A Novel Hybrid Particle Swarm Optimization for Multi-Objective Problems

To solve the multi-objective problems, a novel hybrid particle swarm optimization algorithm is proposed(called HPSODE). The new algorithm includes three major improvement: (I)Population initialization is constructed by statistical method Uniform Design, (II)Regeneration method has two phases: the first phase is particles updated by adaptive PSO model with constriction factor χ, the second phase is Differential Evolution operator with archive, (III)A new accept rule called Distance/volume fitness is designed to update archive. Experiment on ZDTx and DTLZx problems by jMetal 2.1, the results show that the new hybrid algorithm significant outperforms OMOPSO, SMPSO in terms of additive Epsilon, HyperVolume, Genetic Distance, Inverted Genetic Distance.