Improving the Performance of MAX-MIN Ant System on the TSP Using Stubborn Ants

被引：0

作者：

Abdelbar, Ashraf M. ^{[1
]}

Wunsch, Donald C., II ^{[2
]}

机构：

[1] Amer Univ Cairo, Dept Comp Sci & Engn, Cairo, Egypt

[2] Missouri Univ Sci & Technol, Dept Elect & Comp Engn, Rolla, MO USA

来源：

PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTATION COMPANION (GECCO'12) | 2012年

基金：

美国国家科学基金会;

关键词：

Ant colony optimization; traveling salesman problem;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In ant colony optimization (ACO) methods, including Ant System and MAX-MIN[Ant System, each ant stochastically generates its candidate solution, in a given iteration, based on the same pheromone tau and heuristic eta information as every other ant. Stubborn ants is an ACO variation in which if an ant generates a particular candidate solution in a given iteration, then the components of that solution will have a higher probability of being selected in the candidate solution generated by that ant in the next iteration. We evaluate this variation in the context of MAX-MIN Ant System using 41 instances of the Traveling Salesman Problem (TSP), and find that it improves solution quality to a statistically-significant extent.

引用

页码：1395 / 1396

页数：2

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