Design of Migration Operators for Biogeography-Based Optimization and Markov Analysis

Biogeography-Based Optimization (BBO) inspired from the science of biogeography is a population-based evolutionary algorithm and has been developed both in theory and practice. As a crucial operator, migration operator in BBO plays a role of sharing features in solutions, which significantly effects BBO’s performance. Motivated by the crossover operators in Genetic Algorithm, three kinds of migration operators with non-uniform and uniform versions are proposed in this paper, which could enhance BBO’s performance. In addition, we present a Markov analysis as a mathematical proof to illustrate that the proposed migration operators are superior to the original migration operator in searching. A set of fourteen benchmarks is employed in numerical simulations to test proposed migration operators and the results validate their feasibility and effectiveness. In particular, the proposed migration operators with uniform versions are more adaptive in optimization and could achieve good performances in simulations. A 3-b one-max problem is used to test the Markov modeling and the results are in agreement with our analysis.

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