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Journal articles on the topic 'Intelligent Optimization Algorithms'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Guo, Aipeng, and Chunhui Yuan. "Network Intelligent Control and Traffic Optimization Based on SDN and Artificial Intelligence." Electronics 10, no. 6 (2021): 700. http://dx.doi.org/10.3390/electronics10060700.

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For telecom operators, it is of great significance to employ artificial intelligence (AI) and big data technology in a software-defined network (SDN) in order to achieve intelligent network control, traffic management and optimization. This paper proposes a solution for intelligent work control and traffic optimization. This paper is mainly focused on SDN-based network traffic algorithm optimization and experimental verification. In this paper, we design a network control mechanism for network intelligent control as well as solutions for traffic optimization based on SDN and artificial intelli

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2

Sobecki, Janusz. "Comparison of Selected Swarm Intelligence Algorithms in Student Courses Recommendation Application." International Journal of Software Engineering and Knowledge Engineering 24, no. 01 (2014): 91–109. http://dx.doi.org/10.1142/s0218194014500041.

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In this paper a comparison of a few swarm intelligence algorithms applied in recommendation of student courses is presented. Swarm intelligence algorithms are nowadays successfully used in many areas, especially in optimization problems. To apply each swarm intelligence algorithm in recommender systems a special representation of the problem space is necessary. Here we present the comparison of efficiency of grade prediction of several evolutionary algorithms, such as: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Intelligent Weed Optimization (IWO), Bee Colony Optimization

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Xu, Minghai, Li Cao, Dongwan Lu, Zhongyi Hu, and Yinggao Yue. "Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization." Biomimetics 8, no. 2 (2023): 235. http://dx.doi.org/10.3390/biomimetics8020235.

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Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biolo

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Peng, Qiang, Renjun Zhan, Husheng Wu, and Meimei Shi. "Comparative Study of Wolf Pack Algorithm and Artificial Bee Colony Algorithm." International Journal of Swarm Intelligence Research 15, no. 1 (2024): 1–24. http://dx.doi.org/10.4018/ijsir.352061.

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Swarm intelligence optimization algorithms have been widely used in the fields of machine learning, process control and engineering prediction, among which common algorithms include ant colony algorithm (ACO), artificial bee colony algorithm (ABC) and particle swarm optimization (PSO). Wolf pack algorithm (WPA) as a newer swarm intelligence optimization algorithm has many similarities with ABC. In this paper, the basic principles, algorithm implementation processes, and related improvement strategies of these two algorithms were described in detail; A comparative analysis of their performance

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Liu, Bingjie, Li Zhu, and Jianlan Ren. "Intelligent optimization algorithm grid computing-based applications." Journal of Intelligent & Fuzzy Systems 39, no. 4 (2020): 5201–11. http://dx.doi.org/10.3233/jifs-189005.

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Optimization algorithms have been rapidly promoted and applied in many engineering fields, such as system control, artificial intelligence, pattern recognition, computer engineering, etc.; achieving optimization in the production process has an important role in improving production efficiency and efficiency and saving resources. At the same time, the theoretical research of optimization methods also plays an important role in improving the performance of the algorithm, widening the application field of the algorithm, and improving the algorithm system. Based on the above background, the purpo

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Bian, Jinhua, and Xiaoxia Zhang. "A Hill-Climbing Differential Evolutionary Algorithm for solving Multiple Travelling Salesman Problem." Frontiers in Computing and Intelligent Systems 9, no. 2 (2024): 4–7. http://dx.doi.org/10.54097/fm6hta13.

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The Multiple Travelling Salesman Problem (MTSP) is a basic deformation of the Travelling Salesman Problem (TSP), which is a typical NP-Hard problem. For combinatorial optimization problems shaped like MTSP, researchers often use intelligent optimization algorithms such as genetic algorithms, differential evolutionary algorithms, simulated annealing algorithms and other intelligent optimization algorithms to approximate the solution. However, typical intelligent optimization algorithms have the disadvantage of being prone to local convergence and failing to produce theoretically optimal solutio

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Kaya, Ebubekir, Ceren Baştemur Kaya, Emre Bendeş, Sema Atasever, Başak Öztürk, and Bilgin Yazlık. "Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking." Biomimetics 8, no. 5 (2023): 402. http://dx.doi.org/10.3390/biomimetics8050402.

