Name
Title | ||
|---|---|---|
An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1080/09540091.2019.1674247 | CONNECTION SCIENCE |
Keywords | DocType | Volume |
Water wave optimisation,covariance matrix self-adaptation evolution strategy,differential evolution,opposition-based learning mechanism,enhanced water wave optimisation | Journal | 32.0 |
Issue | ISSN | Citations |
2 | 0954-0091 | 1 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fuqing Zhao | 1 | 129 | 22.63 |
| Lixin Zhang | 2 | 6 | 0.76 |
| Yi Zhang | 3 | 400 | 77.93 |
| Weimin Ma | 4 | 427 | 26.76 |
| Chuck Zhang | 5 | 117 | 15.72 |
| Houbin Song | 6 | 1 | 1.02 |