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