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One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in the literature for neural network training. An important group of metaheuristic algorithms is swarm-intelligent-based optimization algorithms. In this study, feed-forward neural network training is carried out for maximum power point tracking by using 13 swarm-intelligent-based optimization al

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Chang, Chang, Tingting Zhang, and Shiyu Chen. "Solving the Problem of Pressure Vessel with Constraint Conditions through Marine Predators Algorithm." Modern Economics & Management Forum 6, no. 1 (2025): 125. https://doi.org/10.32629/memf.v6i1.3568.

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With the development of artificial intelligence technology, various intelligent algorithms are becoming more and more mature. Swarm intelligent algorithm performs well among many algorithms. Optimization problems have been permeating into all aspects of life, and solving the condition optimization problem with constraints is a very important aspect. This paper adopts the newly proposed Marine Predators Algorithm in recent years. The original algorithm is improved by adding constraints. Optimization of pressure vessel problem using Marine predator optimization algorithm. The four variables of s

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Zheng, Lifang, Weixia Liu, and Hangying Chen. "Optimization of Patient Health Management Mechanism Under Intelligent Medical Information System." Journal of Medical Imaging and Health Informatics 12, no. 1 (2022): 83–91. http://dx.doi.org/10.1166/jmihi.2022.3782.

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The establishment of a scientific and complete intelligent medical information analysis application model is of great significance to promote the application of intelligent medical information. Aiming at the deficiencies of Artificial Fish School Algorithm (AFSA) in iterative convergence speed, low optimization accuracy, and Particle Swarm Optimization (PSO) algorithm easily falling into local extremes, this paper combines AFSA and PSO algorithms. We use the fast local convergence ability of the PSO algorithm to overcome the shortcomings of the AFSA algorithm’s low solution accuracy and slow c

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10

Zhang, Li Dong, Lei Jia, and Wen Xing Zhu. "A Review of some Intelligent Optimization Algorithms Applied to Intelligent Transportation System." Advanced Materials Research 383-390 (November 2011): 5717–23. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.5717.

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This paper attempts to summarize the findings of a large number of research papers concerning the application of intelligent optimization algorithms to ITS. A brief introduction to intelligence is included, for the benefit of readers unfamiliar with the techniques. Then it put emphasis on three kinds of intelligent optimization application in ITS, including ANN, GA and PSO. It should be noted first that each of the three subjects can prolong to a long paper, and second that there are also some other intelligent optimization method, such as fuzzy logic, ant colony, shuffle frog-leaping algorith

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Li, Shijin, and Fucai Wang. "Research on Optimization of Improved Gray Wolf Optimization-Extreme Learning Machine Algorithm in Vehicle Route Planning." Discrete Dynamics in Nature and Society 2020 (October 6, 2020): 1–7. http://dx.doi.org/10.1155/2020/8647820.

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With the rapid development of intelligent transportation, intelligent algorithms and path planning have become effective methods to relieve traffic pressure. Intelligent algorithm can realize the priority selection mode in realizing traffic optimization efficiency. However, there is local optimization in intelligence and it is difficult to realize global optimization. In this paper, the antilearning model is used to solve the problem that the gray wolf algorithm falls into local optimization. The positions of different wolves are updated. When falling into local optimization, the current posit

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Ahmet, Demir, and Kose Utku. "BRAIN Journal - Solving Optimization Problems via Vortex Optimization Algorithm and Cognitive Development Optimization Algorithm." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 7, no. 4 (2016): 23–42. https://doi.org/10.5281/zenodo.1045013.

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ABSTRACT In the fields which require finding the most appropriate value, optimization became a vital approach to employ effective solutions. With the use of optimization techniques, many different fields in the modern life have found solutions to their real-world based problems. In this context, classical optimization techniques have had an important popularity. But after a while, more advanced optimization problems required the use of more effective techniques. At this point, Computer Science took an important role on providing software related techniques to improve the associated literature.

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Wisam, Abdulelah Qasim. "A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOLF OPTIMIZATION ALGORITHM." International Journal of Artificial Intelligence and Applications (IJAIA) 11, January (2020): 31–44. https://doi.org/10.5281/zenodo.3690787.

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In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a t

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Li, Pengjiao, and Jun Yang. "PSO Algorithm-Based Design of Intelligent Education Personalization System." Computational Intelligence and Neuroscience 2022 (July 9, 2022): 1–11. http://dx.doi.org/10.1155/2022/9617048.

