Performance Analysis and Tuning for Parallelization of Ant Colony Optimization Using Open MPAhmed A Abouelfarag1, Walid Mohamed Aly2*, and Ashraf G Elbialy2
- Corresponding Author:
- Walid Mohamed Aly
College of Computing and Information Technology
Arab Academy for Science and Technology
Maritime Transport, Alexandria, Egypt
E-mail: [email protected]
Received date: June 21, 2014; Accepted date: July 20, 2015; Published date: July 25, 2015
Citation: Abouelfarag AA, Mohamed Aly W, Elbialy AG (2015) Performance Analysis and Tuning for Parallelization of Ant Colony Optimization Using Open MP Int J Swarm Intel Evol Comput 4:117. doi:10.4172/2090-4908.1000117
Copyright: © 2015 Abouelfarag AA et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.