Diversity controlled multiobjective particle swarm optimization

For solving the premature in traditional multiobjective particle swarm optimization, a multi-objective particle swarm optimization based on diversity control is proposed. The proposed algorithm utilizes a diversity metric, which is based on weight vectors, to evaluate the population diversity in each generation and control the evolution process of the algorithm adaptively. To maintain population diversity, an adaptive mutation strategy based on Steffensen's method is adopted to update the repository population. With the purpose of balancing the population diversity and convergence, the global best positions of particles areselected adaptively. This algorithm is compared with several widely used multiobjective evolutionary algorithms on a set of benchmark test problems in the experimental part. Statistical results demonstrate the effectiveness of the proposed algorithm. © 2021, The Editorial Board of Journal of Xidian University. All right reserved.