Population distributions in biogeography-based optimization algorithms with elitism

  • Authors:

  • Dan Simon;Mehmet Ergezer;Dawei Du

  • Affiliations:

  • Cleveland State University, Department of Electrical and Computer Engineering, Cleveland, Ohio;Cleveland State University, Department of Electrical and Computer Engineering, Cleveland, Ohio;Cleveland State University, Department of Electrical and Computer Engineering, Cleveland, Ohio

  • Venue:
  • SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
  • Year:
  • 2009

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Abstract

Biogeography-based optimization (BBO) is an evolutionary algorithm that is based on the science of biogeography. Biogeography is the study of the geographical distribution of organisms. In BBO, problem solutions are represented as islands, and the sharing of features between solutions is represented as migration between islands. This paper develops a Markov analysis of BBO, including the option of elitism. Our analysis gives the probability of BBO convergence to each possible population distribution for a given problem. We compare our BBO Markov analysis with a similar genetic algorithm (GA) Markov analysis. Analytical comparisons on three simple problems show that with high mutation rates the performance of GAs and BBO is similar, but with low mutation rates BBO outperforms GAs. Our analysis also shows that elitism is not necessary for all problems, but for some problems it can significantly improve performance.