Hybrid metaheuristics for global optimization: A comparative study

Antoniya Georgieva*, Ivan Jordanov

*Corresponding author for this work

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


Several metaheuristic approaches for global optimization (GO) are investigated and their performances compared in this paper. We critically review and analyze recently proposed Stochastic Genetic Algorithm (StGA) for GO and compare it with our GO hybrid metaheuristic called Genetic LPτ and Simplex Search (GLPτS), which combines the effectiveness of Genetic Algorithms during the early stages of the search with the advantages of Low-Discrepancy sequences and the local improvement abilities of Simplex search. For comparison purposes we also use Fast Evolutionary Programming (FEP) and Differential Evolution (DE) methods. In parallel to our method, FEP and DE, we investigate further, re-run and test the StGA implementation on a number of multimodal mathematical functions. The obtained StGA results demonstrate inferior performance (compared with our GLPτS and DE methods), producing much worse than the reported in [1] results (with the only exception for the two-dimensional cases). We argue that the published in [1] accuracy and convergence speed results (given as number of function evaluations) are incorrect for most of the testing functions and investigate why the method is failing in those cases.

Original languageEnglish
Title of host publicationHybrid Artificial Intelligence Systems
Subtitle of host publicationThird International Workshop, HAIS 2008, Burgos, Spain, September 24-26, 2008, Proceedings
EditorsEmilio Corchado, Ajith Abraham, Witold Pedrycz
Number of pages8
ISBN (Electronic)9783540876564
ISBN (Print)9783540876557
Publication statusPublished - 1 Oct 2008
Event3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS 2008 - Burgos, Spain
Duration: 24 Sept 200826 Sept 2008

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS 2008


  • Evolutionary computation
  • Global optimization
  • Hybrid methods
  • Metaheuristics


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