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The application of artificial intelligence in the field of education is becoming more and more extensive and in-depth. The intelligent education system can not only solve the limitations of location, time, and resources in the traditional learning field but it can also provide learners with a convenient, real-time, and interactive learning environment and has become one of the important applications in the Internet field. Particle swarm optimization (PSO) is a swarm intelligence-enabled stochastic optimization scheme. It is derived from a model of bird population foraging behavior. Because of

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15

AlDeeb, Bashar Abedal Mohdi, Norita Md Norwawi, and Mohammed A. Al-Betar. "A Survey on Intelligent Water Drop Algorithm." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 10 (2014): 5075–84. http://dx.doi.org/10.24297/ijct.v13i10.2329.

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In the optimization areas, there are different algorithms that have been applied such as swarm intelligence algorithms. The researchers have found different algorithms by simulating the behaviors of various swarms of insects and animals such as fishes, bees, and ants. The intelligent water drops algorithm is one of the recently developed algorithms in the swarm intelligence field; this algorithm mimicked the dynamic of river systems. The natural water drops used to develop Intelligent Water Drop (IWD) algorithm. Therefore, the mechanisms that happen in rivers have inspired the researchers main

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Yang, Jian, and Xuejun Huang. "Intelligent Planning Modeling and Optimization of UAV Cluster Based on Multi-Objective Optimization Algorithm." Electronics 11, no. 24 (2022): 4238. http://dx.doi.org/10.3390/electronics11244238.

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As a flight tool integrating carrier and reconnaissance, unmanned aerial vehicles (UAVs) are applied in various fields. In recent years, mission planning and path optimization have become the most important research focuses in the field of UAVs. With the continuous maturity of artificial intelligence technology, various search algorithms have been applied in the field of unmanned aerial vehicles. However, these algorithms have certain defects, which lead to problems, such as large search volume and low efficiency in task planning, and cannot meet the requirements of path planning. The objectiv

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17

Gong, Fanghai. "Application of Artificial Intelligence Computer Intelligent Heuristic Search Algorithm." Advances in Multimedia 2022 (September 24, 2022): 1–12. http://dx.doi.org/10.1155/2022/5178515.

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In order to transform three-dimensional space path planning into two-dimensional plane path planning problem and greatly reduce the search time, an intelligent heuristic search algorithm based on artificial intelligence is proposed. The heuristic search algorithm is analyzed and introduced, and A is chosen. A two-dimensional spatial environment model of picking robot path planning is investigated, and a spatial model of picking robot path planning is established by raster method. Then, considering the whole day operation time, the whole day operation time is divided into several periods. With

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18

Alatas, Bilal, and Harun Bingol. "Comparative Assessment Of Light-based Intelligent Search And Optimization Algorithms." Volume 28, Number 6, 2020, no. 03-2020 (December 2020): 51–59. http://dx.doi.org/10.33383/2019-029.

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Classical optimization and search algorithms are not effective for nonlinear, complex, dynamic large-scaled problems with incomplete information. Hence, intelligent optimization algorithms, which are inspired by natural phenomena such as physics, biology, chemistry, mathematics, and so on have been proposed as working solutions over time. Many of the intelligent optimization algorithms are based on physics and biology, and they work by modelling or simulating different nature-based processes. Due to philosophy of constantly researching the best and absence of the most effective algorithm for a

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19

Zhao, Qixing. "Improved Grey Wolf Optimization Algorithm based on SoftPlus Inertia Weight Strategy." Transactions on Computer Science and Intelligent Systems Research 1 (October 12, 2023): 193–201. http://dx.doi.org/10.62051/4g6y6057.

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In order to solve the problems of the Grey Wolf Optimization (GWO) algorithm, such as low optimization accuracy and low convergence speed, this paper proposes an Improved Grey Wolf Optimization Algorithm Based on SoftPlus Inertia Weight Strategy (SGWO). SGWO combines the nonlinear characteristics of SoftPlus function, and introduces the inertia weight strategy based on SoftPlus function to improve the optimization performance of GWO. Eight classic test functions are used to compare the performance of SGWO with five classical swarm intelligence algorithms. Experimental results show that SGWO is

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20

Jin, Juanli. "The Intelligent Selection Method of Distribution Sites Driven by the Intelligent Optimization Algorithm." Mobile Information Systems 2022 (October 7, 2022): 1–13. http://dx.doi.org/10.1155/2022/9266844.

