Chapter

Simulated Evolution and Learning

Volume 5361 of the series Lecture Notes in Computer Science pp 180-189

An Island Based Hybrid Evolutionary Algorithm for Optimization

  • Changhe LiAffiliated withSchool of Computer Science and information Technology, RMIT UniversityDepartment of Computer Science, University of Leicester
  • , Shengxiang YangAffiliated withSchool of Computer Science and information Technology, RMIT UniversityDepartment of Computer Science, University of Leicester

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Abstract

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.