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Binary artificial algae algorithm for multidimensional knapsack problems

delete2016-06-01
delete63
PRE
AI
X
Xuedong Zhang
C
Changzhi Wu
J
Jing Li
王翔宇 封面图
王翔宇 (Xiangyu Wang)
杨志景 封面图
杨志景 (Zhijing Yang) *
J
Jae-Myung Lee
K
Kwang‐Hyo Jung
DOI:10.1016/j.asoc.2016.02.027delete
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摘要

摘要

En 中文
The multidimensional knapsack problem (MKP) is a well-known NP-hard optimization problem. Various meta-heuristic methods are dedicated to solve this problem in literature. Recently a new meta-heuristic algorithm, called artificial algae algorithm (AAA), was presented, which has been successfully applied to solve various continuous optimization problems. However, due to its continuous nature, AAA cannot settle the discrete problem straightforwardly such as MKP. In view of this, this paper proposes a binary artificial algae algorithm (BAAA) to efficiently solve MKP. This algorithm is composed of discrete process, repair operators and elite local search. In discrete process, two logistic functions with different coefficients of curve are studied to achieve good discrete process results. Repair operators are performed to make the solution feasible and increase the efficiency. Finally, elite local search is introduced to improve the quality of solutions. To demonstrate the efficiency of our proposed algorithm, simulations and evaluations are carried out with total of 94 benchmark problems and compared with other bio-inspired state-of-the-art algorithms in the recent years including MBPSO, BPSOTVAC, CBPSOTVAC, GADS, bAFSA, and IbAFSA. The results show the superiority of BAAA to many compared existing algorithms. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Artificial algae algorithm
Multidimensional knapsack problem
Pseudo-utility ratio
Elite local search
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期刊

Applied Soft Computing 封面图
Applied Soft Computing
IF:
6.6
论文数:
1.4W
被引数:
4.8W

机构

P
pusan national university
学者数:
2.1W
论文数: 1.9W
被引数: 20
C
Curtin University
学者数:
1.5W
论文数: 1.8W
被引数: 2.8W
K
kyung hee university
学者数:
2.3W
论文数: 2.2W
被引数: 234
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引用论文

引用论文

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