Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization

This paper presents a novel binary monarch butterfly optimization (BMBO) method, intended for addressing the 0–1 knapsack problem (0–1 KP). Two tuples, consisting of real-valued vectors and binary vectors, are used to represent the monarch butterfly individuals in BMBO. Real-valued vectors constitut...

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Veröffentlicht in:	Neural computing & applications Jg. 28; H. 7; S. 1619 - 1634
Hauptverfasser:	Feng, Yanhong, Wang, Gai-Ge, Deb, Suash, Lu, Mei, Zhao, Xiang-Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.07.2017 Springer Nature B.V
Schlagworte:	Accuracy Artificial Intelligence Branch & bound algorithms Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Dimensional stability Image Processing and Computer Vision Knapsack problem Optimization Original Article Probability and Statistics in Computer Science Solution space Vectors (mathematics) Monarch butterfly optimization Knapsack problems Evolutionary computation Greedy optimization algorithm
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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