Two-Archive Evolutionary Algorithm Based on Multi-Search Strategy for Many-Objective Optimization

Abstract

Taking both convergence and diversity into consideration, this paper proposes a two-archive an evolutionary algorithm based on multi-search strategy (TwoArchM) to cope with many-objective optimization problems. The basic idea is to use two separate archives to balance the convergence and diversity and use a multi-search strategy to improve convergence and diversity. To be specific, two updated strategies are adopted to maintain diversity and improve the convergence, respectively; a multi-search strategy is utilized to balance exploration and exploitation. A search strategy selects convergent solutions from offspring and two archives as parents to enhance the convergence; the goal of another search strategy is to balance exploration and exploitation. The TwoArchM is compared experimentally with several state-of-the-art algorithms on the CEC2018 many-objective benchmark functions with up to 15 objectives and the experimental results verify the competitiveness and effectiveness of the proposed algorithm.