A GROUPING GENETIC ALGORITHM FOR THE MULTIPLE TRAVELING SALESPERSON PROBLEM

Evelyn C. Brown (), Cliff T. Ragsdale () and Arthur E. Carter ()
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Evelyn C. Brown: Department of Engineering, East Carolina University, 225 Slay Building, Greenville, NC 27858, USA
Cliff T. Ragsdale: Department of Business Information Technology, Virginia Tech, 1007 Pamplin Hall, Blacksburg, VA 24061, USA
Arthur E. Carter: College of Information Science and Technology, Radford University, P.O. Box 6933, Radford, VA, 24142, USA

International Journal of Information Technology & Decision Making (IJITDM), 2007, vol. 06, issue 02, 333-347

Abstract: The multiple traveling salesperson problem (MTSP) involves schedulingm > 1salespersons to visit a set ofn > mlocations. Thus, thenlocations must be divided intomgroups and arranged so that each salesperson has an ordered set of cities to visit. The grouping genetic algorithm (GGA) is a type of genetic algorithm (GA) designed particularly for grouping problems. It has been successfully applied to a variety of grouping problems. This paper focuses on the application of a GGA to solve the MTSP. Our GGA introduces a new chromosome representation to indicate which salesperson is assigned to each tour and the ordering of the cities within each tour. We compare our method to standard GAs that employ either the one-chromosome or two-chromosome representation for MTSP. This research demonstrates that our GGA with its new chromosome representation is capable of solving a variety of MTSP problems from the literature and can outperform the traditional encodings of previously published GA methods.

Keywords: Multiple traveling salesperson problem; grouping genetic algorithm; genetic algorithm (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (4)

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DOI: 10.1142/S0219622007002447

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