Multi-objective Particle Swarm Optimization Algorithm with Multiple Strategies
WANG Yan
WANG Xia
WANG Han
Abstract：In order to overcome the shortcoming of particle swarm optimization(PSO)that it is easy to fall into local optimal when dealing with multi-objective optimization problems,and improve the solving stability of the algorithm,a multi-strategy multi-objective particle swarm optimization algorithm(MOPSOMS)is proposed.Firstly,a phased selection strategy of global opti-mal particle based on dominant number is proposed to make the population approach the real Pareto optimal solution faster.Com-bined with adaptive grid technology and roulette strategy,the algorithm can take into account the diversity better.Secondly,the multi-objective Fibonacci mutation strategy and the dynamic population position average strategy are proposed.The mutation is car-ried out according to the Fibonacci principle in a single randomly selected dimension,and the dynamic population position average is used to replace the individual optimal particle to improve the diversity of solution and avoid the algorithm falling into the local opti-mal.The proposed MOPSOMS algorithm is compared with 8 multi-objective optimization algorithms on the ZDT test function set.The experimental results show that MOPSOMS algorithm has better solution stability,and the obtained solution set has better conver-gence and distribution.
Keywords：multi-objective optimizationparticle swarm optimization algorithmdomination numbermulti-objective Fibo-naccidynamic population position average
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：8( 2987-2994 )