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http://dx.doi.org/10.7472/jksii.2021.22.1.1

A Hybrid Estimation of Distribution Algorithm with Differential Evolution based on Self-adaptive Strategy  

Fan, Debin (School of Information Science and Technology, Jiujiang University)
Lee, Jaewan (Department of Information and Communication Engineering, Kunsan National University)
Publication Information
Journal of Internet Computing and Services / v.22, no.1, 2021 , pp. 1-11 More about this Journal
Abstract
Estimation of distribution algorithm (EDA) is a popular stochastic metaheuristic algorithm. EDA has been widely utilized in various optimization problems. However, it has been shown that the diversity of the population gradually decreases during the iterations, which makes EDA easily lead to premature convergence. This article introduces a hybrid estimation of distribution algorithm (EDA) with differential evolution (DE) based on self-adaptive strategy, namely HEDADE-SA. Firstly, an alternative probability model is used in sampling to improve population diversity. Secondly, the proposed algorithm is combined with DE, and a self-adaptive strategy is adopted to improve the convergence speed of the algorithm. Finally, twenty-five benchmark problems are conducted to verify the performance of HEDADE-SA. Experimental results indicate that HEDADE-SA is a feasible and effective algorithm.
Keywords
Hybrid algorithm; Estimation of Distribution; Differential Evolution; Self-adaptive Strategy;
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