Hybrid optimization algorithm of ant colony optimization and Lagrangian relaxation for solving multidimensional knapsack problem

被引：0
|
作者：
Ren Z.-G. [1 ]
Zhao S.-Y. [1 ]
Huang S.-S. [1 ]
Liang Y.-S. [1 ]
机构：
[1] School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an
来源：
Ren, Zhi-Gang (renzg@mail.xjtu.edu.cn) | 1600年 / Northeast University卷 / 31期
关键词：
Ant colony optimization; Core problem; Lagrangian relaxation; Multidimensional knapsack problem;
D O I：
10.13195/j.kzyjc.2015.0690
中图分类号：
学科分类号：
摘要：
A hybrid optimization algorithm that integrates ant colony optimization(ACO) with Lagrangian relaxation(LR) is proposed for solving NP-hard and strongly constrained multidimensional knapsack problems(MKP). This algorithm takes ACO as the basic framework and defines a novel utility index for MKP based on LR dual information. ACO endows the algorithm with global search ability, and the designed utility index organically combines the optimization object and the constraint conditions of MKP together. Benefiting from this characteristic, the utility index is used to define the core problem for MKP on the one hand, with the aim of reducing the problem scale. On the other hand, it can be used as the heuristic factor of ACO, directing the algorithm to intensively search those promising solution areas. Simulation results on a large number of benchmark instances show that the proposed algorithm is of strong robustness. Compared with existing algorithms, it is also highly competitive in terms of solution quality and efficiency. © 2016, Editorial Office of Control and Decision. All right reserved.
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页码：1178 / 1184
页数：6
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