一种受集体动物行为启发的多峰函数优化算法
An optimization algorithm for multimodal functions inspired by collective animal behavior
摘要：
由于现实世界的优化问题往往需要在一个搜索空间内定位多个最优解，因此对多峰函数优化的兴趣正在迅速扩大。提出了一种新的多峰优化算法--集体动物行为算法（CAB）。动物群体，如鱼群、鸟群、蝗虫群和角马群，表现出各种各样的行为，包括围绕食物源群集，围绕中心位置旋转或在对齐的群体中进行长距离迁移。这些集体行为往往对群体有利，使它们能够提高收获效率，遵循更好的迁移路线，改善空气动力学并避免捕食。在该算法中，智能体是一组动物，它们根据集体运动的生物学定律相互作用。实验结果表明，与文献中报道的其他方法相比，该算法能够以更高的效率找到基准多峰优化问题的全局和局部最优解.
英文摘要：
Interest in multimodal function optimization is expanding rapidly since real world optimization problems often demand locating multiple optima within a search space. This article presents a new multimodal optimization algorithm named as the Collective Animal Behavior (CAB). Animal groups, such as schools of fish, flocks of birds, swarms of locusts and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic and to avoid predation. In the proposed algorithm, searcher agents are a group of animals which interact to each other based on the biological laws of collective motion. Experimental results demonstrate that the proposed algorithm is capable of finding global and local optima of benchmark multimodal optimization problems with a higher efficiency in comparison to other methods reported in the literature.
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