期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 68, 期 -, 页码 63-80出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2017.10.024
关键词
Grey wolf optimizer; Exploration; Exploitation; High-dimensional numerical optimization; Global optimization
类别
资金
- National Natural Science Foundation of China [61463009, 61463046]
- Science and Technology Foundation of Guizhou Province [[2016]1022]
- Program for the Science and Technology Top Talents of Higher Learning Institutions of Guizhou [KY[2017]070]
- Guizhou University of Finance and Economics [2016SWBZD13]
- Ministry of Commerce [2016SWBZD13]
- Education Department of Guizhou Province [KY[2017]004]
- Central Support Local Projects [PXM 2013-014210-000173]
- Natural Science Foundation of Hunan Province [2015JJ3005]
Grey wolf optimizer (GWO) algorithm is a relatively novel population-based optimization technique that has the advantage of less control parameters, strong global optimization ability and easy of implementation. It has received significant interest from researchers in different fields. However, there is still an insufficiency in the GWO algorithm regarding its position-updated equation, which is good at exploitation but poor at exploration. In this work, we proposed an improved algorithm called the exploration-enhanced GWO (EEGWO) algorithm. In order to improve the exploration, a new position-updated equation is presented by applying a random individual in the population to guide the search of new candidate individuals. In addition, in order to make full use of and balance the exploration and exploitation of the GWO algorithm, we introduced a nonlinear control parameter strategy, i.e., the control parameter of (a) over right arrow is nonlinearly increased over the course of iterations. The experimental result on a set of 23 benchmark functions and 4 engineering applications demonstrate the effectiveness and efficiency of the modified position-updated equation and the nonlinear control parameter strategy. The comparisons show that the proposed EEGWO algorithm significantly improves the performance of GWO. Moreover, EEGWO offers the highest solution quality, strongest robustness, and fastest global convergence among all of the contenders on almost all of the test functions. (C) 2017 Elsevier Ltd. All rights reserved.
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