Abstract
In this paper, a communication strategy for the parallelized Grey Wolf Optimizer is proposed for solving numerical optimization problems. In this proposed method, the population wolves are split into several independent groups based on the original structure of the Grey Wolf Optimizer (GWO), and the proposed communication strategy provides the information flow for the wolves to communicate in different groups. Four benchmark functions are used to test the behavior of convergence, the accuracy, and the speed of the proposed method. According to the experimental results, the proposed communicational strategy increases the speed and accuracy of the GWO on finding the best solution is up to 75% and 45% respectively in comparison with original method.
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Pan, TS., Dao, TK., Nguyen, TT., Chu, SC. (2016). A Communication Strategy for Paralleling Grey Wolf Optimizer. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. GEC 2015. Advances in Intelligent Systems and Computing, vol 388. Springer, Cham. https://doi.org/10.1007/978-3-319-23207-2_25
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DOI: https://doi.org/10.1007/978-3-319-23207-2_25
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