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A Dynamic Opposite Learning-Assisted Grey Wolf Optimizer
The grey wolf optimization (GWO) algorithm is widely utilized in many global optimization applications. In this paper, a dynamic opposite learning-assisted grey wolf optimizer (DOLGWO) was proposed to improve the search ability. Herein, a dynamic opposite learning (DOL) strategy is adopted, which has an asymmetric search space and can adjust with a random opposite point to enhance the exploitation and exploration capabilities. To validate the performance of DOLGWO algorithm, 23 benchmarkdoi:10.3390/sym14091871 fatcat:plgscdvigrenfjfaipoqj3qmzm