A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization
A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial b...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2012/578064 |
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| _version_ | 1832548798617878528 |
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| author | Daqing Wu Jianguo Zheng |
| author_facet | Daqing Wu Jianguo Zheng |
| author_sort | Daqing Wu |
| collection | DOAJ |
| description | A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC) for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms. |
| format | Article |
| id | doaj-art-195c35f40c4546359a44085e981630cb |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-195c35f40c4546359a44085e981630cb2025-02-03T06:12:54ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2012-01-01201210.1155/2012/578064578064A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function OptimizationDaqing Wu0Jianguo Zheng1Glorious Sun School of Business and Management, DongHua University, Shanghai 200051, ChinaGlorious Sun School of Business and Management, DongHua University, Shanghai 200051, ChinaA novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC) for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.http://dx.doi.org/10.1155/2012/578064 |
| spellingShingle | Daqing Wu Jianguo Zheng A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization Discrete Dynamics in Nature and Society |
| title | A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization |
| title_full | A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization |
| title_fullStr | A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization |
| title_full_unstemmed | A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization |
| title_short | A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization |
| title_sort | dynamic multistage hybrid swarm intelligence optimization algorithm for function optimization |
| url | http://dx.doi.org/10.1155/2012/578064 |
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