An Island Based Hybrid Evolutionary Algorithm for Optimization

  • Changhe Li
  • Shengxiang Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)


Evolutionary computation has become an important problem solving methodology among the set of search and optimization techniques. Recently, more and more different evolutionary techniques have been developed, especially hybrid evolutionary algorithms. This paper proposes an island based hybrid evolutionary algorithm (IHEA) for optimization, which is based on Particle swarm optimization (PSO), Fast Evolutionary Programming (FEP), and Estimation of Distribution Algorithm (EDA). Within IHEA, an island model is designed to cooperatively search for the global optima in search space. By combining the strengths of the three component algorithms, IHEA greatly improves the optimization performance of the three basic algorithms. Experimental results demonstrate that IHEA outperforms all the three component algorithms on the test problems.


Particle Swarm Optimization Component Algorithm Hybrid Evolutionary Algorithm Free Lunch Theorem Global Search Capability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Changhe Li
    • 1
  • Shengxiang Yang
    • 1
  1. 1.Department of Computer ScienceUniversity of LeicesterLeicesterUK

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