Multi-strategy Adaptive Multi-objective Particle Swarm Optimization Algorithm Based on Swarm Partition

In the multi-objective particle swarm optimization algorithm, balancing the convergence and diversity of the algorithm is the key to obtain the Pareto front with good distribution and accuracy. Most of the proposed methods rely on only one strategy to guide the particle search, and the algorithm may lack convergence and diversity when solving complex problems. To solve this problem, a multi-strategy adaptive multi-objective particle swarm optimization based on swarm partition is proposed. Firstly, the algorithm detects environment by the convergence contribution of particles and adjusts the process of particle exploration and exploitation adaptively. Secondly, in order to accurately formulate the search strategy of particles with different performances, a multi-strategy global optimal particle selection method and a mutation method are proposed. According to the evaluation index of the convergence of the particles, the population is divided into three regions. Combining particle performance with the algorithm optimization process can improve the search efficiency of each particle. Thirdly, an individual optimal particle selection scheme with memory interval is proposed to solve the problem that the algorithm falls into local optimization because the selected individual optimal particles cannot guide the flight direction of particles effectively. That can improve the reliability of individual optimal particle selection, and accelerate the process of particle convergence. Finally, the fusion metric including particle convergence and diversity is used to maintain the external archive. It can avoid deleting the particles with good convergence and resulting in population degradation and affecting particle development capabilities, when external archive maintenance is just based on the particle density. Experimental results show that the proposed algorithm has good performance compared with some other multi-objective optimization algorithms. © 2022 Science Press. All rights reserved.