Grey Wolf Optimizer
Grey Wolf Optimizer
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DOI：
10.1016/j.advengsoft.2013.12.007
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发表时间：
2014-03-01
影响因子：
4.8
通讯作者：
Lewis, Andrew
中科院分区：
文献类型：
--
作者：
Mirjalili, Seyedali;Mirjalili, Seyed Mohammad;Lewis, Andrew
这项工作提出了一个新的元启发式称为灰狼优化（GWO）的启发灰狼（犬狼疮）。GWO算法模拟了自然界中灰狼的领导层级和狩猎机制。采用了阿尔法、贝塔、德尔塔和欧米茄四种类型的灰狼来模拟领导层级。此外，狩猎的三个主要步骤，寻找猎物，包围猎物，攻击猎物，都是实施的。在29个著名测试函数上对该算法进行了测试，并与粒子群算法、重力搜索算法、差分进化算法、进化规划算法和进化策略算法进行了比较。结果表明，GWO算法是能够提供非常有竞争力的结果相比，这些著名的元算法。本文还考虑解决三个经典的工程设计问题（拉/压弹簧，焊接梁，压力容器的设计），并提出了一个真实的应用所提出的方法在光学工程领域。经典工程设计问题和真实的应用结果表明，该算法适用于搜索空间未知的挑战性问题。(C)2013爱思唯尔有限公司保留所有权利。
This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces. (C) 2013 Elsevier Ltd. All rights reserved.