VaCSO: A Multi-objective Collaborative Competition Particle Swarm Algorithm Based on Vector Angles.

Recently, particle swarm algorithm (PSO) has demonstrated its effectiveness in solving multi-objective optimization problems (MOPs). However, due to rapid convergence, PSO has poor distribution when processing MOPs. To solve the above problems, we propose a multi-objective collaborative competition particle swarm algorithm based on vector angles (VaCSO). Firstly, in order to remove the influence of global or individual optimal particles, the competition mechanism is used. Secondly, in order to increase the diversity of solutions while maintaining the convergence, the population is clustered into two groups which use different learning strategies. Finally, a three-particle competition and co-evolution mechanism is proposed to improve the distribution and diversity of particle swarms. We set up comparative experiments to test the performance of VaCSO compared with the current popular multi-objective particle swarm algorithm. Experimental results show that VaCSO has excellent performance in convergence and distribution, and has a significant effect in optimizing quality.