An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning
Water Wave Optimisation algorithm (WWO) is a new swarm-based metaheuristic inspired by shallow wave models for global optimisation. In this paper, an enhanced WWO, which combines with multiple assistant strategies (EWWO), is proposed. First, the random opposition-based learning (ROBL) mechanism is i...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-04-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2019.1674247 |
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| author | Fuqing Zhao Lixin Zhang Yi Zhang Weimin Ma Chuck Zhang Houbin Song |
| author_facet | Fuqing Zhao Lixin Zhang Yi Zhang Weimin Ma Chuck Zhang Houbin Song |
| author_sort | Fuqing Zhao |
| collection | DOAJ |
| description | Water Wave Optimisation algorithm (WWO) is a new swarm-based metaheuristic inspired by shallow wave models for global optimisation. In this paper, an enhanced WWO, which combines with multiple assistant strategies (EWWO), is proposed. First, the random opposition-based learning (ROBL) mechanism is introduced to generate the initial population with high quality. Second, a new modified operation is designed and embedded into propagation operation to balance the global exploration and the local exploitation. Third, the covariance matrix self-adaptation evolution strategy (CMA-ES) is employed by the refraction operation to further strengthen the local exploitation. Furthermore, the diversity of the population is maintained in the evolution process by using a crossover operator. The experiment results based on CEC 2017 benchmarks indicate that the EWWO outperforms the state-of-the-art variant algorithms of the WWO and the standard WWO. |
| format | Article |
| id | doaj-art-0b996853f9064ce19b65ff227c890d08 |
| institution | DOAJ |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2020-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-0b996853f9064ce19b65ff227c890d082025-08-20T03:17:14ZengTaylor & Francis GroupConnection Science0954-00911360-04942020-04-0132213216110.1080/09540091.2019.16742471674247An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learningFuqing Zhao0Lixin Zhang1Yi Zhang2Weimin Ma3Chuck Zhang4Houbin Song5Lanzhou University of TechnologyLanzhou University of TechnologyXijin UniversityTongji UniversityGeorgia Institute of TechnologyLanzhou University of TechnologyWater Wave Optimisation algorithm (WWO) is a new swarm-based metaheuristic inspired by shallow wave models for global optimisation. In this paper, an enhanced WWO, which combines with multiple assistant strategies (EWWO), is proposed. First, the random opposition-based learning (ROBL) mechanism is introduced to generate the initial population with high quality. Second, a new modified operation is designed and embedded into propagation operation to balance the global exploration and the local exploitation. Third, the covariance matrix self-adaptation evolution strategy (CMA-ES) is employed by the refraction operation to further strengthen the local exploitation. Furthermore, the diversity of the population is maintained in the evolution process by using a crossover operator. The experiment results based on CEC 2017 benchmarks indicate that the EWWO outperforms the state-of-the-art variant algorithms of the WWO and the standard WWO.http://dx.doi.org/10.1080/09540091.2019.1674247water wave optimisationcovariance matrix self-adaptation evolution strategydifferential evolutionopposition-based learning mechanismenhanced water wave optimisation |
| spellingShingle | Fuqing Zhao Lixin Zhang Yi Zhang Weimin Ma Chuck Zhang Houbin Song An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning Connection Science water wave optimisation covariance matrix self-adaptation evolution strategy differential evolution opposition-based learning mechanism enhanced water wave optimisation |
| title | An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning |
| title_full | An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning |
| title_fullStr | An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning |
| title_full_unstemmed | An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning |
| title_short | An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning |
| title_sort | improved water wave optimisation algorithm enhanced by cma es and opposition based learning |
| topic | water wave optimisation covariance matrix self-adaptation evolution strategy differential evolution opposition-based learning mechanism enhanced water wave optimisation |
| url | http://dx.doi.org/10.1080/09540091.2019.1674247 |
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