An Improved Evolutionary Algorithm Based on a Multi-Search Strategy and an External Population Strategy for Many-Objective Optimization

Balancing the convergence and diversity of many-objective evolutionary algorithms is difficult and challenging. In this work, a multi-search strategy based on decomposition is proposed to generate good offspring and improve convergence, and an external population strategy is used to maintain the diversity of the obtained solutions. The multi-search strategy allows the selection of sparse and convergent nondominated solutions to carry out the exploration and exploitation steps. Experiments are conducted on 15 benchmark functions from the CEC 2018 with 5, 10, and 15 objectives. The results indicate that the proposed algorithm can obtain a set of solutions with better diversity and convergence than the five efficient state-of-the-art algorithms, i.e. NSGAIII, MOEA/D, MOEA/DD, KnEA, and RVEA.