A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems - Qu, Boyang; Li, Chao; Liang, Jing; Yan, Li; Yu, Kunjie; Zhu, Yongsheng (Applied soft computing, 2020)
This paper proposes a self-organized speciation based multi-objective particle swarm optimizer (SS-MOPSO) to locate multiple Pareto optimal solutions for solving multimodal multi-objective problems. In the proposed method, the speciation strategy is used to form stable niches and these niches/subpopulations are optimized to search and maintain Pareto-optimal solutions in parallel. Moreover, a self-organized mechanism is proposed to improve the efficiency of the species formulation as well as the performance of the algorithm. To maintain the diversity of the solutions in both the decision and objective spaces, SS-MOPSO is incorporated with the non-dominated sorting scheme and special crowding distance techniques. The performance of SS-MOPSO is compared with a number of the state-of-the-art multi-objective optimization algorithms on fourteen test problems. Moreover, the proposed SS-MOSPO is also employed to solve a real-life problem. The experimental results suggest that the proposed algorithm is able to solve the multimodal multi-objective problems effectively and shows superior performance by finding more and better distributed Pareto solutions. •Speciation is employed for the first time to solve the MMOP.•A self-organized mechanism is proposed to improve the performance of the speciation.•The experimental results show that the SS-MOPSO is competitive in solving the MMOP..