蝙蝠算法是受自然界中的蝙蝠通过回声定位进行搜寻、捕食猎物行为的启发,并将多智能体系统与进化机制相结合发展而来的优化方法。作为一种新颖的仿生群体智能优化算法,分析了蝙蝠算法的仿生原理、优化机理及特点,对算法优化过程进行了定义。通过标准算例对蝙蝠算法在连续空间和离散空间的优化性能进行了仿真测试,结果表明该算法在函数优化和组合优化方面应用的可行性和有效性,具有良好的应用前景。
Inspired by the echolocation behavior of bats and combined multi-agent system with evolution mechanism, bat algo- rithm (BA) was developed as a novel bionic swarm intelligence optimization method. This paper analyzed the bionic principle and trait of BA as well as defined the mechanism of optimization by formulation. It tested the BA by benchmarks of continuous space optimization and discrete space optimization, simulation results show that the new bin-inspired algorithm has better feasi- bilities and validities for function optimization and combinatorial optimization.