Analysis of stagnation behavior of vector evaluated particle swarm optimization

Abstract

The vector evaluated particle swarm optimization (VEPSO) algorithm is a cooperative, multi-swarm algorithm. Each sub-swarm optimizes only a single objective of a multi-objective problem (MOP), and implements a knowledge transfer strategy (KTS) to share optimal positions of the different objectives among the sub-swarms, guiding the particles to different regions of the Pareto front. This paper shows that the stagnation problem that occurs in VEPSO can be addressed by using a different KTS. A comparison is made between the ring-based and random knowledge transfer strategies. Experimental results show that the random knowledge transfer strategy suffers less from stagnation than the ring-based KTS, making it the preferred KTS to use.