Greedy Strategy Based Self-adaption Ant Colony Algorithm for 0/1 Knapsack Problem

被引：6

作者：

Du, De-peng ^{[1
,2
]}

Zu, Yue-ran ^{[1
,2
]}

机构：

[1] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Peoples R China

[2] Shandong Prov Key Lab Distributed Comp Software N, Jinan 250014, Peoples R China

来源：

UBIQUITOUS COMPUTING APPLICATION AND WIRELESS SENSOR | 2015年 / 331卷

关键词：

0/1 knapsack problem; Ant colony algorithm; Greedy strategy; Normal distribution;

D O I：

10.1007/978-94-017-9618-7_70

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

080201 [机械制造及其自动化];

摘要：

The notion of using a meta-heuristic approach to solve the Knapsack Problem has been intensively studied in recent years. By comparing and analyzing the research of Ant Colony Algorithm (AVA) for 0/1 Knapsack Problem the authors propose an improved ACA based on greedy strategy and normal distribution. Experimental results show that the proposed approach performs better than the basic ACA.

引用

页码：663 / 670

页数：8

共 9 条

[1]

Liao Can-xing, 2011, Journal of System Simulation, V23, P1156

[2]

[刘华蓥 Liu Huaying], 2005, [大庆石油学院学报, Journal of Daqing Petroleum Institute], V29, P59

[3]

LUO X, 2004, J SOOCHOW U ENG SCI, V24, P41

[4]

Ma L, 2001, COMPUT APPL, V21

[5]

Martello S, 1990, KNAPSACK PROBLEMS AL, P13

[6]

Pisinger D, 1995, THESIS, P33

[7]

Qin L, 2004, ANT COLONY ALGORITHM

[8]

Linking up with the international track: What's in a slogan? [J].

Wang, Hongying .

CHINA QUARTERLY, 2007, (189) :1-23

[9]

Xiong Y, 2012, COMPUT DIGIT ENG, V1

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