A global particle swarm optimization algorithm applied to electromagnetic design problem

Particle Swarm Optimization (PSO) is a stochastic search algorithm inspired from the natural behavior of insects and birds. Due to its few controlling parameters and easiness in implementations, PSO is very popular among other optimal algorithms. However, PSO is often trapped into local optima while solving high dimensional, complicated inverse and multimodal objective problems. To tackle this difficulty, an improved PSO, having an adaptive, dynamic and an improved parameter, is proposed. The adaptive and dynamic parameters will bring balance between the exploration and exploitation search abilities while the improved parameter controls the diversity of the population at the final stages of the search process. The experimental results demonstrate that the performance of the proposed PSO is better as compared to other well designed variants.