Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization
Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization
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DOI：
10.1016/j.eswa.2015.10.039
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发表时间：
2016-04-01
影响因子：
8.5
通讯作者：
Coelho, Leandro dos S.
中科院分区：
文献类型：
--
作者：
Mirjalili, Seyedali;Saremi, Shahrzad;Coelho, Leandro dos S.
由于灰太狼优化器（GWO）的新奇，在文献中没有研究设计一个多目标版本的这种算法。本文首次提出了一种多目标灰狼优化器（MOGWO），用于解决多目标优化问题。一个固定大小的外部档案集成到GWO保存和检索帕累托最优解。这个档案，然后定义的社会阶层和模拟的狩猎行为的灰狼在多目标搜索空间。在10个多目标基准问题上对该方法进行了测试，并与基于分解的多目标进化算法（MOEA/D）和多目标粒子群优化算法（MOPSO）进行了比较。定性和定量的结果表明，该算法是能够提供非常有竞争力的结果，优于其他算法。请注意，MOGWO的源代码可在http://www.alimirjalili.com/GWO.html上公开获得。(C)2015爱思唯尔有限公司版权所有。
Due to the novelty of the Grey Wolf Optimizer (GWO), there is no study in the literature to design a multi objective version of this algorithm. This paper proposes a Multi-Objective Grey Wolf Optimizer (MOGWO) in order to optimize problems with multiple objectives for the first time. A fixed-sized external archive is integrated to the GWO for saving and retrieving the Pareto optimal solutions. This archive is then employed to define the social hierarchy and simulate the hunting behavior of grey wolves in multi-objective search spaces. The proposed method is tested on 10 multi-objective benchmark problems and compared with two well-known meta-heuristics: Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Multi-Objective Particle Swarm Optimization (MOPSO). The qualitative and quantitative results show that the proposed algorithm is able to provide very competitive results and outperforms other algorithms. Note that the source codes of MOGWO are publicly available at http://www.alimirjalili.com/GWO.html. (C) 2015 Elsevier Ltd. All rights reserved.