A Multipopulation Dynamic Adaptive Coevolutionary Strategy for Large-Scale Complex Optimization Problems
In this paper, a multipopulation dynamic adaptive coevolutionary strategy is proposed for large-scale optimization problems, which can dynamically and adaptively adjust the connection between population particles according to the optimization problem characteristics. Based on analysis of the network...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/5/1999 |
| _version_ | 1797473723395080192 |
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| author | Yanlei Yin Lihua Wang Litong Zhang |
| author_facet | Yanlei Yin Lihua Wang Litong Zhang |
| author_sort | Yanlei Yin |
| collection | DOAJ |
| description | In this paper, a multipopulation dynamic adaptive coevolutionary strategy is proposed for large-scale optimization problems, which can dynamically and adaptively adjust the connection between population particles according to the optimization problem characteristics. Based on analysis of the network evolution characteristics of collaborative search between particles, a dynamic adaptive evolutionary network (DAEN) model with multiple interconnection couplings is established in this algorithm. In the model, the swarm type is divided according to the judgment threshold of particle types, and the dynamic evolution of collaborative topology in the evolutionary process is adaptively completed according to the coupling connection strength between different particle types, which enhances the algorithm’s global and local searching capability and optimization accuracy. Based on that, the evolution rules of the particle swarm dynamic cooperative search network were established, the search algorithm was designed, and the adaptive coevolution between particles in different optimization environments was achieved. Simulation results revealed that the proposed algorithm exhibited a high optimization accuracy and converging rate for high-dimensional and large-scale complex optimization problems. |
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| id | doaj.art-8ad5138ac7404396acfab95bdc24c02d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T20:20:31Z |
| publishDate | 2022-03-01 |
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| series | Sensors |
| spelling | doaj.art-8ad5138ac7404396acfab95bdc24c02d2023-11-23T23:49:35ZengMDPI AGSensors1424-82202022-03-01225199910.3390/s22051999A Multipopulation Dynamic Adaptive Coevolutionary Strategy for Large-Scale Complex Optimization ProblemsYanlei Yin0Lihua Wang1Litong Zhang2Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaIn this paper, a multipopulation dynamic adaptive coevolutionary strategy is proposed for large-scale optimization problems, which can dynamically and adaptively adjust the connection between population particles according to the optimization problem characteristics. Based on analysis of the network evolution characteristics of collaborative search between particles, a dynamic adaptive evolutionary network (DAEN) model with multiple interconnection couplings is established in this algorithm. In the model, the swarm type is divided according to the judgment threshold of particle types, and the dynamic evolution of collaborative topology in the evolutionary process is adaptively completed according to the coupling connection strength between different particle types, which enhances the algorithm’s global and local searching capability and optimization accuracy. Based on that, the evolution rules of the particle swarm dynamic cooperative search network were established, the search algorithm was designed, and the adaptive coevolution between particles in different optimization environments was achieved. Simulation results revealed that the proposed algorithm exhibited a high optimization accuracy and converging rate for high-dimensional and large-scale complex optimization problems.https://www.mdpi.com/1424-8220/22/5/1999large-scale complex optimizationdynamic adaptive evolutionary networkcollaborative topologysearch rules |
| spellingShingle | Yanlei Yin Lihua Wang Litong Zhang A Multipopulation Dynamic Adaptive Coevolutionary Strategy for Large-Scale Complex Optimization Problems Sensors large-scale complex optimization dynamic adaptive evolutionary network collaborative topology search rules |
| title | A Multipopulation Dynamic Adaptive Coevolutionary Strategy for Large-Scale Complex Optimization Problems |
| title_full | A Multipopulation Dynamic Adaptive Coevolutionary Strategy for Large-Scale Complex Optimization Problems |
| title_fullStr | A Multipopulation Dynamic Adaptive Coevolutionary Strategy for Large-Scale Complex Optimization Problems |
| title_full_unstemmed | A Multipopulation Dynamic Adaptive Coevolutionary Strategy for Large-Scale Complex Optimization Problems |
| title_short | A Multipopulation Dynamic Adaptive Coevolutionary Strategy for Large-Scale Complex Optimization Problems |
| title_sort | multipopulation dynamic adaptive coevolutionary strategy for large scale complex optimization problems |
| topic | large-scale complex optimization dynamic adaptive evolutionary network collaborative topology search rules |
| url | https://www.mdpi.com/1424-8220/22/5/1999 |
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