Paper Details: Downloads: 406
Serial Number: P1120632001
Title: Design and Performance Optimization of a Multi-TSP (Traveling Salesman Problem) Algorithm
Authors: S. P. Koh and I. B. Aris and C. K. Ho and S. M. Bashi
Abstract: This paper presents a new approach to solve multi- TSP problem, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the traveling tasks need to be segregated and assigned for each salesman, and path planning where the best combinatorial tours for each salesman are determined in order to minimize the total traveling time. The main motivation for this study is to introduce and evaluate advance new customized GA. Comparison results of different combinatorial operators, and tests with different probability factors are shown. The performance of the new operators called GA_INSP (GA Inspection Module) and DPPC (Dynamic Pre-Populated Crossover) for a better evolutionary approach to the time-based problem has been discussed in the paper. The representation approach has been implemented via a computer program in order to achieve optimized multi-TSP performance.
Keywords: Multi-TSP, Genetic Algorithm.
Journal/Conference: International Journal of Artificial Intelligence and Machine Learning
Volume: 6
Issue: 3
Submission Date: 7/1/2006 12:00:00 AM
Review Date: 8/1/2006 12:00:00 AM
Publishing Date: 9/1/2006 12:00:00 AM
Article Downloads: 406
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