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A Hybrid Evolutionary Algorithm for Global Optimization

In: Optimization and Optimal Control

Author

Listed:
  • Mend-Amar Majig

    (Kyoto University)

  • Abdel-Rahman Hedar

    (Kyoto University)

  • Masao Fukushima

    (Kyoto University)

Abstract

Summary. In this work, we propose a method for finding as many as possible, hopefully all, solutions of the global optimization problem. For this purpose, we hybridize an evolutionary search algorithm with a fitness function modification procedure. Moreover, to make the method more effective, we employ some local search method and a special procedure to detect unpromising trial solutions. Numerical results for some well-known global optimization test problems show the method works well in practice.

Suggested Citation

  • Mend-Amar Majig & Abdel-Rahman Hedar & Masao Fukushima, 2010. "A Hybrid Evolutionary Algorithm for Global Optimization," Springer Optimization and Its Applications, in: Altannar Chinchuluun & Panos M. Pardalos & Rentsen Enkhbat & Ider Tseveendorj (ed.), Optimization and Optimal Control, pages 169-184, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-89496-6_9
    DOI: 10.1007/978-0-387-89496-6_9
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