A Shared-Memory ACO-Based Algorithm for Numerical Optimization

Numerical optimization techniques are applied to a variety of engineering problems. The objective function evaluation is an important part of the numerical optimization and is usually realized as a black-box simulator. For efficient solving the numerical optimization problem, new shared-memory appro...

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Bibliographic Details
Published in2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum pp. 352 - 357
Main Authors Korosec, P., Silc, Jurij, Vajtersic, M., Kutil, R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2011
Subjects
Complexity theory
Computational modeling
Computers
Numerical models
Optimization
Parallel processing
Program processors
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ISBN9781612844251
1612844251
ISSN1530-2075
DOI10.1109/IPDPS.2011.176

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Summary:Numerical optimization techniques are applied to a variety of engineering problems. The objective function evaluation is an important part of the numerical optimization and is usually realized as a black-box simulator. For efficient solving the numerical optimization problem, new shared-memory approach is proposed. The algorithm is based on an ACO meta-heuristics, where indirect coordination between ants drives the search procedure towards the optimal solution. Indirect coordination offers a high degree of parallelism and therefore relatively straightforward shared-memory implementation. For the communication between processors, the Intel-OpenMP library is used. It is shown that speed-up strongly depends on the simulation time. Therefore, algorithm's performance, according to simulator's time complexity, is experimentally evaluated and discussed.
ISBN:9781612844251
1612844251
ISSN:1530-2075
DOI:10.1109/IPDPS.2011.176