Vector-evaluated particle swarm optimization with local search

Abstract

Many real-world optimization problems contain multiple goals to be optimized concurrently. Vector-evaluated particle swarm optimization is a particle swarm optimization variant which employs multiple swarms to solve multi-objective optimization problems. Each swarm optimizes a single objective and information regarding current best positions is passed among swarms using a knowledge transfer strategy. This paper investigates the application of a local search technique to the vector-evaluated particle swarm optimization algorithm. A hill climbing algorithm is applied to non-dominated solutions, dominated solutions, swarm personal best positions and swarm global best positions. Performance of each local search strategy is compared with the standard vector-evaluated particle swarm optimization algorithm using various knowledge transfer strategies. The results indicate that three out of the four local search techniques significantly improved performance of the vector-evaluated particle swarm optimization algorithm for problems possessing two objectives. No significant performance improvement was found for three-objective problems.