Hybrid algorithm based on biogeography-based Optimization and differential evolution for global optimization

Abstract

Biogeography-based Optimization(BBO) is a new biogeography inspired optimization algorithm, and it searches for global optimum through two operators: migration and mutation. To alleviate the slow convergence and premature problem of the BBO, a hybrid optimization algorithm based on BBO and differential evolution(DE) has been presented in this paper. In the given hybrid algorithm new habitats in ecosystem are generated through a hybrid migration operator, i.e. BBO migration strategy and DE/best/1 differential strategy, to overcome stagnation phenomenon at the later evolution stage. In additional, Gaussian mutation operator is introduced to improve the diversity of the population and enhance the exploration ability. The experimental results show that this new algorithm not only improves the global optimization performance, but also quickens the convergence speed and obtains robust results with good quality, which indicates this new algorithm is an effective approach for solving global optimization problems.