A Novel Opposition-Based Particle Swarm Optimization for Noisy Problems

Abstract

Particle swarm optimization (PSO) is a simple, reliable, and efficient optimization algorithm. However, it suffers from a weakness, losing the efficiency over optimization of noisy problems. In many real-word optimization problems we are faced with noisy environments. This paper presents a new algorithm to improve the efficiency of PSO to cope with noisy optimization problems. It employs opposition-based learning for swarm initialization, generation jumping, and also improving swarm's best member. A set of commonly used benchmark functions is employed for experimental verification, and the results show clearly the new algorithm outperforms PSO in terms of convergence speed and global search ability.