A two-archive model based evolutionary algorithm for multimodal multi-objective optimization problems - Hu, Yi; Wang, Jie; Liang, Jing; Wang, Yanli; Ashraf, Usman; Yue, Caitong; Yu, Kunjie (Applied soft computing, 2022)
Multimodal multi-objective optimization (MMO) can offer more elegant solutions and provide diverse decisions to decision-makers in real world optimization problems. Many multimodal evolutionary mechanisms have been proposed to explore and exploit two solution spaces (i.e. decision space and objective space) in recent years. However, most existing methods only use single evolutionary operator to generate offsprings and ignore the advantage of using hybrid evolutionary algorithm. Moreover, it is still a great challenge to balance the effectiveness and efficiency simultaneously in the evolutionary process of MMO. In view of this, an efficient Two-Archive model based multimodal evolutionary algorithm is proposed in this paper. Two parallel offspring generation mechanisms based on competitive particle swarm optimizer and differential evolution are applied to expand two solution spaces with different evolutionary requirements. Moreover, niching local search scheme and reverse vector mutation strategy play roles in achieving better convergence and diversity. Finally, 22 MMO test problems are used to validate the superiority of the proposed method by comparing it with 5 state-of-the-art MMO algorithms. The proposed method is also expanded to solve 9 feature selection problems for validating the effectiveness of the proposed method on real world applications. •Two offspring generation mechanisms based on competitive PSO and DE are proposed.•Two diversity maintenance strategies named niching local search scheme and reverse vector mutation strategy are designed.•This paper proposes a novel multimodal multi-objective optimization algorithm with a new Two-Archive model.•Experiments validate that the proposed method can solve MMOPs effectively and efficiently with a relatively smaller population size.•The proposed algorithm is expanded to deal with 9 real world feature selection problems.