1. An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning.
- Author
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Zhao, Fuqing, Zhang, Lixin, Zhang, Yi, Ma, Weimin, Zhang, Chuck, and Song, Houbin
- Subjects
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WATER waves , *COVARIANCE matrices , *METAHEURISTIC algorithms , *ALGORITHMS , *PARTICLE swarm optimization , *SHALLOW-water equations , *DIFFERENTIAL evolution - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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