%0 Conference Proceedings
%T Performance Analysis and Tuning for Parallelization of Ant Colony Optimization by Using OpenMP
%+ Arab Academy for Science, Technology and Maritime Transport [Alexandria] (AASTMT)
%A Abouelfarag, Ahmed, A.
%A Aly, Walid, Mohamed
%A Elbialy, Ashraf, Gamal
%Z Part 2: Algorithms
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 14th Computer Information Systems and Industrial Management (CISIM)
%C Warsaw, Poland
%Y Khalid Saeed
%Y Władysław Homenda
%I Springer
%3 Computer Information Systems and Industrial Management
%V LNCS-9339
%P 73-85
%8 2015-09-24
%D 2015
%R 10.1007/978-3-319-24369-6_6
%K Parallel metaheuristic
%K Ant colony optimization
%K Shared memory model
%K Openmp
%K Parallel threads
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X Ant colony optimization algorithm (ACO) is a soft computing metaheuristic that belongs to swarm intelligence methods. ACO has proven a well performance in solving certain NP-hard problems in polynomial time. This paper proposes the analysis, design and implementation of ACO as a parallel metaheuristics using the OpenMP 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 configurations 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.
%G English
%Z TC 8
%2 https://inria.hal.science/hal-01444506/document
%2 https://inria.hal.science/hal-01444506/file/978-3-319-24369-6_6_Chapter.pdf
%L hal-01444506
%U https://inria.hal.science/hal-01444506
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC8
%~ IFIP-CISIM
%~ IFIP-LNCS-9339