1. College of Artificial Intelligence, Key Lab of Intelligent Data Information Processing & Control of Hebei Province,
Tangshan University, Tangshan, Hebei 063000, China
2. Yanshan University, Qinhuangdao, Hebei 066004, China
3. Kailuan General Hospital, Tangshan, Hebei 063000, China
4. Tangshan Normal University, Tangshan, Hebei 063000, China
Abstract To solve the problem that the particle swarm optimization algorithm is easy to fall into the local optimum.A multi-objective optimization algorithm based on the combination of particle swarm optimization and clustering method is proposed.The algorithm is based on the method of reference vector decomposition, and the global optimal solution is updated through the clustering optimization particle strategy.First, the particles are clustered by each uniformly distributed reference vector to promote the diversity of particles. A particle with the smallest aggregation function fitness value is selected from each cluster in order to balance convergence and diversity.The global optimal solution and the individual optimal solution are dynamically updated, and the population is guided to be evenly distributed near the Pareto front.It is compared with the four particle swarm multi-objective optimization algorithms through simulation experiments.Experimental results show that the proposed algorithm obtains 20 IGD optimal values on 27 selected benchmark problems.
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2023, Vol. 44 
