Hybrid Evolutionary Algorithm for Solving Global Optimization Problems

Authors:
Radha Thangaraj;Millie Pant;Ajith Abraham;Youakim Badr
Affiliations:
Department of Paper Technology, IIT Roorkee, India;Department of Paper Technology, IIT Roorkee, India;National Institute of Applied Sciences of Lyon, INSA-Lyon, Villeurbanne, France;National Institute of Applied Sciences of Lyon, INSA-Lyon, Villeurbanne, France
Venue:
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Year:
2009

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Abstract

Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. This paper presents a simple and modified hybridized Differential Evolution algorithm for solving global optimization problems. The proposed algorithm is a hybrid of Differential Evolution (DE) and Evolutionary Programming (EP). Based on the generation of initial population, three versions are proposed. Besides using the uniform distribution (U-MDE), the Gaussian distribution (G-MDE) and Sobol sequence (S-MDE) are also used for generating the initial population. Empirical results show that the proposed versions are quite competent for solving the considered test functions.