Abstract
A population-based algorithm for the multi travelling salesman problem
Highlights
The multi-traveling salesman problem (mTSP) problem can be viewed from the perspective of two well-known problems: i) as a generalization of the Travelling Salesman Problem (TSP), where a set of routes is assigned to m salesmen who all start from and return to a home city, and ii) as a special case of the vehicle routing problem (VRP), in which customers are considered unitary demands and every travelling salesman only visits a predetermined number of cities
The main contributions of this paper are as follows: 1) An effective population algorithm is proposed; 2) different heuristic strategies that improve the quality of the initial population are presented; and 3) six local search operators are integrated into the methodology, with which a modified and improved version of the Genetic Algorithm presented by Chu and Beasley (1997) is obtained
While in specialized literature the work related to TSP and VRP are abundant and numerous instances and test systems are presented, relatively few studies are found on mTSP with which comparisons can be made regarding the best known solutions
Summary
The mTSP problem can be viewed from the perspective of two well-known problems: i) as a generalization of the Travelling Salesman Problem (TSP), where a set of routes is assigned to m salesmen who all start from and return to a home city, and ii) as a special case of the vehicle routing problem (VRP), in which customers are considered unitary demands and every travelling salesman only visits a predetermined number of cities. The VRP and TSP have been widely discussed in the literature, the research on the mTSP is limited. Few papers in the literature address the mTSP through efficient population-based algorithms. The main motivation for formulating a population methodology lies in the ease of integration with multi-objective strategies, which allow introducing practical aspects, such as profit, fuel consumption and environmental impact, among others. The main contributions of this paper are as follows: 1) An effective population algorithm is proposed; 2) different heuristic strategies that improve the quality of the initial population are presented; and 3) six local search operators are integrated into the methodology, with which a modified and improved version of the Genetic Algorithm presented by Chu and Beasley (1997) is obtained.
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