An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning. Issue 2 (2nd April 2020)

• Record Type: Journal Article
• Authors: Zhao, Fuqing; Zhang, Lixin; Zhang, Yi; Ma, Weimin; Zhang, Chuck; Song, Houbin
• Is Part Of: Connection science. Volume 32:Issue 2(2020)
• Page Start: 132
• Page End: 161
• Publication Date: 2020-04-02
• Subjects: Water wave optimisation -- covariance matrix self-adaptation evolution strategy -- differential evolution -- opposition-based learning mechanism -- enhanced water wave optimisation; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2019.1674247
• Languages: English
• ISSNs: 0954-0091

Abstract

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.