一种基于权重策略的蝙蝠算法

A bat algorithm based on weighted strategy

针对基本蝙蝠算法存在的后期收敛速度慢、易陷入局部极值、稳定性差等缺点,提出一种基于权重策略的蝙蝠算法.该算法在蝙蝠学习机制中引入权重策略,使其不再单一地向全局最优蝙蝠学习,而是与邻域内所有蝙蝠进行信息共享与交流,并根据自身寻优能力自适应地调节向其他蝙蝠学习的力度,优化迭代种群,增加种群多样性,有效地提高了算法的全局搜索能力和搜索精度.数值测试结果表明,新算法有较快收敛速度和较高的寻优精度.

In view of the shortcomings of the bat algorithm,such as slow convergence rate,easy to fall into local extremum and poor stability,a bat algorithm based on weighted strategy is presented.The weighted strategy is introduced in the bats learning mechanism for no longer learning from the global optimal bat,but sharing and exchanging information with all the bats in the neighborhood,and adaptively adjusts the force of learning from other bats to optimize the iterative population,increase the diversity of the population,and effectively improve the global search ability and search precision of the algorithm.The numerical results show that the new algorithm has faster convergence speed and higher optimization accuracy.