Testing Different Particle Swarm Optimization Strategies

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23210%2F17%3A43949489" target="_blank" >RIV/49777513:23210/17:43949489 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Testing Different Particle Swarm Optimization Strategies
Original language description
The paper deals with testing different strategies - Inertia Weight Strategies, Constriction Factors and Interpersonal Learning – of a Particle Swarm Optimization implemented in a simulation optimizer. The strategies were tested on five different discrete event simulation models reflecting real optimization problems in industrial companies. We specified different objective functions of discrete event simulation models considering the simulated system. We tested different settings of the strategies to reduce bad settings. We replicated optimization experiments with concrete optimization method parameters settings to reduce the randomness of the behaviour of the optimization method strategies. We evaluated the success of finding the optimum by the different PSO strategies according to other optimization methods - Random Search, Downhill Simplex, Hill Climbing, Tabu Search, Local Search, Simulated Annealing, Evolution Strategy, Differential Evolution and Self Organizing Migrating Algorithm.
Czech name
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Czech description
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Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 30th International Business Information Management Association Conference (IBIMA)
ISBN
978-0-9860419-9-0
ISSN
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e-ISSN
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Number of pages
22
Pages from-to
3444-3465
Publisher name
International Business Information Management Association
Place of publication
Norristown
Event location
Madrid, Spain
Event date
Nov 8, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000443640502091

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