Population declining ant colony optimization algorithm and its applications

被引：26

作者：

Wu, Zhilu ^{[1
]}

Zhao, Nan ^{[1
]}

Ren, Guanghui ^{[1
]}

Quan, Taifan ^{[1
]}

机构：

[1] Harbin Inst Technol 342, Sch Elect & Informat Technol, Harbin 150001, HeilongJiang, Peoples R China

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 03期

关键词：

Artificial intelligence; Ant colony optimization; Traveling salesman problem; CDMA; Multiuser detection; SEARCH;

D O I：

10.1016/j.eswa.2008.07.013

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Population declining ant colony optimization (PDACO) algorithm is proposed and applied to the traveling salesman problem (TSP) and multiuser detection in this paper. Ant colony optimization (ACO) algorithms have already successfully been used in combinatorial optimization, however, as the pheromone accumulates, we may not get a global optimum because it stops searching early. PDACO can enlarge searching range through increasing the initial population of the ant colony, and the population declines in successive iterations. So, the performance of PDACO is superior with the same computational complexity. PDACO is applied to TSP and multiuser detection. Via computer simulations it is shown that PDACO has better performance in solving these two problems than ACO algorithms. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：6276 / 6281

页数：6

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