An Improved Migrating Birds Optimization for Solving the Multidimensional Knapsack Problem

被引：0
|
作者：
Meng, Tao [1 ,2 ]
Duan, Jun-hua [3 ]
Pan, Quan-ke [1 ]
Chen, Qing-da [4 ]
机构：
[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200072, Peoples R China
[2] Liaocheng Univ, Coll Math Sci, Liaocheng 252059, Peoples R China
[3] Shanghai Univ, Comp Ctr, Shanghai 200444, Peoples R China
[4] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Liaoning, Peoples R China
来源：
2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2017年
关键词：
Meta-heuristic; Multidimensional Knapsack Problem; Migrating Birds Optimization; Sharing Scheme; Binary Algorithm; PARTICLE SWARM OPTIMIZATION; GENETIC ALGORITHM; SYSTEMS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The multidimensional knapsack problem (MKP) is a famous NP-hard combinatorial optimization problem with strong engineering backgrounds. In this paper, we propose an improved migrating birds optimization (IMBO) to solve the MKP. In IMBO, to guarantee the initial swarm with a certain level of quality and diversity, we generate some meaningful solutions while other individuals are constructed randomly. In addition, considering the characteristics of MBO and MKP, an effective sharing scheme (NSS) is designed to deliver useful information to the following individual. Numerical experiments are performed and comparisons with state-of-the-art algorithms demonstrate the effectiveness of the proposed IMBO for solving the MKP.
引用
收藏
页码：4698 / 4703
页数：6
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