbook of Optimization. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30504-7_35.

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Naik, Manoj Kumar, Leena Samantaray, and Rutuparna Panda. "A Hybrid CS–GSA Algorithm for Optimization." In Hybrid Soft Computing Approaches. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2544-7_1.

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Margain, Lourdes, Alberto Ochoa, Lissette Martínez Almaguer, and Rigoberto Velázquez. "Model on Oil Platform Using Brain Storm Optimization Algorithm." In Hybrid Intelligent Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76351-4_32.

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Conference papers on the topic "Keyword: Hybrid Optimization Algorithm"

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Wei, Junyi, Hongbin Dong, and Xiaoping Zhang. "Information interconnection Harris hawks hybrid optimization algorithm." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822249.

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Song, Xiaoyu, Yanlin Zhu, and Ming Zhao. "Hybrid Optimization Algorithm of Differential Evolution Algorithm and Artificial Bee Colony Algorithm." In 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2024. http://dx.doi.org/10.1109/cisce62493.2024.10653349.

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Kanna, G. Prabu, and V. Vasudevan. "Enhancing the security of user data using the keyword encryption and hybrid cryptographic algorithm in cloud." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016. http://dx.doi.org/10.1109/iceeot.2016.7755398.

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Abdenouri, Naji, H. El Ferouali, M. Gharafi, A. Zoukit, and S. Doubabi. "Hybrid solar-gas-electric dryer optimization with genetic algorithms." In 21st International Drying Symposium. Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/ids2018.2018.7521.

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To promote the hybrid solar dryers for use even under unfavorable weather and to overcome the intermittance state issue, the energy consumption should be optimized and the response time should be reduced. This work concerns a drying chamber connected to a solar absorber where the air can be heated also by combustion of gas and by electric resistance. To optimize the control parameters, an evolutionary optimization algorithm simulating natural selection was used. It was combined with a predictive model based on the artificial neural networks (ANN) technique and used as a fitness function for the genetic algorithm (GA). The ANN is a learning algorithm that needs training through a large dataset, which was collected using CFD simulation and experimental data. Then a GA was executed in order to optimize two objectives: The energy consumption and the t95% response time in which the drying chamber temperature reaches its set point (60°C). After optimization, a 30% decrease of the t95% response time, and 20% decrease of the energy consumption were obtained. Keywords: hybrid solar dryer; artificial neural network; temperature regulation; energy consumption; genetic algorithm.
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Senevirathne, S. S. M. A. C., and Chathuri J. Samarasinghe. "A Hybrid Particle Swarm Optimization – Travelling Salesman Problem for Efficient Multi Depot Vehicle Routing." In SLIIT INTERNATIONAL CONFERENCE ON ADVANCEMENTS IN SCIENCES AND HUMANITIES. Faculty of Humanities & Sciences, SLIIT, 2024. https://doi.org/10.54389/jxsh5934.

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The Fast-Moving Consumer Goods (FMCG) industry faces increasing demand to optimize distribution networks to reduce costs. The company seeks to establish a proper redistribution route network, optimize truck allocation, and minimize warehouse operations, administration, and transportation costs while adhering to capacity and volume constraints. To achieve this, the study formulates the problem as a Multi -Depot Vehicle Routing Problem (MDVRP) with 3 depots. The proposed model with the addition of a particle swarm algorithm yields a substantial cost reduction of 21.41% compared to the existing system, demonstrating the potential of hybrid metaheuristic algorithms for addressing complex logistics challenges in the FMCG industry. Keywords: Multi -depot vehicle routing problem; K-Means Clustering; Gravity model; Particle Swarm Optimization; Travelling Salesman Problem
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Ye, Zhiwei, Lie Ma, and Hongwei Chen. "A hybrid rice optimization algorithm." In 2016 11th International Conference on Computer Science & Education (ICCSE). IEEE, 2016. http://dx.doi.org/10.1109/iccse.2016.7581575.

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Gao, Jianping, Zhennan Liu, Zhijun Guo, and Yuehui Wei. "Optimization of Hybrid Electric Bus Control Strategy with Hybrid Optimization Algorithm." In 2nd International Conference On Systems Engineering and Modeling. Atlantis Press, 2013. http://dx.doi.org/10.2991/icsem.2013.62.

