Multi-objective Particle Swarm Optimization Based on Dual Global Best Solution
LI Shi
SHI Yan
CAI Chao
Abstract：In order to improve the optimization performance of the multi-objective particle swarm optimization algorithm,a new multi-objective particle swarm optimization algorithm based on dual global best solution is proposed.Firstly,a dual global best solution adaptive selection strategy is introduced to balance the global search and local search capabilities of the algorithm.Second-ly,in order to flexibly adjust the evolution direction of the population,the convergence contribution of non-dominated solutions is proposed to detect the variations of evolutionary environment in optimization process.At the same time,the fusion ranking index is used to select Personal best solution,which can effectively guide the flight of the particles and prevent the algorithm from falling into a local optimum.Finally,the special crowding distance is used to maintain the diversity of non-dominated solutions and the external archives.The experimental results demonstrate that the proposed algorithm has better convergence,diversity and distribution,and has better overall performance than other five comparison algorithms.
Keywords：multi-objective optimizationmulti-objective particle swarm optimizationPareto solutionscrowding distance
Publication Date：2025-09-20
Online Publishing Date：2025-12-23(First online date of this platform, not the publication date of the document)
Pages：10( 2377-2385,2408 )