StPSO: Strengthened particle swarm optimization

Abstract

In this paper, we present a novel approach to strengthen Particle Swarm Optimization (PSO). PSO is a population-based metaheuristic that takes advantage of individual memory and social cooperation in a swarm. It has been applied to a variety of optimization problems because of its simplicity and fast convergence. However, straightforward application of PSO suffers from premature convergence and lack of intensification around the local best locations. To rectify these problems, we modify update procedure for the best particle in the swarm and propose a simple and random moving strategy. We perform a Reduced Variable Neighborhood Search (RVNS) based local search around the particle, as well. The resulting strengthened PSO (StPSO) algorithm not only has superior exploration and exploitation mechanisms but also provides a dynamical balance between them. Experimental analysis of StPSO is performed on continuous function optimization problems and a discrete problem, Orienteering Problem. Its performance is quite robust and consistent for all problem types; discrete or continuous, unimodal or multimodal. StPSO either reproduces the best known solution or provides a competitive solution for each problem instance. So, it is a valuable tool producing promising solutions for all problem types.