Computational intelligence and the traveling salesman

Author

Samuel A. Mulder

Abstract

"The Traveling Salesman Problem (TSP) is one of the most widely studied problems in the computer science literature...Computational Intelligence approaches have traditionally performed very poorly on combinatorial optimization problems such as the TSP, when compared to results in Operations Research. This dissertation explores two different Computational Intelligence techniques and combines them with the latest Operations Research local search heuristics to develop new heuristics that expand the capabilities of existing techniques. The first approach combines an Adaptive Resonance Theory (ART) neural network with the iterated Lin-Kernighan algorithm to divide and conquer extremely large TSPs. This new algorithm provides significant advantages in scaling and memory usage. The second approach uses an Evolutionary Algorithm to parallelize the iterated Lin-Kernighan algorithm and explore the search space more thoroughly"--Abstract, page iii.

Department(s)

Computer Science

Degree Name

Ph. D. in Computer Science

Comments

System requirements: C++ Compiler required. Microsoft Visual Studio.NET using the Intel C++ Compiler 8.0 recommended.

Publisher

University of Missouri--Rolla

Publication Date

Summer 2004

Pagination

ix, 77 pages; CD-ROM

Note about bibliography

Includes bibliographical references (pages 73-76).

Rights

© 2004 Samuel Aaron Mulder, All rights reserved.

Document Type

Dissertation - Citation

File Type

text

Language

English

Subject Headings

Traveling-salesman problemHeuristic programmingCombinatorial optimization

Thesis Number

T 8560

Print OCLC #

61895974

Recommended Citation

Mulder, Samuel A., "Computational intelligence and the traveling salesman" (2004). Doctoral Dissertations. 1559.
https://scholarsmine.mst.edu/doctoral_dissertations/1559