A Competitive Particle Swarm Algorithm Based on Vector Angles for Multi-Objective Optimization

Recently, the particle swarm algorithm (PSO) has demonstrated its effectiveness in solving multi-objective optimization problems. However, the performance of most existing multi-objective particle swarm algorithms depends largely on the global or individual best particles. Moreover, due to the rapid convergence of PSO in single objective optimization problems, PSO is prone to poorly distributed indicators when dealing with multi-objective optimization problems. To solve the above problems, we propose a multi-objective competitive particle swarm algorithm based on vector angles (VaCSO). Firstly, in order to remove the influence of global best particles or individual best particles on the algorithm, the competition mechanism is used. Secondly, in order to increase the diversity of solutions while maintaining the convergence of the algorithm, the population is clustered into two populations. Population 1 mainly considers the convergence of the solution in the offspring generation strategy. As a supplement, population 2 adds a new offspring generation strategy to maintain the distribution of the solution, and we innovatively proposed a three-particle competition to improve the distribution and diversity of particle swarms. Finally, based on vector angle information, we consider auxiliary learning to optimize the population gap, so as to improve the distribution of the algorithm. We have established two sets of comparative experiments to test the performance of VaCSO. We compared VaCSO with the currently popular multi-objective particle swarm optimizers and multi-objective evolutionary algorithms. Experimental results show that VaCSO has an excellent performance in convergence and distribution, and has a significant effect in optimizing quality.