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Entropy 2017, 19(10), 533; doi:10.3390/e19100533

Cross Entropy Method Based Hybridization of Dynamic Group Optimization Algorithm

1
Department of Computer and Information Science, University of Macau, Macau, China
2
Department of Information Technology, Techno India College of Technology, Kalkata 700156, India
3
School of Computer Science & Engineering, University of New South Wales, Sydney 00098G, Australia
4
School of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
*
Author to whom correspondence should be addressed.
Received: 7 August 2017 / Revised: 21 September 2017 / Accepted: 29 September 2017 / Published: 9 October 2017
(This article belongs to the Special Issue Entropy-based Data Mining)
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Abstract

Recently, a new algorithm named dynamic group optimization (DGO) has been proposed, which lends itself strongly to exploration and exploitation. Although DGO has demonstrated its efficacy in comparison to other classical optimization algorithms, DGO has two computational drawbacks. The first one is related to the two mutation operators of DGO, where they may decrease the diversity of the population, limiting the search ability. The second one is the homogeneity of the updated population information which is selected only from the companions in the same group. It may result in premature convergence and deteriorate the mutation operators. In order to deal with these two problems in this paper, a new hybridized algorithm is proposed, which combines the dynamic group optimization algorithm with the cross entropy method. The cross entropy method takes advantage of sampling the problem space by generating candidate solutions using the distribution, then it updates the distribution based on the better candidate solution discovered. The cross entropy operator does not only enlarge the promising search area, but it also guarantees that the new solution is taken from all the surrounding useful information into consideration. The proposed algorithm is tested on 23 up-to-date benchmark functions; the experimental results verify that the proposed algorithm over the other contemporary population-based swarming algorithms is more effective and efficient. View Full-Text
Keywords: entropy-based; meta-heuristics; dynamic group optimization algorithm entropy-based; meta-heuristics; dynamic group optimization algorithm
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Tang, R.; Fong, S.; Dey, N.; Wong, R.K.; Mohammed, S. Cross Entropy Method Based Hybridization of Dynamic Group Optimization Algorithm. Entropy 2017, 19, 533.

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