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Insertion ant colony optimization fo...

State University of New York at Buffalo., Geography.

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Insertion ant colony optimization for solving the traveling salesman problem.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Insertion ant colony optimization for solving the traveling salesman problem./
Author:	Kuo, Yu-Cheng.
Description:	145 p.
Notes:	Adviser: Jean-Claude Thill.
Contained By:	Dissertation Abstracts International69-08B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3320491
ISBN:	9780549735625

Insertion ant colony optimization for solving the traveling salesman problem.
Kuo, Yu-Cheng.

Insertion ant colony optimization for solving the traveling salesman problem. - 145 p.

Adviser: Jean-Claude Thill.

Thesis (Ph.D.)--State University of New York at Buffalo, 2008.

Inspired from the ant foraging behavior, the ant colony algorithm has provided a new approach for solving discrete optimization problems---the traveling salesman problem (TSP), for instance, is usually tested as the benchmark. Since the ant colony algorithm was introduced in the 1990's, many refinements have been developed to improve the performance by refining the pheromone updating strategies, which have achieved great success on small- and medium-size of TSP instances. However, refining the pheromone updating strategies only can not further improve the performance of the traditional ant colony algorithms on large scale TSP instances. In this study, a new algorithm, insertion ant colony system (IACS) is proposed in order to overcome drawbacks that traditional ant colony algorithms may encounter while solving large scale TSP instances. By incrementally re-optimizing the initial solution, the IACS performs as well as the LK algorithm on large TSP instances.

ISBN: 9780549735625Subjects--Topical Terms:

626642
Computer Science.

Insertion ant colony optimization for solving the traveling salesman problem.
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100 1 $a Kuo, Yu-Cheng. $3 1064852
245 1 0 $a Insertion ant colony optimization for solving the traveling salesman problem.
300 $a 145 p.
500 $a Adviser: Jean-Claude Thill.
500 $a Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4628.
502 $a Thesis (Ph.D.)--State University of New York at Buffalo, 2008.
520 $a Inspired from the ant foraging behavior, the ant colony algorithm has provided a new approach for solving discrete optimization problems---the traveling salesman problem (TSP), for instance, is usually tested as the benchmark. Since the ant colony algorithm was introduced in the 1990's, many refinements have been developed to improve the performance by refining the pheromone updating strategies, which have achieved great success on small- and medium-size of TSP instances. However, refining the pheromone updating strategies only can not further improve the performance of the traditional ant colony algorithms on large scale TSP instances. In this study, a new algorithm, insertion ant colony system (IACS) is proposed in order to overcome drawbacks that traditional ant colony algorithms may encounter while solving large scale TSP instances. By incrementally re-optimizing the initial solution, the IACS performs as well as the LK algorithm on large TSP instances.
590 $a School code: 0656.
650 4 $a Computer Science. $3 626642
650 4 $a Physical Geography. $3 893400
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690 $a 0984
710 2 $a State University of New York at Buffalo. $b Geography. $3 1028193
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