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An Approach for Solving Multiple Travelling Salesman Problem using Ant Colony Optimization

Krishna H. Hingrajiya, Ravindra Kumar Gupta Gajendra Singh

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Abstract

Ant Colony Optimization (ACO) is a heuristic algorithm which has been proven a successful technique and applied to a number of combinatorial optimization (CO) problems. The traveling salesman problem (TSP) is one of the most important combinatorial problems. Multiple traveling salesman problem (MTSP) is a typical computationally complex combinatorialOptimization problem, which is an extension of the famous traveling salesman problem (TSP). The paper proposed an approach to show how the ant colony optimization (ACO) can be applied to the MTSP with ability constraint. There are several reasons for the choice of the TSP as the problem to explain the working of ACO algorithms: it is an important NP-hard optimization problem that arises in several applications; it is a problem to which ACO algorithms are easily applied; it is easily understandable, so that the algorithm behavior is not obscured by too many technicalities; and it is a standard test bed for new algorithmic ideas as a good performance on the TSP is often taken as a proof of their usefulness. Keywords — Ant colony optimization, Traveling salesman problem

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What this paper is about

Ant Colony Optimization (ACO) is a heuristic algorithm which has been proven a successful technique and applied to a number of combinatorial optimization (CO) problems. The traveling salesman problem (TSP) is one of the most important combinatorial problems. Multiple traveling salesman problem (MTSP) is a typical computationally complex combinatorialOptimization problem, which is an extension of the famous traveling salesman problem (TSP). The paper proposed an approach to show how the ant colony optimization (ACO) can be applied to the MTSP with ability constraint. There are several reasons for the choice of the TSP as the problem to explain the working of ACO algorithms: it is an important NP-hard optimization problem that arises in several applications; it is a problem to which ACO algorithms are easily applied; it is easily understandable, so that the algorithm behavior is not obscured by too many technicalities; and it is a standard test bed for new algorithmic ideas as a good performance on the TSP is often taken as a proof of their usefulness. Keywords — Ant colony optimization, Traveling salesman problem

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Available abstract

Ant Colony Optimization (ACO) is a heuristic algorithm which has been proven a successful technique and applied to a number of combinatorial optimization (CO) problems. The traveling salesman problem (TSP) is one of the most important combinatorial problems. Multiple traveling salesman problem (MTSP) is a typical computationally complex combinatorialOptimization problem, which is an extension of the famous traveling salesman problem (TSP). The paper proposed an approach to show how the ant colony optimization (ACO) can be applied to the MTSP with ability constraint. There are several reasons for the choice of the TSP as the problem to explain the working of ACO algorithms: it is an important NP-hard optimization problem that arises in several applications; it is a problem to which ACO algorithms are easily applied; it is easily understandable, so that the algorithm behavior is not obscured by too many technicalities; and it is a standard test bed for new algorithmic ideas as a good performance on the TSP is often taken as a proof of their usefulness. Keywords — Ant colony optimization, Traveling salesman problem

Key concepts: Travelling salesman problem, Ant colony optimization algorithms, Extremal optimization, Computer science, Mathematical optimization, Combinatorial optimization, 2-opt, Bottleneck traveling salesman problem

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