Testing Different Particle Swarm Optimization Strategies

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Testing Different Particle Swarm Optimization Strategies

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Testing Different Particle Swarm Optimization Strategies

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20301 - Mechanical engineering

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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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Počet stran výsledku

22

Strana od-do

3444-3465

Název nakladatele

International Business Information Management Association

Místo vydání

Norristown

Místo konání akce

Madrid, Spain

Datum konání akce

8. 11. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000443640502091