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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Bibliographic Details
Published in:Neural computing & applications Vol. 28; no. 7; pp. 1619 - 1634
Main Authors: Feng, Yanhong, Wang, Gai-Ge, Deb, Suash, Lu, Mei, Zhao, Xiang-Jun
Format: Journal Article
Language:English
Published: London Springer London 01.07.2017
Springer Nature B.V
Subjects:
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 Access:Get full text
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Summary: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 constitute the search space, whereas binary vectors form the solution space. In other words, monarch butterfly optimization works directly on real-valued vectors, while solutions are represented by binary vectors. Three kinds of individual allocation schemes are tested in order to achieve better performance. Toward revising the infeasible solutions and optimizing the feasible ones, a novel repair operator, based on greedy strategy, is employed. Comprehensive numerical experimentations on three types of 0–1 KP instances are carried out. The comparative study of the BMBO with four state-of-the-art classical algorithms clearly points toward the superiority of the former in terms of search accuracy, convergent capability and stability in solving the 0–1 KP, especially for the high-dimensional instances.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-015-2135-1