A Hybrid Multiobjective Optimization Based on Nondominated Sorting and Crowding Distance, with Applications to Wave Energy Converters

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has attracted a lot of attention since it can handle multiobjective problems (MOP) with complicated Pareto front. The procedure involves decomposing MOP into single subproblems, which are eventually optimized simultaneously the neighborhood information. However, MOEA/D strategy tends to produce distributed optimization that is not good quality in some complex optimal front, such as long tail and sharp peak, common real-world situations. This paper proposes an improved enhance minimize its complexity while accelerating get better solution. method achieved by incorporating Hybrid Differential Evolution/Particle Swarm Optimization hybrid operator nondominated sorting crowding distance algorithm. incorporation takes place mutation generator initial population part original Simulations comparisons carried out benchmark functions verify proposed method’s performance. experimental results show achieves performance compared other algorithms. Furthermore, also applied optimize wave energy converter model maximize power per year cost unit power.