Hybrid evolutionary algorithm for solving global optimization problems

Radha Thangaraj, Millie Pant, Ajith Abraham, Youakim Badr

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationHybrid Artificial Intelligence Systems - 4th International Conference, HAIS 2009, Proceedings
Pages310-318
Number of pages9
DOIs
StatePublished - Nov 9 2009
Event4th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2009 - Salamanca, Spain
Duration: Jun 10 2009Jun 12 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5572 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2009
CountrySpain
CitySalamanca
Period6/10/096/12/09

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Hybrid evolutionary algorithm for solving global optimization problems'. Together they form a unique fingerprint.

Cite this