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

共 50 条

[1] A technique for improving the Max-Min Ant System algorithm
See, Phen Chiak
Wong, Kuan Yew
Komarudin
[J]. 2008 INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION ENGINEERING, VOLS 1-3, 2008, : 863 - 866
[2] MAX-MIN Ant System
Stützle, T
Hoos, HH
[J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2000, 16 (08): : 889 - 914
[3] A Weighted Max-Min Ant Colony Algorithm for TSP Instances
Bu, Yun
Li, Tian Qian
Zhang, Qiang
[J]. IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2015, E98A (03): : 894 - 897
[4] Parallel Max-Min Ant System Using MapReduce
Tan, Qing
He, Qing
Shi, Zhongzhi
[J]. ADVANCES IN SWARM INTELLIGENCE, ICSI 2012, PT I, 2012, 7331 : 182 - 189
[5] A max-min ant system algorithm with εMAX selection
Ogawa, Masato
Li, Lei
[J]. INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL, 2008, 11 (04): : 467 - 471
[6] On MAX-MIN ant system's parameters
Pellegrini, Paola
Favaretto, Daniela
Moretti, Elena
[J]. ANT COLONY OPTIMIZATION AND SWARM INTELLIGENCE, PROCEEDINGS, 2006, 4150 : 203 - 214
[7] MAX-MIN Ant System in Image Preprocessing
Laptik, R.
Navakauskas, D.
[J]. ELEKTRONIKA IR ELEKTROTECHNIKA, 2009, (01) : 21 - 24
[8] On the Explorative Behavior of MAX-MIN Ant System
Favaretto, Daniela
Moretti, Elena
Pellegrini, Paola
[J]. ENGINEERING STOCHASTIC LOCAL SEARCH ALGORITHMS: DESIGNING, IMPLEMENTING AND ANALYZING EFFECTIVE HEURISTICS, 2009, 5752 : 115 - 119
[9] Reactive max-min ant system with recursive local search and its application to TSP and QAP
Sagban, Rafid
Ku-Mahamud, Ku Ruhana
Abu Bakar, Muhamad Shahbani
[J]. INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2017, 23 (01): : 127 - 134
[10] IMPROVING HARD DISK DRIVE PRODUCTIVITY VIA MAX-MIN ANT SYSTEM
Saeheaw, Teerapun
Charoenchai, Nivit
[J]. PROCEEDINGS OF THE 2010 INTERNATIONAL CONFERENCE ON MECHANICAL, INDUSTRIAL, AND MANUFACTURING TECHNOLOGIES (MIMT 2010), 2010, : 437 - 442

← 1 2 3 4 5 →