Particle Swarm Optimization with social exclusion and its application in electromagnetics

Abstract

The behavior of Particle Swarm Optimization (PSO), a population based optimization algorithm, depends on the movements of the particles and the attractions among them. This behavior was extracted from the observations of the swarms in nature. Every swarm desires to remain powerful in order to survive in nature and to protect its descendants. Therefore, the weakest members in the swarm are isolated, and generally abandoned to live on their own resources. This act is known as social exclusion. In this research, this phenomenon is incorporated to PSO. At the early phase of time-line, the swarm is divided into two groups based on their cost/fitness values. Each group proceeds their own journey without the knowledge of other group. This new algorithm is named as Social Exclusion-PSO (SEPSO). First, the performance of this new algorithm was evaluated/compared with an inertia weight PSO via unimodal, multimodal, expended benchmark functions, and then, it is applied to the circular antenna array design problem. For each implementation, the performance of two sub-populations and the undivided population are presented to demonstrate and compare the behaviour of the socially excluded swarm. The results show that excluding the members with the worst cost values from the population increases the performance of the algorithm in terms of global best solution with approximately 20% smaller number of function evaluations.