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PARALLELIZATION STRATEGY FOR ANT COLONY OPTIMIZATION METAHEURISTIC

Štefan Baboš

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Abstract

Ant Colony Optimization (ACO) is a metaheuristic for complex combinatorial optimization problems. Recent research in ACO algorithms has strongly focused on improving the performance of ACO algorithms. This paper presents the possibility of speeding up the algorithm by using parallel processing and presents a parallelization strategy for ACO metaheuristic.

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What this paper is about

Ant Colony Optimization (ACO) is a metaheuristic for complex combinatorial optimization problems. Recent research in ACO algorithms has strongly focused on improving the performance of ACO algorithms. This paper presents the possibility of speeding up the algorithm by using parallel processing and presents a parallelization strategy for ACO metaheuristic.

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Method / approach

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Available abstract

Ant Colony Optimization (ACO) is a metaheuristic for complex combinatorial optimization problems. Recent research in ACO algorithms has strongly focused on improving the performance of ACO algorithms. This paper presents the possibility of speeding up the algorithm by using parallel processing and presents a parallelization strategy for ACO metaheuristic.

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

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