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Neutrosophic Adaptive Clustering Optimization in Genetic Algorithm and Its Application in Cubic Assignment Problem

Fangwei Zhang1,2,*, Shihe Xu3, Bing Han4, Liming Zhang2, Jun Ye5
1 School of Navigation and Shipping, Shandong Jiaotong University, Weihai, 264209, China
2 College of Transport and Communications, Shanghai Maritime University, Shanghai, 201306, China
3 School of Mathematics and Statistics, Zhaoqing University, Zhaoqing, 526061, China
4 School of Business, Shandong Normal University, Jinan, 250358, China
5 School of Civil and Environmental Engineering, Ningbo University, Ningbo, 315211, China
* Corresponding Author: Fangwei Zhang. Email:
(This article belongs to this Special Issue: Decision making Modeling, Methods and Applications of Advanced Fuzzy Theory in Engineering and Science)

Computer Modeling in Engineering & Sciences 2023, 134(3), 2211-2226. https://doi.org/10.32604/cmes.2022.022418

Received 09 March 2022; Accepted 30 May 2022; Issue published 20 September 2022

Abstract

In optimization theory, the adaptive control of the optimization process is an important goal that people pursue. To solve this problem, this study introduces the idea of neutrosophic decision-making into classical heuristic algorithm, and proposes a novel neutrosophic adaptive clustering optimization thought, which is applied in a novel neutrosophic genetic algorithm (NGA), for example. The main feature of NGA is that the NGA treats the crossover effect as a neutrosophic fuzzy set, the variation ratio as a structural parameter, the crossover effect as a benefit parameter and the variation effect as a cost parameter, and then a neutrosophic fitness function value is created. Finally, a high order assignment problem in warehouse management is taken to illustrate the effectiveness of NGA.

Keywords

Neutrosophic fuzzy set; heuristic algorithm; genetic algorithm; intelligent control; warehouse operation

Cite This Article

Zhang, F., Xu, S., Han, B., Zhang, L., Ye, J. (2023). Neutrosophic Adaptive Clustering Optimization in Genetic Algorithm and Its Application in Cubic Assignment Problem. CMES-Computer Modeling in Engineering & Sciences, 134(3), 2211–2226.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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