Empirical analysis of a micro-evolutionary algorithm for numerical optimization

International Journal of Physical Sciences

Francisco Viveros-Jimenez¹ 

关键词: Optimization methods;    nature-inspired algorithms;    evolutionary computation;    swarm intelligence.;   

DOI  :  10.5897/IJPS11.303

学科分类：物理（综合）

来源: Academic Journals

【 摘 要 】

This paper presents an empirical comparison of some evolutionary algorithms to solve numerical optimization problems. The aim of the paper is to test a micro-evolutionary algorithm called Elitist evolution, originally designed to work with small populations, on a set of diverse test problems (unimodal, multimodal, separable, non-separable, shifted, and rotated) with different dimensionalities. The comparison covers micro-evolutionary algorithms based on differential evolution and particle swarm optimization. The number of successful runs, the quality of results and the computational cost, measured by the number of evaluations required to reach the vicinity of the global optimum, are used as performance criteria. Furthermore, a comparison against a state-of-the-art algorithm is presented. The obtained results suggest that the Elitist evolution is very competitive as compared with other algorithms, especially in high-dimensional search spaces.

【 授权许可】

CC BY