期刊
NEURAL COMPUTING & APPLICATIONS
卷 30, 期 10, 页码 3019-3036出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-017-2903-1
关键词
Discounted {0-1} knapsack problem; Monarch butterfly optimization; Neighborhood mutation; Gaussian perturbation
资金
- National Natural Science Foundation of China [61503165]
- Natural Science Foundation of Jiangsu Province [BK20150239]
- Scientific Research Project Program of Colleges and Universities in Hebei Province [ZD2016005]
- Open Research Fund of Sichuan Key Laboratory for Nature Gas and Geology [2015trqdz04]
- Natural Science Foundation of Hebei Province [F2016403055]
As an expanded classical 0-1 knapsack problem (0-1 KP), the discounted {0-1} knapsack problem (DKP) is proposed based on the concept of discount in the commercial world. The DKP contains a set of item groups where each group includes three items, whereas no more than one item in each group can be packed in the knapsack, which makes it more complex and challenging than 0-1 KP. At present, the main two algorithms for solving the DKP include exact algorithms and approximate algorithms. However, there are some topics which need to be further discussed, i.e., the improvement of the solution quality. In this paper, a novel multi-strategy monarch butterfly optimization (MMBO) algorithm for DKP is proposed. In MMBO, two effective strategies, neighborhood mutation with crowding and Gaussian perturbation, are introduced into MMBO. Experimental analyses show that the first strategy can enhance the global search ability, while the second strategy can strengthen local search ability and prevent premature convergence during the evolution process. Based on this, MBO is combined with each strategy, denoted as NCMBO and GMMBO, respectively. We compared MMBO with other six methods, including NCMBO, GMMBO, MBO, FirEGA, SecEGA and elephant herding optimization. The experimental results on three types of large-scale DKP instances show that NCMBO, GMMBO and MMBO are all suitable for solving DKP. In addition, MMBO outperforms other six methods and can achieve a good approximate solution with its approximation ratio close to 1 on almost all the DKP 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