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 |
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01.07.2017
Springer Nature B.V |
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Lu, Mei Feng, Yanhong Wang, Gai-Ge Deb, Suash Zhao, Xiang-Jun |
| Author_xml | – sequence: 1 givenname: Yanhong surname: Feng fullname: Feng, Yanhong organization: School of Information Engineering, Shijiazhuang University of Economics – sequence: 2 givenname: Gai-Ge surname: Wang fullname: Wang, Gai-Ge email: gaigewang@163.com, gaigewang@gmail.com organization: School of Computer Science and Technology, Jiangsu Normal University, Institute of Algorithm and Big Data Analysis, Northeast Normal University, School of Computer Science and Information Technology, Northeast Normal University – sequence: 3 givenname: Suash surname: Deb fullname: Deb, Suash organization: IT & Educational Consultant – sequence: 4 givenname: Mei surname: Lu fullname: Lu, Mei organization: School of Computer Science and Technology, Jiangsu Normal University – sequence: 5 givenname: Xiang-Jun surname: Zhao fullname: Zhao, Xiang-Jun organization: School of Computer Science and Technology, Jiangsu Normal University |
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| Keywords | Monarch butterfly optimization Knapsack problems Evolutionary computation Greedy optimization algorithm |
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| Title | Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization |
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