Indexé dans
- Genamics JournalSeek
- RechercheRef
- Université Hamdard
- EBSCO AZ
- OCLC - WorldCat
- Publions
- Pub européen
- Google Scholar
Liens utiles
Partager cette page
Dépliant de journal
Revues en libre accès
- Agriculture et aquaculture
- Alimentation et nutrition
- Biochimie
- Bioinformatique et biologie des systèmes
- Business & Management
- Chimie
- Génétique et biologie moléculaire
- Immunologie & Microbiologie
- Ingénierie
- La science des matériaux
- Neurosciences & Psychologie
- Science générale
- Sciences cliniques
- Sciences environnementales
- Sciences médicales
- Sciences pharmaceutiques
- Sciences vétérinaires
- Soins infirmiers et soins de santé
Abstrait
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.