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For the internal enterprise, the intelligent selection of logistics distribution sites can optimize the distribution route, which is conducive to reducing the total distribution cost and improving the enterprise revenue. This paper takes urban logistics distribution point selection optimization as the research content and compares and analyses the applicability of different intelligent algorithms to logistics data. The AFSA is selected as the optimization algorithm, and knowledge learning is introduced to optimize the algorithm. The optimized AFSA model is applied to the mathematical model of

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21

Cheng, Hua, Huan Zhou, and Yue Shen. "An improved grey wolf optimization algorithm based on bounded subpopulation re-search strategy." Journal of Physics: Conference Series 2902, no. 1 (2024): 012035. https://doi.org/10.1088/1742-6596/2902/1/012035.

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Abstract Swarm intelligence optimization algorithms are widely used in solving and optimizing complex problems in various industries due to their simple and effective structure, as well as ease of deployment and implementation. The paper puts forward a new grey wolf optimization (GWO) algorithm by using bounded grey wolf subpopulation re-search strategy to deal with problems of local optima and low precision in solving complex control, decision-making, and planning problems in intelligent optimization algorithms. Firstly, based on the traditional GWO algorithm model, the intelligent individual

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Singh, Chandrabhan, Mohit Gangwar, and Upendra Kumar. "Enhancing Feature Selection Accuracy using Butterfly and Lion Optimization Algorithm with Specific Reference to Psychiatric Disorder Detection & Diagnosis." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 11 (2022): 107–14. http://dx.doi.org/10.17762/ijritcc.v10i11.5786.

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As the complexity of medical computing increases the use of intelligent methods based on methods of soft computing also increases. During current decade this intelligent computing involves various meta-heuristic algorithms for Optimization. Many new meta-heuristic algorithms are proposed in last few years. The dimension of this data has also wide. Feature selection processes play an important role in these types of wide data. In intelligent computation feature selection is important phase after the pre-processing phase. The success of any model depends on how better optimization algorithms is

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Jameel, Noor, and Hasanen S. Abdullah. "Intelligent Feature Selection Methods: A Survey." Engineering and Technology Journal 39, no. 1B (2021): 175–83. http://dx.doi.org/10.30684/etj.v39i1b.1623.

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Consider feature selection is the main in intelligent algorithms and machine learning to select the subset of data to help acquire the optimal solution. Feature selection used an extract the relevance of the data and discarding the irrelevance of the data with increment fast to select it and to reduce the dimensional of dataset. In the past, it used traditional methods, but these methods are slow of fast and accuracy. In modern times, however, it uses the intelligent methods, Genetic algorithm and swarm optimization methods Ant colony, Bees colony, Cuckoo search, Particle optimization, fish al

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Liu, Songzi, Mou Lv, and Hongwei Li. "Intelligent Leakage Location of Urban Small Water Supply Network." Journal of Physics: Conference Series 2185, no. 1 (2022): 012041. http://dx.doi.org/10.1088/1742-6596/2185/1/012041.

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Abstract In this paper, the leakage location model of pipe network is established based on EPANET software. Two intelligent swarm optimization algorithms, ant lion optimization algorithm and particle swarm optimization algorithm, are used to solve the model. Taking the industrial water supply network of a coastal city in North China as an example, the operation of the two algorithms is analyzed and compared. The results show that the ant lion optimization algorithm has stronger global optimization ability and higher search efficiency in the problem of leakage location; it also has high applica

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Xu, Weixiang, Dongbao Jia, Zhaoman Zhong, Cunhua Li, and Zhongxun Xu. "Intelligent Dendritic Neural Model for Classification Problems." Symmetry 14, no. 1 (2021): 11. http://dx.doi.org/10.3390/sym14010011.

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In recent years, the dendritic neural model has been widely employed in various fields because of its simple structure and inexpensive cost. Traditional numerical optimization is ineffective for the parameter optimization problem of the dendritic neural model; it is easy to fall into local in the optimization process, resulting in poor performance of the model. This paper proposes an intelligent dendritic neural model firstly, which uses the intelligent optimization algorithm to optimize the model instead of the traditional dendritic neural model with a backpropagation algorithm. The experimen

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Kaushal, Payal, Meenu Khurana, and K. R. Ramkumar. "A Systematic Review of Swarm Intelligence Algorithms to Perform Routing for VANETs Communication." ECS Transactions 107, no. 1 (2022): 5027–35. http://dx.doi.org/10.1149/10701.5027ecst.

