An Improved Biogeography-based Optimization with Hybrid Migration and Feedback Differential Evolution and its Performance Analysis

Abstract

Biogeography-based optimization (BBO) is not suitable for solving high-dimensional or multi-modal problems. To improve the optimization efficiency of BBO, this study proposes a novel BBO variant, which is named ZGBBO. For the selection operator, an example learning method is designed to ensure inferior solution will not destroy the superior solution. For the migration opeartor, a convex migration is proposed to increase the convergence speed, and the probability of finding the optimal solution is increased by using opposition-based learning to generate opposite individuals. The mutation operator of BBO is deleted to eliminate the generation of poor solutions. A differential evolution with feedback mechanism is merged to improve the convergence accuracy of the algorithm for multi-modal and irregular problems. Meanwhile, the greedy selection is used to make the population always moves in the direction of a better area. Then, the global convergence of ZGBBO is proved with Markov model and sequence convergence model. Quantitative evaluations, compared with three self-variants, seven improved BBO variants and six state-of-the-art evolutionary algorithms, experimental results on 24 benchmark functions show that every improved strategy is indispensable, and the overall performance of ZGBBO is better. Besides, the complexity of ZGBBO is analyzed by comparing with BBO, and ZGBBO has less computation and lower complexity.