Applying particle swarm optimization in multiobjective optimization and hybrid optimization

Abstract

Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. PSO has gained widespread appeal amongst researchers and has been shown to offer good performance in a variety of application domains, with potential for hybridisation and specialisation. This paper presents a overview of the basic concepts of PSO according to continuous PSO and discrete PSO. The difference between single objective PSO and multiobjective PSO is presented. At the same time an implementation of PSO in multiobjective optimization is discussed. To overcome the limitations of PSO, hybrid optimization algorithms are proposed by many scholars. Several hybrid PSO approaches are presented in this paper.