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The performance of Vehicular Adhoc Networks (VANETs), the underlying technology for intelligent vehicles, has shown improvement with the application of swarm-based algorithms for routing. Swarm Intelligence is a self-intelligence group of similar agents functioning on distribution, flexibility, and communication among the agents. The multiple problems occurring in the modern communication systems, including VANETs, have been tried to be resolved by the application of swarm intelligence algorithms viz, Genetic Algorithms (GA), Differential Evolution (DE), Ant Colony Optimization (ACO), Particle

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Zhao, Xiaoai. "Deep Mining Technology of Database Information Based on Artificial Intelligence Technology." International Journal of Information Technologies and Systems Approach 16, no. 2 (2023): 1–13. http://dx.doi.org/10.4018/ijitsa.316458.

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The database is the core of the information management system. When there is no modern management tool such as enterprise information management system, the status of enterprise resources and the direction of adjustment are unclear, and it is quite difficult to make adjustment arrangements. In order to improve the information mining ability of enterprise database in the future, this paper combines intelligent optimization technology under artificial intelligence technology with information mining technology, and compares its information mining ability with traditional technology. The research

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Lin, Tsung-Xian, Zhong-huan Wu, and Wen-Tsao Pan. "Optimal location of logistics distribution centres with swarm intelligent clustering algorithms." PLOS ONE 17, no. 8 (2022): e0271928. http://dx.doi.org/10.1371/journal.pone.0271928.

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A clustering algorithm is a solution for grouping a set of objects and for distribution centre location problems. But the common K-means clustering algorithm may give local optimal solutions. Swarm intelligent algorithms simulate the social behaviours of animals and avoid local optimal solutions. We employ three swarm intelligent algorithms to avoid these solutions. We propose a new algorithm for the clustering problem, the fruit-fly optimization K-means algorithm (FOA K-means). We designed a distribution centre location problem and three clustering indicators to evaluate the performance of al

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Rao, Xiaoyang, and Xuesong Yan. "Particle Swarm Optimization Algorithm Based on Information Sharing in Industry 4.0." Wireless Communications and Mobile Computing 2022 (March 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/4328185.

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Intelligent manufacturing is an important part of Industry 4.0; artificial intelligence technology is a necessary means to realize intelligent manufacturing. This requires the exploration of pattern recognition, computer vision, intelligent optimization, and other related technologies. Particle swarm optimization (PSO) algorithm is an optimization algorithm inspired by the foraging behavior of birds. PSO was an intelligent technology and an efficient optimization algorithm verified by a lot of research and experiments. In this paper, the traditional PSO algorithm is compared with genetic algor

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Zhang, Dong Yan, Chun Yan Zhang, Liang Kuan Zhu, and Zhi Duo Diao. "A Novel Intelligent Modeling Method for Wood Drying Process." Applied Mechanics and Materials 121-126 (October 2011): 647–51. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.647.

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This paper investigates the development and intelligent modeling problem for a wood drying kiln process via optimized support vector machine (SVM). Based on parameters optimization and model selection idea, the swarm intelligence algorithms of Particle Swarm Optimization (PSO)-SVM and Genetic Algorithm (GA)-SVM were proposed for wood drying process with strong coupling and nonlinear characteristics. The simulation results showed that both of these two kinds of swarm intelligence optimization algorithm could get the appropriate parameters of SVM effectively, and by contrast, PSO showed a better

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Chen, Shan. "Workshop Layout Optimization Based on Improved SLP Method and Intelligent Algorithms: Literature Review and Innovative Directions." International Journal of Global Economics and Management 6, no. 3 (2025): 160–63. https://doi.org/10.62051/ijgem.v6n3.21.

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In recent years, research on production workshop layout optimization has advanced rapidly in the context of intelligent manufacturing, with the integration of traditional Systematic Layout Planning (SLP) and intelligent algorithms emerging as a mainstream trend. This paper reviews the latest developments in workshop layout optimization, including: (1) The fusion of SLP and intelligent algorithms (e.g., genetic algorithms, sparrow search algorithm, particle swarm optimization), significantly improving logistics efficiency and non-logistics relationships; (2) The application of digital twins and

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Xu, Kaige. "Comprehensive Analysis and Applications of Fuzzy PID Control." Highlights in Science, Engineering and Technology 111 (August 19, 2024): 465–72. https://doi.org/10.54097/q1y37b65.

