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中文摘要：
蝙蝠算法是受自然界中的蝙蝠通过回声定位进行搜寻、捕食行为的启发演变而来的一种新颖的仿生群智能优化算法。在分析基本算法仿生原理和局限性的基础上,提出一种改进蝙蝠局部搜索能力的优化算法,通过逻辑自映射函数产生混沌序列,引入到蝙蝠算法中对精英个体进行混沌优化,同时动态收缩搜索空间以加快收敛速度。改进算法有效结合了基本蝙蝠算法的全局优化能力和混沌算法的局部搜索能力,对经典函数的仿真测试表明,改进算法显著提高了优化性能,在寻优精度和全局收敛能力方面优于基本蝙蝠算法,是解决工程应用中复杂函数优化问题的一种有效方法。
英文摘要：
Inspired by the echolocation behavior of bats,bat algorithm（BA） is developed as a novel bionic swarm intelligence optimization method.An improved bat algorithm was proposed to enhance the local searching ability on the basis of analyzing bionic principle and limitations of basic BA.A series of chaotic variables according to the self-logical mapping function were introduced into the course of BA to optimize the elite of artificial bats,and shrink the search field dynamically.The improved algorithm makes use of the chaotic search to improve the capability of precise search and also keeps the ability of global search of basic BA.Simulation results for benchmark functions show that the proposed algorithm has improved the global optimizing ability remarkably,and has great advantage of accuracy and convergence property compared to BA and PSO.
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