基于混合迁移策略的生物地理学优化算法

Biogeography-Based Optimization Based on Hybrid Migration Strategy

为提高生物地理学优化算法(BBO)的性能,提出一种基于混合迁移策略的生物地理学优化算法(HMB-BO).该算法通过动态选取待迁出种群个体,平衡对解集搜索过程中的选择压力.采用混合迁移策略改进迁移机制,增强算法对解的搜索能力,避免引起过早收敛.并加入分段Logistic混沌机制对个体进行变异,提高算法的收敛精度.基于标准测试函数的仿真实验表明,HMBBO算法可有效避免早熟收敛,在收敛速度和收敛精度上较标准BBO算法有较大提高.

To improve the performance of biogeography-based optimization （BBO）, a biogeography-based optimization algorithm with hybrid migration strategy （HMBBO） is proposed. In this algorithm, the emigrated individuals are dynamically selected to balance the selection pressure for the solution set searching. A migration mechanism based on hybrid migration strategy is applied to enhance search capability and avoid premature convergence. The chaotic mutation mechanism is applied to improve the convergence precision for individuals. The experimental results on benchmark functions show that the HMBBO algorithm effectively avoids the premature convergence and improves convergence property and robustness compared to BBO algorithm.