Wolf Pack Algorithm for Unconstrained Global Optimization
Wolf Pack Algorithm for Unconstrained Global Optimization
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DOI：
10.1155/2014/465082
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发表时间：
2014-03
影响因子：
--
通讯作者：
Husheng Wu;Fengming Zhang
中科院分区：
文献类型：
--
作者：
Husheng Wu;Fengming Zhang
狼群团结协作，在青藏高原猎杀猎物，展现了绝妙的技能和惊人的谋略。受猎物行为和分布模式的启发，我们抽象出侦察、呼唤和围攻三种智能行为，以及领头狼的赢家通吃生成规则和狼群的强生存更新规则两种智能规则。在此基础上，提出了一种新的启发式群体智能算法--狼群算法。对一组具有不同特征、单峰/多峰、可分离/不可分离的基准函数进行了实验，讨论了几种距离测量和参数对WPA的影响。通过与遗传算法、粒子群优化算法、人工鱼群算法、人工蜂群算法、萤火虫算法等五种典型智能算法的对比仿真实验表明，WPA算法具有更好的收敛性能和鲁棒性，特别是对于高维函数。
The wolf pack unites and cooperates closely to hunt for the prey in the Tibetan Plateau, which shows wonderful skills and amazing strategies. Inspired by their prey hunting behaviors and distribution mode, we abstracted three intelligent behaviors, scouting, calling, and besieging, and two intelligent rules, winner-take-all generation rule of lead wolf and stronger-survive renewing rule of wolf pack. Then we proposed a new heuristic swarm intelligent method, named wolf pack algorithm (WPA). Experiments are conducted on a suit of benchmark functions with different characteristics, unimodal/multimodal, separable/nonseparable, and the impact of several distance measurements and parameters on WPA is discussed. What is more, the compared simulation experiments with other five typical intelligent algorithms, genetic algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, and firefly algorithm, show that WPA has better convergence and robustness, especially for high-dimensional functions.