Multi-objective particle swarm optimization algorithm based on competition mechanism

Abstract

The performance of existing multi-objective particle swarm optimization algorithms largely depends on the global or individual optimal particles stored in the external archive. To simplify the process, a multi-objective particle swarm optimization algorithm with competition mechanism has been proposed. The algorithm maintains the diversity of the population through the max and min angle between the general individual and the more excellent individual. The performance of the proposed NMOPSO is verified by comparing with three advanced multi-objective algorithms. Experimental results show that the method has good performance in optimization quality and convergence speed.