A comparative study of different metaheuristic optimization algorithms using standard test functions

Abstract

The role of metaheuristic optimization algorithms in the analysis of real world optimization problems is significantly increasing against traditional optimization methods. But these algorithms possesses the limitation that they are highly problem dependent. The selection of an optimization algorithm for a specific application can be validated using standard test functions. A comparative study of three metaheuristic algorithms, Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm using standard test functions is presented in this paper. Standard test functions which are very much similar to real world optimization problems are used for the performance comparison of optimization algorithms.