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Performance comparison of sequential and parallel execution of the Ant Colony Optimization algorithm for solving the traveling salesman problem

Elmedina Fejzagic, Adna Oputic

Open publisher page 6 citations

Abstract

Ant Colony Optimization (ACO) is a metaheuristic algorithm which uses ideas from nature to find solutions to instances of the Travelling Salesman Problem (TSP) and other combinatorial optimisation problems. ACO is taken as one of the high performance computing methods for TSP. In this paper, the impact of parallelizing an ant colony optimization (ACO) algorithm for the traveling salesman problem in increasing performances is studied, using the task parallel library. One of the main reasons for parallelizing this alghoritm is to reduce the time needed to find a solution while the quality of solution is the same as in the algorithm which is not parallelized.

About this research paper

What this paper is about

Ant Colony Optimization (ACO) is a metaheuristic algorithm which uses ideas from nature to find solutions to instances of the Travelling Salesman Problem (TSP) and other combinatorial optimisation problems. ACO is taken as one of the high performance computing methods for TSP. In this paper, the impact of parallelizing an ant colony optimization (ACO) algorithm for the traveling salesman problem in increasing performances is studied, using the task parallel library. One of the main reasons for parallelizing this alghoritm is to reduce the time needed to find a solution while the quality of solution is the same as in the algorithm which is not parallelized.

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OpenAlex reports 6 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Ant Colony Optimization (ACO) is a metaheuristic algorithm which uses ideas from nature to find solutions to instances of the Travelling Salesman Problem (TSP) and other combinatorial optimisation problems. ACO is taken as one of the high performance computing methods for TSP. In this paper, the impact of parallelizing an ant colony optimization (ACO) algorithm for the traveling salesman problem in increasing performances is studied, using the task parallel library. One of the main reasons for parallelizing this alghoritm is to reduce the time needed to find a solution while the quality of solution is the same as in the algorithm which is not parallelized.

Key concepts: Travelling salesman problem, Ant colony optimization algorithms, Metaheuristic, Computer science, Parallel metaheuristic, Extremal optimization, Mathematical optimization, Combinatorial optimization

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