摘要：

针对单一种群算法较难权衡全局寻优能力和局部搜索能力的问题,提出一种种间双系统协作蝙蝠优化算法。根据蝙蝠个体运动状态将整个种群分为探测系统和开发系统,并通过信息交流进行进化协作。设计和运用动态变化算子实现全局寻优和局部寻优的实时平衡,利用位置更新算子减少开发系统随机性带来的影响,提高局部区域的开发效率,运用伪变异算子保持探测系统的多样性,提高全局搜索的效率。实验结果表明,该算法在快速收敛的同时能够避免陷入局部最优,可解决多局部极值的复杂优化问题。

关键词: 双系统, 蝙蝠算法, 爬山思想, 伪变异算子, 寻优精度

Abstract:

Aiming at the problem that it is difficult to balance the global optimization ability and local search ability for a single population algorithm,an inter-species dual-system cooperative bat optimization algorithm is proposed.The whole population is divided into detection system and development system,they evolves and coordinates through information exchange.In the algorithm,the dynamic change operator is designed and applied to realize the real-time balance between global optimization and local optimization.The position update operator is gived to reduce the impact of the randomness of the development system to improve the development efficiency of the local area.The pseudo mutation operator is designed to child maintain the diversity of the detection system to improve the efficiency of the global search.Experimental results show that the algorithm can avoid the local optimization while fast convergence,and can be used to solve the complex optimization problem of multi-local extremum.

Key words: dual-system, Bat Algorithm(BA), hill-climbing thought, pseudo mutation operator, optimizing accuracy

中图分类号: 

• TP18