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Evolutionary Multimodal Optimization

In: Optimization Methods and Applications

Author

Listed:
  • Mykola M. Glybovets

    (National University of Kyiv-Mohyla Academy)

  • Nataliya M. Gulayeva

    (National University of Kyiv-Mohyla Academy)

Abstract

In this chapter, a comprehensive review of niching genetic algorithms designed to solve multimodal optimization problems is given. First, an introduction to multimodal optimization problem and to niching is provided. After that, a number of niching algorithms are discussed. These algorithms are presented according to their spatial-temporal classification, although other classifications are also mentioned. Methods analyzed in detail among others include sequential niching, fitness sharing, clearing, multinational genetic algorithm, clustering, species conserving genetic algorithm, crowding (standard, deterministic, probabilistic, multi-niche), restricted tournament selection, and others. Most methods are followed by their numerous modifications. The efficiency of hybridization of different algorithms is discussed, and examples of such hybridization are provided. Experimental approach to analyze performance of niching algorithms is described. To estimate the ability of the algorithms in finding and maintaining multiple optima, most popular test criteria and benchmark problems are given.

Suggested Citation

  • Mykola M. Glybovets & Nataliya M. Gulayeva, 2017. "Evolutionary Multimodal Optimization," Springer Optimization and Its Applications, in: Sergiy Butenko & Panos M. Pardalos & Volodymyr Shylo (ed.), Optimization Methods and Applications, pages 137-181, Springer.
    • Handle: RePEc:spr:spochp:978-3-319-68640-0_8
    DOI: 10.1007/978-3-319-68640-0_8
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