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With the advancement of artificial intelligence and enhanced computing power, the integration and application of fuzzy control and other algorithms have garnered significant attention. This paper reviews the innovations and challenges of fuzzy control systems, focusing on the fundamental principles, development history, optimization algorithms, and future development prospects and challenges of fuzzy PID control. Through a comprehensive literature review and case analysis, the advantages and limitations of fuzzy PID control in practical applications are examined, and the impact of various opti

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Qin, Yuqi. "Research on machine learning Algorithm optimization based on 0-1 coding." Journal of Physics: Conference Series 2083, no. 4 (2021): 042086. http://dx.doi.org/10.1088/1742-6596/2083/4/042086.

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Abstract Machine learning algorithm is the core of artificial intelligence, is the fundamental way to make computer intelligent, its application in all fields of artificial intelligence. Aiming at the problems of the existing algorithms in the discrete manufacturing industry, this paper proposes a new 0-1 coding method to optimize the learning algorithm, and finally proposes a learning algorithm of “IG type learning only from the best”.

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Sun, Xiaotian. "A Novel Method for Handicrafts Design Based on Fusion of Multi-Intelligent Decision Algorithm." Scientific Programming 2022 (January 3, 2022): 1–13. http://dx.doi.org/10.1155/2022/8495381.

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With the rapid development of artificial intelligence, handicraft design has developed from artificial design to artificial intelligence design. Traditional handicraft design has the problems of long time consumption and low output, so it is necessary to improve the process technology. Artificial intelligence technology can provide optimized design steps in handicraft design and improve design efficiency and process level. Handicrafts are regarded as important social products and exist in people’s daily life. In the current society, many people do handicrafts and there are major exhibitions. F

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Huang, Xiufeng, Rongwu Xu, Wenjing Yu, and Shiji Wu. "Evaluation and Analysis of Heuristic Intelligent Optimization Algorithms for PSO, WDO, GWO and OOBO." Mathematics 11, no. 21 (2023): 4531. http://dx.doi.org/10.3390/math11214531.

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In order to comprehensively evaluate and analyze the effectiveness of various heuristic intelligent optimization algorithms, this research employed particle swarm optimization, wind driven optimization, grey wolf optimization, and one-to-one-based optimizer as the basis. It applied 22 benchmark test functions to conduct a comparison and analysis of performance for these algorithms, considering descriptive statistics such as convergence speed, accuracy, and stability. Additionally, time and space complexity calculations were employed, alongside the nonparametric Friedman test, to further assess

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VASYLKIVSKYI, Mikola, Andrii PRYKMETA, Andrii OLIYNYK, and Diana NIKITOVYCH. "OPTIMIZATION OF INTELLIGENT TELECOMMUNICATION NETWORKS." Herald of Khmelnytskyi National University. Technical sciences 217, no. 1 (2023): 33–41. http://dx.doi.org/10.31891/2307-5732-2023-317-1-33-41.

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The paper presents the results of research on the use of machine learning in telecommunication networks and describes the basics of the theory of artificial intelligence. The impact of dynamic Bayesian network (DBN) and DNN on the development of many technologies, including user activity detection, channel estimation, and mobility tracking, is determined. The indicators of the effectiveness of communications based on the theory of information bottlenecks, which is at the junction of machine learning and forecasting, statistics and information theory, are considered. A neural network model that

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Anchekov, M. I., A. Z. Apshev, K. Ch Bzhikhatlov, et al. "Principles of ontophylogenetic development of artificial general intelligence systems based on multi-agent neurocognitive architectures." BIO Web of Conferences 84 (2024): 02015. http://dx.doi.org/10.1051/bioconf/20248402015.

