期刊
NEURAL COMPUTING & APPLICATIONS
卷 28, 期 7, 页码 1619-1634出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-015-2135-1
关键词
Evolutionary computation; Monarch butterfly optimization; Knapsack problems; Greedy optimization algorithm
资金
- National Natural Science Foundation of China [61272297, 61402207, 61503165]
- Jiangsu Province Science Foundation for Youths [BK20150239]
- RAMP
- D Program for Science and Technology of Shijiazhuang [155790215]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据