群知能と進化的計算の国際ジャーナル

概要

Performance Analysis and Tuning for Parallelization of Ant Colony Optimization Using Open MP

Ahmed A Abouelfarag, Walid Mohamed Aly, and Ashraf G Elbialy

Abstract Ant colony optimization algorithm (ACO) is a soft computing met heuristic that belongs to swarm intelligence methods. ACO has proven a well performance in solving certain NP-hard problems in polynomial time. This paper presents the analysis, design and implementation of ACO as a Parallel Me-heuristics using the Open MP framework. To improve the efficiency of ACO parallelization, different related aspects are examined, including scheduling of threads, race hazards and efficient tuning of the effective number of threads. A case study of solving the traveling salesman problem (TSP) using different con-figurations is presented to evaluate the performance of the proposed approach. Experimental results show a significant speedup in execution time for more than 3 times over the sequential implementation.