关键词: 生物地理学优化算法, 邻域选择, 引力学习, 自适应高斯变异机制

Abstract: Biogeography-Based Optimization（BBO） easily suffers from the premature convergence and local optima trapping problems. In order to solve these issues, a new algorithm, named Neighbor Force Learning Biogeography-Based Optimization（NFBBO）, is proposed in this paper. NFBBO presents a neighbor selection strategy, in which an emigrating solution is selected from the neighbors of the immigrating solution based on its suitability and distance. This operation can exploit the neighborhood information of swarm and improve the population diversity. Then, a force learning strategy is integrated with the migration operator to update the immigrating solutions. This strategy can expand the solutions search space and enhance the searching ability of BBO to avoid the prematurity. Furthermore, in order to escape from the local optima, an adaptive Gaussian mutation mechanism is further introduced, which is an effective jump-out mechanism. Experimental study is conducted on 10 well-known high-dimensional benchmark functions. The experimental results indicate that NFBBO has better search performance compared with other competing algorithms in terms of the convergence rate and the quality of the final solutions.

Key words: Biogeography-Based Optimization（BBO）, neighbor selection strategy, force learning, adaptive Gaussian mutation mechanism