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The purpose of the study is to study the possibilities of multigenerational optimization of behavior control systems for agents of general artificial intelligence capable of independently solving a universal range of tasks in a real environment. The main principles of ontophylogenetic synthesis of control systems for agents of general artificial intelligence based on multi-agent neurocognitive architectures have been developed. Methods and algorithms for synthesizing the phenotypes of control systems of intelligent agents according to their genotypes are proposed. A software package for simula

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Patil, Ramakant S., Sharad P. Jadhav, and Machhindranath D. Patil. "Review of Intelligent and Nature-Inspired Algorithms-Based Methods for Tuning PID Controllers in Industrial Applications." Journal of Robotics and Control (JRC) 5, no. 2 (2024): 336–58. https://doi.org/10.18196/jrc.v5i2.20850.

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PID controllers can regulate and stabilize processes in response to changes and disturbances. This paper provides a comprehensive review of PID controller tuning methods for industrial applications, emphasizing intelligent and nature-inspired algorithms. Techniques such as Fuzzy Logic (FL), Artificial Neural Networks (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) are explored. Additionally, nature-inspired algorithms, including evolutionary algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Simulated

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Bassim M. Salih, Omer K. Jasim Mohammad,. "Improving Task Scheduling in Cloud Datacenters by Implementation of an Intelligent Scheduling Algorithm." Journal of Electrical Systems 20, no. 3s (2024): 1465–78. http://dx.doi.org/10.52783/jes.1522.

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The need for mobile and online Apps and services has led to rapid growth in cloud computing services. The rapid increase in growth highlights the importance of reducing the time required for scheduling and efficiently utilizing resources in a dynamic setting. Consequently, numerous scheduling algorithms have been devised to address these problems by employing intelligent scheduling approaches, including Genetic Algorithms, greedy algorithm, Antlion Optimizer, Ant Colony optimization, and Cuckoo Intelligent Algorithm. This study provides an overview of intelligent optimization strategies, with

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Du, Sheng, Zixin Huang, Li Jin, and Xiongbo Wan. "Recent Progress in Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes." Algorithms 17, no. 12 (2024): 569. https://doi.org/10.3390/a17120569.

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This editorial discusses recent progress in data-driven intelligent modeling and optimization algorithms for industrial processes. With the advent of Industry 4.0, the amalgamation of sophisticated data analytics, machine learning, and artificial intelligence has become pivotal, unlocking new horizons in production efficiency, sustainability, and quality assurance. Contributions to this Special Issue highlight innovative research in advancements in work-sampling data analysis, data-driven process choreography discovery, intelligent ship scheduling for maritime rescue, process variability monit

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Lie, Luo. "Heuristic Artificial Intelligent Algorithm for Genetic Algorithm." Key Engineering Materials 439-440 (June 2010): 516–21. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.516.

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A genetic algorithm is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.

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Huang, Huadong, and Heng Liao. "An improved crow search algorithm with multi strategy disturbance." ITM Web of Conferences 47 (2022): 02007. http://dx.doi.org/10.1051/itmconf/20224702007.

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As a new meta heuristic intelligent algorithm, crow search algorithm simulates the behavior of crows following each other and stealing food. Due to the simplicity and robustness of crow search algorithm, it has been successfully applied in many fields. However, like other swarm intelligence optimization algorithms, crow search algorithm also has the disadvantages of slow convergence speed and easy to fall into local optimization. In order to improve the convergence accuracy and later search ability of the algorithm, a new hybrid crow search algorithm called multi strategy disturbance improved

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Li, Kangshun, Fahui Gu, Wei Li, and Ying Huang. "A Dual-Population Evolutionary Algorithm Adapting to Complementary Evolutionary Strategy." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 01 (2018): 1959004. http://dx.doi.org/10.1142/s0218001419590043.

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Optimization problems widely exist in scientific research and engineering practice, which have been one of the research hotshots and difficulties in intelligent computing. The single swarm intelligence optimization algorithms often show such defects as searching stagnation, low accuracy of convergence, part optimum and poor generalization ability when facing the increasingly sophisticated optimization problems. In the study of multiple population, the choice of evolution strategy often has great influence on the performance of the algorithm, and this paper puts forward a kind of dual-populatio

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Guo, Binghua, and Nan Guo. "Cash Collection Model of Electric Power Business Office Based on Computer Algorithm." Journal of Physics: Conference Series 2146, no. 1 (2022): 012023. http://dx.doi.org/10.1088/1742-6596/2146/1/012023.

