Unconstrained global optimization: A benchmark comparison of population-based algorithms

摘要

In this paper we provide a systematic comparison of the following population-based optimization techniques: Genetic Algorithm (GA), Evolution Strategy (ES), Cuckoo Search (CS), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The considered techniques have been implemented and evaluated on a set of 67 multivariate functions. We carefully selected the tested optimization functions which have different features and gave exactly the same number of objective function evaluations for all of the algorithms. This study has revealed that the DE algorithm is preferable in the majority of cases of the tested functions. The results of numerical evaluations and parameter optimization are presented in this paper.