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Shang, Gao, Jiang Xin-zi, Tang Kezong, and Yang Jingyu. "Hybrid Algorithm Combining Ant Colony Optimization Algorithm with Particle Swarm Optimization." In 2006 Chinese Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/chicc.2006.280708.

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Changxing, Qi, Bi Yiming, Han Huihua, and Li Yong. "A hybrid particle swarm optimization algorithm." In 2017 3rd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2017. http://dx.doi.org/10.1109/compcomm.2017.8322924.

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Tang, Chenjun, Wei Sun, Wei Wu, and Min Xue. "A hybrid improved whale optimization algorithm." In 2019 IEEE 15th International Conference on Control and Automation (ICCA). IEEE, 2019. http://dx.doi.org/10.1109/icca.2019.8900003.

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Reports on the topic "Keyword: Hybrid Optimization Algorithm"

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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines trajectory optimization, model predictive control (MPC), Lyapunov stability, and hierarchical RL for ensuring safe and robust control in complex environments. Through case studies in self-driving vehicles, autonomous drones, robotic manipulation, healthcare robotics, and multi-agent systems, this research highlights the trade-offs between model-based and model-free approaches, as well as the challenges of scalability, sample efficiency, hardware acceleration, and ethical AI deployment. The findings underscore the importance of hybrid RL-control frameworks, real-world RL training, and policy optimization techniques in advancing robotic intelligence and autonomous decision-making. Keywords: Optimal control, reinforcement learning, model-based RL, model-free RL, dynamic programming, policy optimization, Hamilton-Jacobi-Bellman equations, actor-critic methods, deep reinforcement learning, trajectory optimization, model predictive control, Lyapunov stability, hierarchical RL, multi-agent RL, robotics, self-driving cars, autonomous drones, robotic manipulation, AI-driven automation, safety in RL, hardware acceleration, sample efficiency, hybrid RL-control frameworks, scalable AI.
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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum kernel methods, while analyzing their impact on neural networks, generative models, and reinforcement learning. Hybrid quantum-classical AI architectures, which combine quantum subroutines with classical deep learning models, are examined for their ability to provide computational advantages in optimization and large-scale data processing. Despite the promise of quantum AI, challenges such as qubit noise, error correction, and hardware scalability remain barriers to full-scale implementation. This study provides an in-depth evaluation of quantum-enhanced AI, highlighting existing applications, ongoing research, and future directions in quantum deep learning, autonomous systems, and scientific computing. The findings contribute to the development of scalable quantum machine learning frameworks, offering novel solutions for next-generation AI systems across finance, healthcare, cybersecurity, and robotics. Keywords Quantum machine learning, quantum computing, artificial intelligence, quantum neural networks, quantum kernel methods, hybrid quantum-classical AI, variational quantum algorithms, quantum generative models, reinforcement learning, quantum optimization, quantum advantage, deep learning, quantum circuits, quantum-enhanced AI, quantum deep learning, error correction, quantum-inspired algorithms, quantum annealing, probabilistic computing.
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Homaifar, Abdollah, Albert Esterline, and Bahram Kimiaghalam. Hybrid Projected Gradient-Evolutionary Search Algorithm for Mixed Integer Nonlinear Optimization Problems. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada455904.

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Bellabai, Robert, Jeen Robert, and Ramasubbu Rajkumar. Multi-objective Optimization Using Hybrid Algorithm and Its Application to Scheduling in Flow Shops. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2019. http://dx.doi.org/10.7546/crabs.2019.01.14.

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Li, Yan, Yuhao Luo, and Xin Lu. PHEV Energy Management Optimization Based on Multi-Island Genetic Algorithm. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0739.

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The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor. Afterwards, based on the Isight and ADVISOR co-simulation platform, with the goal of ensuring certain dynamic performance and optimal fuel economy of the vehicle, the multi-island genetic algorithm is used to optimize the parameters of the membership function of the fuzzy control strategy to overcome it to a certain extent. The disadvantages of selecting parameters based on experience are compensated for, and the efficiency and feasibility of fuzzy control are improved. Finally, the PHEV vehicle model simulation comparison was carried out under the UDDS working condition through ADVISOR software. The optimization results show that while ensuring the required power performance, the vehicle fuzzy controller after parameter optimization using the multi-island genetic algorithm is more efficient, which can significantly reduce vehicle fuel consumption and improve exhaust emissions.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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