蝙蝠算法优于间歇搜索策略
Bat Algorithm is Better Than Intermittent Search Strategy
摘要：
任何一种元启发式算法的效率在很大程度上取决于如何平衡局部密集开发和全局多样性开发。研究表明，蝙蝠算法可以提供一个很好的平衡这两个关键组成部分与优越的效率。在本文中，我们首先回顾了一些常用的元启发式算法，然后比较蝙蝠算法的性能与所谓的间歇搜索策略。通过仿真实验，我们发现蝙蝠算法优于最优间歇搜索策略。我们还分析了比较结果及其对高维最优化问题的影响。此外，我们还将bat算法应用于商业优化和工程设计问题的求解。
英文摘要：
The efficiency of any metaheuristic algorithm largely depends on the way of balancing local intensive exploitation and global diverse exploration. Studies show that bat algorithm can provide a good balance between these two key components with superior efficiency. In this paper, we first review some commonly used metaheuristic algorithms, and then compare the performance of bat algorithm with the so-called intermittent search strategy. From simulations, we found that bat algorithm is better than the optimal intermittent search strategy. We also analyse the comparison results and their implications for higher dimensional optimization problems. In addition, we also apply bat algorithm in solving business optimization and engineering design problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
