Multi-Objective Particle Swarm Optimization Algorithm Based on Double Random

The number of populations in a multi-objective particle swarm optimization has a significant impact on the performance of the algorithm, so this paper explores whether the number of non-inferior solutions in the external archive also affects the performance of the algorithm. This paper proposes multi-objective particle swarm optimization algorithm based on double random(DRMOPSO). In this paper, the results are verified by experimental simulations and a relatively good interval of non-dominated solutions is obtained. According to this interval the double random strategy formed by grid technique and mixed ranking strategy is controlled within the interval of better non-dominated solutions to improve the probability of particles flying to the true Pareto front. The simulative experimental results show that the algorithm in this paper has better convergence and diversity compared with MOPSO.