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An information entropy-based grey wolf optimizer
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DOI：10.1007/s00500-022-07593-9.png)
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Abstract
En 中文
In this study, an entropy-based grey wolf optimizer (IEGWO) algorithm is proposed for solving global optimization problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between exploitation and exploration, and the premature convergence of grey wolf optimizer algorithm and consists of three aspects: Firstly, we proposed an information entropy-based population generation strategy to optimize the distribution of initial grey wolf pack. Secondly, a modified dynamic position update equation based on information entropy is introduced to maintain the population diversity in the process of iteration, thus avoiding premature convergence. Thirdly, a nonlinear convergence strategy is proposed to balance the exploration and exploitation. The performance of the proposed IEGWO algorithm is assessed on the CEC2014 and CEC2017 test suites and compared with other meta-heuristic algorithms. Furthermore, two engineering design problems and one real-world problem are also solved using the IEGWO algorithm. The experimental and statistical results indicate that the IEGWO algorithm has better solution accuracy and robustness than the compared algorithms in solving global optimization problems.
Keywords:
Global optimization
Grey wolf optimizer
Information entropy
Meta-heuristic
Journal
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2.5
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