Improved Particle Swarm Optimization By Means of Manipulation of the Inertia Weighting Factor Based on Albert Einstein Theory of Photoelectric Effect

Abstract

Particle Swarm Optimization (PSO) has been praised by many researchers in the field of Engineering and computer science since its introduction in 1995.This is due to its fast convergence and ability to reach optimal solutions during problem optimization. However, like any other Evolutionary algorithms it has its own drawbacks. PSO suffers premature convergence and getting stuck on local minima sometimes. This paper proposes an improved PSO based on the theory of photoelectric effect by Albert Einstein. The constrained and unconstrained benchmark functions have been used to validate the optimization performance of the proposed method. The statistical results showed that the proposed method is able to explore best solutions faster and effective during optimization for both constrained and unconstrained problems compared to the traditional method.