An adaptive grey wolf optimization with differential evolution operator for solving the discount {0–1} knapsack problem

The discount {0–1} knapsack problem (D {0–1} KP) is a new variant of the knapsack problem. It is an NP-hard problem and also a binary optimization problem. As a new intelligent algorithm that imitates the leadership function of wolves, the grey wolf optimizer (GWO) can solve NP problems more effecti...

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Veröffentlicht in:	Neural computing & applications Jg. 37; H. 27; S. 22369 - 22385
Hauptverfasser:	Wang, Zijian, Fang, Xi, Gao, Fei, Xie, Liang, Meng, Xianchen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.09.2025 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Datasets Discounts Dynamic programming Evolutionary computation Image Processing and Computer Vision Knapsack problem Mathematical models Operators (mathematics) Optimization Parameters Probability and Statistics in Computer Science S.I.: Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Scaling factors Special Issue on Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications D {0–1} NP-hard Grey wolf optimizer KP Greedy repair operator Differential evolution operator
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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