An Adaptive Lion Swarm Optimization Algorithm Incorporating Tent Chaotic Search and Information Entropy
Abstract This paper proposes an improved adaptive lion swarm optimization (LSO) algorithm integrating the chaotic search strategy and information entropy to address the problem that the standard LSO algorithm has slow convergence and easily falls into the local optimum in later iterations. At first,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-03-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00216-7 |
| _version_ | 1797859805906337792 |
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| author | Miaomiao Liu Yuying Zhang Jingfeng Guo Jing Chen Zhigang Liu |
| author_facet | Miaomiao Liu Yuying Zhang Jingfeng Guo Jing Chen Zhigang Liu |
| author_sort | Miaomiao Liu |
| collection | DOAJ |
| description | Abstract This paper proposes an improved adaptive lion swarm optimization (LSO) algorithm integrating the chaotic search strategy and information entropy to address the problem that the standard LSO algorithm has slow convergence and easily falls into the local optimum in later iterations. At first, an adaptive factor is introduced to improve tent chaotic mapping and used for population position initialization to enhance population diversity and realize uniform traversal while ensuring random distribution, ultimately improving the global search ability. Second, to address the problem that the cub selection strategy is blind, resulting in insufficient traversal in the early stage, a dynamic step-size perturbation factor is established using the second-order norm and information entropy. Adaptive parameters are used to dynamically adjust the selection probability of different cub behaviors based on the number of iterations to suppress the premature convergence of the algorithm. Finally, tent chaotic search is employed to adaptively adjust the search range and improve the individuals with poor fitness through multiple neighborhood points of the local optimal solution, further improving the algorithm’s search speed and accuracy. Experimental results on 18 benchmark functions revealed that the proposed algorithm yields superior performance in terms of convergence speed, optimization accuracy, and ability to jump out of the local optimal solution compared with the standard LSO, gray wolf optimizer, and particle swarm optimization algorithms. Furthermore, the improved LSO algorithm was used to optimize the initial weights and thresholds of the BP neural network, and the effectiveness of the proposed algorithm was further verified by studying the house price prediction problem using two real-world datasets. |
| first_indexed | 2024-04-09T21:35:32Z |
| format | Article |
| id | doaj.art-2d0c9c0f6aaa4482878f2192a710bf9a |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-09T21:35:32Z |
| publishDate | 2023-03-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-2d0c9c0f6aaa4482878f2192a710bf9a2023-03-26T11:17:33ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-03-0116111810.1007/s44196-023-00216-7An Adaptive Lion Swarm Optimization Algorithm Incorporating Tent Chaotic Search and Information EntropyMiaomiao Liu0Yuying Zhang1Jingfeng Guo2Jing Chen3Zhigang Liu4School of Computer and Information Technology, Northeast Petroleum UniversitySchool of Computer and Information Technology, Northeast Petroleum UniversityCollege of Information Science and Engineering, Yanshan UniversityCollege of Information Science and Engineering, Yanshan UniversitySchool of Computer and Information Technology, Northeast Petroleum UniversityAbstract This paper proposes an improved adaptive lion swarm optimization (LSO) algorithm integrating the chaotic search strategy and information entropy to address the problem that the standard LSO algorithm has slow convergence and easily falls into the local optimum in later iterations. At first, an adaptive factor is introduced to improve tent chaotic mapping and used for population position initialization to enhance population diversity and realize uniform traversal while ensuring random distribution, ultimately improving the global search ability. Second, to address the problem that the cub selection strategy is blind, resulting in insufficient traversal in the early stage, a dynamic step-size perturbation factor is established using the second-order norm and information entropy. Adaptive parameters are used to dynamically adjust the selection probability of different cub behaviors based on the number of iterations to suppress the premature convergence of the algorithm. Finally, tent chaotic search is employed to adaptively adjust the search range and improve the individuals with poor fitness through multiple neighborhood points of the local optimal solution, further improving the algorithm’s search speed and accuracy. Experimental results on 18 benchmark functions revealed that the proposed algorithm yields superior performance in terms of convergence speed, optimization accuracy, and ability to jump out of the local optimal solution compared with the standard LSO, gray wolf optimizer, and particle swarm optimization algorithms. Furthermore, the improved LSO algorithm was used to optimize the initial weights and thresholds of the BP neural network, and the effectiveness of the proposed algorithm was further verified by studying the house price prediction problem using two real-world datasets.https://doi.org/10.1007/s44196-023-00216-7Lion swarm optimization algorithmTent chaotic mappingInformation entropyAdaptive parameterTent chaotic searchSecond-order norm |
| spellingShingle | Miaomiao Liu Yuying Zhang Jingfeng Guo Jing Chen Zhigang Liu An Adaptive Lion Swarm Optimization Algorithm Incorporating Tent Chaotic Search and Information Entropy International Journal of Computational Intelligence Systems Lion swarm optimization algorithm Tent chaotic mapping Information entropy Adaptive parameter Tent chaotic search Second-order norm |
| title | An Adaptive Lion Swarm Optimization Algorithm Incorporating Tent Chaotic Search and Information Entropy |
| title_full | An Adaptive Lion Swarm Optimization Algorithm Incorporating Tent Chaotic Search and Information Entropy |
| title_fullStr | An Adaptive Lion Swarm Optimization Algorithm Incorporating Tent Chaotic Search and Information Entropy |
| title_full_unstemmed | An Adaptive Lion Swarm Optimization Algorithm Incorporating Tent Chaotic Search and Information Entropy |
| title_short | An Adaptive Lion Swarm Optimization Algorithm Incorporating Tent Chaotic Search and Information Entropy |
| title_sort | adaptive lion swarm optimization algorithm incorporating tent chaotic search and information entropy |
| topic | Lion swarm optimization algorithm Tent chaotic mapping Information entropy Adaptive parameter Tent chaotic search Second-order norm |
| url | https://doi.org/10.1007/s44196-023-00216-7 |
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