Self-learning differential evolution algorithm for dynamic polycentric problems
A novel self-learning differential evolution algorithm is proposed to solve dynamical multi-center optimization problems.The approach of re-evaluating some specific individuals is used to monitor environmental changes.The proposed self-learning operator guides the evolutionary group to a new environ...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2015-07-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2015154/ |
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| Summary: | A novel self-learning differential evolution algorithm is proposed to solve dynamical multi-center optimization problems.The approach of re-evaluating some specific individuals is used to monitor environmental changes.The proposed self-learning operator guides the evolutionary group to a new environment,meanwhile maintains the stable topology structure of group to maintain the current evolutionary trend.A neighborhood search mechanism and a random immigrant mechanism are adapted to make a tradeoff between algorithmic convergence and population diversity.The experiment studies on a periodic dynamic function set suits are done,and the comparisons with peer algorithms show that the self-learning differential algorithm outperforms other algorithms in term of convergence and adaptability under dynamical environment. |
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| ISSN: | 1000-436X |