Grey Wolf Optimizer With a Novel Weighted Distance for Global Optimization

被引：25
作者：
Yan, Fu [1 ]
Xu, Xinliang [2 ]
Xu, Jianzhong [1 ]
机构：
[1] Harbin Engn Univ, Sch Econ & Management, Harbin 150001, Peoples R China
[2] Northeast Agr Univ, Coll Econ & Management, Harbin 150030, Peoples R China
来源：
IEEE ACCESS | 2020年 / 8卷
基金：
中国国家自然科学基金;
关键词：
Grey wolf optimizer; global optimization; weight distance strategy; elimination and repositioning strategy; engineering design problem; PARTICLE SWARM OPTIMIZATION; DESIGN; ALGORITHM; EVOLUTIONARY; GSA;
D O I：
10.1109/ACCESS.2020.3005182
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a new grey wolf optimizer (GWO) variant based on a novel weighted distance (WD) called the GWO-WD algorithm is presented to solve global optimization problems. First, a modified position-updating equation formulated using the proposed strategy is employed to obtain additional information and improved global solutions. Then, several of the worst individuals are eliminated and repositioned using an elimination and repositioning strategy to improve the capability of the algorithm and avoid falling into local optima. The performance of the algorithm is verified by utilizing 23 widely used benchmark test functions, the IEEE CEC2014 test suite and three well-known engineering design problems. The simulation results of the proposed algorithm are compared with those of the standard GWO algorithm, three GWO variants and several existing methods, and the proposed algorithm is revealed to be very competitive and, in many cases, superior.
引用
收藏
页码：120173 / 120197
页数：25
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