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Abstract With the continuous development of intelligent algorithms, mobile robot (hereinafter referred to as MR) technology is gradually mature, which has been widely used in a variety of industries, such as industry, agriculture, medical treatment, service and so on. With the improvement of intelligent level, people have higher and higher requirements for MRs, which requires MRs to constantly adapt to different environments, especially dynamic environments. In the dynamic environment, obstacle avoidance technology has become the focus of intelligent robot research, which needs to continuously

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Li, Hao, Bolin Liao, Jianfeng Li, and Shuai Li. "A Survey on Biomimetic and Intelligent Algorithms with Applications." Biomimetics 9, no. 8 (2024): 453. http://dx.doi.org/10.3390/biomimetics9080453.

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The question “How does it work” has motivated many scientists. Through the study of natural phenomena and behaviors, many intelligence algorithms have been proposed to solve various optimization problems. This paper aims to offer an informative guide for researchers who are interested in tackling optimization problems with intelligence algorithms. First, a special neural network was comprehensively discussed, and it was called a zeroing neural network (ZNN). It is especially intended for solving time-varying optimization problems, including origin, basic principles, operation mechanism, model

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Karunanidy, Dinesh, Rajakumar Ramalingam, Ankur Dumka, et al. "An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs." Processes 9, no. 12 (2021): 2171. http://dx.doi.org/10.3390/pr9122171.

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Intelligent Transportation system are becoming an interesting research area, after Internet of Things (IoT)-based sensors have been effectively incorporated in vehicular ad hoc networks (VANETs). The optimal route discovery in a VANET plays a vital role in establishing reliable communication in uplink and downlink direction. Thus, efficient optimal path discovery without a loop-free route makes network communication more efficient. Therefore, this challenge is addressed by nature-inspired optimization algorithms because of their simplicity and flexibility for solving different kinds of optimiz

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Ajeil, Fatin Hassan, Ibraheem Kasim Ibraheem, Ahmad Taher Azar, and Amjad J. Humaidi. "Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment." International Journal of Advanced Robotic Systems 17, no. 3 (2020): 172988142092949. http://dx.doi.org/10.1177/1729881420929498.

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The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. Two modifications are suggested to improve the searching process of the standard bat algorithm with the result of two novel algorithms. The first algorithm is a Modified Frequency Bat algorithm, and the second is a hybridization between the Particle Swarm Optimization with the Modified Frequency Bat algorithm, namely, the Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithm. Both Modified Frequency Bat and

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Devarajan, Jinil Persis, and T. Paul Robert. "Swarm Intelligent Data Aggregation in Wireless Sensor Network." International Journal of Swarm Intelligence Research 11, no. 2 (2020): 1–18. http://dx.doi.org/10.4018/ijsir.2020040101.

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Data aggregation in WSNs is an interesting problem wherein data sensed by the sensors is routed to an aggregation node in an efficient way. Since the sensors are battery operated, it is very important for a routing protocol to conserve energy and also ensure load balancing and faster delivery. In this study, a multi-objective linear programming model is developed for this problem and solved using an exact algorithm applying dominance principle. In order to ensure faster convergence, routing algorithms incorporating strategies of swarms in nature such as Ants, Bees and Fireflies are adapted. In

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Xu, Haiman. "An Intelligent Optimization for Building Design Based on BP Neural Network and SPEA-II Multiobjective Algorithm." Computational Intelligence and Neuroscience 2022 (April 22, 2022): 1–11. http://dx.doi.org/10.1155/2022/3667187.

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With the continuous development of the field of building optimization, more and more optimization methods have sprung up, among which there are many kinds of intelligent optimization algorithms. This kind of intelligent optimization algorithm usually relies on the traditional building performance simulation method to obtain the building performance index for optimization. However, intelligent optimization algorithms generally require large-scale calculations. At the same time, the time required for building performance simulation is often limited by the complexity of building models and the co

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Wen, Lingying, Wen Lin, and Mingde Guo. "Study on Optimization of Marketing Communication Strategies in the Era of Artificial Intelligence." Mobile Information Systems 2022 (October 11, 2022): 1–11. http://dx.doi.org/10.1155/2022/1604184.

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This paper starts from the research of computational advertising and scene theory, based on the background of artificial intelligence technology application, and studies the communication strategy of the computational advertising scene. It studies the three major strategies of computing advertising scene communication and deeply analyzes the principles and applications of scene insight strategy, content selection strategy, and community operation strategy. Results show the following: (1) under the background that the era of artificial intelligence has arrived, computing advertising uses artifi

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