基于微分进化的混合生物地理学约束优化算法

摘要

Basic biogeography based optimization (BBO) can be easily trapped into local optima .To modify the defect ,a hybrid biogeography based optimization with differential evolution (BBO‐DE) was proposed ,which combined the exploitation ability of BBO and the exploration ability of differential evolution (DE) reasonably to balance the exploitation ability and exploration abili‐ty .In addition ,feasibility‐based constraint handling mechanism was introduced into BBO‐DE ,which extended traditional BBO to solve constrained optimization problem .The proposed BBO‐DE was performed on eight selected benchmark functions .Simula‐tion results demonstrate that it is a feasible and effective method for constrained optimization .With respect to basic BBO and DE , BBO‐DE has distinct superiority in terms of the quality of final solutions and the convergence speed .%针对生物地理学优化算法（biogeography based optimization ，BBO）容易陷入局部最优解的缺点，提出一种基于微分进化（differential evolution ，DE）改进BBO算法的混合生物地理学（BBO‐DE）优化算法。通过有机结合BBO算法的利用能力和DE算法的搜索能力，实现利用能力与搜索能力的平衡；引入基于可行性的约束处理机制，解决传统BBO算法无法求解约束优化的问题。通过选定的8个标准测试函数对改进算法进行仿真测试，测试结果验证了改进算法的可行性和有效性，与基本BBO和DE算法相比，其在最终解的质量和收敛速度上具有明显优势。