基于追尾行为的改进型人工萤火虫群算法

Improved Glowworm Swarm Optimization Based on the Behavior of Follow

利用人工鱼群算法的追尾思想并在过程中加入拥挤度因子,对人工萤火虫群算法进行了改进,提出了一种改进型人工萤火虫群算法,并将该算法用于多峰函数的优化问题。通过实验仿真及与其他算法进行的对比分析表明,改进后的人工萤火虫群算法在种群规模较小、迭代次数较少的情况下也可以精确捕获函数定义域内的所有峰值。

To improve the glowworm swarm optimization,the behavior of follow of the artificial fish school algorithm and a swarm degree were used.The improved algorithm was used to optimise multi-modal functions.The experiment results show that the algorithm,with the smaller populations and the fewer number of iterations,can simultaneous capture multiple optima of several standard multimodal test function.