A Two-Step Approach to the Search of Minimum Energy Designs via Swarm Intelligence

Recently, Swarm Intelligence Based (SIB) method, a nature-inspired metaheuristic optimization method, has been widely used in many problems that their solutions fall in discrete and continuous domains. SIB 1.0 is efficient to converge to optimal solution but its particle size is fixed and pre-define...

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Autores principales: Phoa, Frederick Kin Hing, Tsai, Tzu-Chieh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354790/
http://dx.doi.org/10.1007/978-3-030-53956-6_4
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author Phoa, Frederick Kin Hing
Tsai, Tzu-Chieh
author_facet Phoa, Frederick Kin Hing
Tsai, Tzu-Chieh
author_sort Phoa, Frederick Kin Hing
collection PubMed
description Recently, Swarm Intelligence Based (SIB) method, a nature-inspired metaheuristic optimization method, has been widely used in many problems that their solutions fall in discrete and continuous domains. SIB 1.0 is efficient to converge to optimal solution but its particle size is fixed and pre-defined, while SIB 2.0 allows particle size changes during the procedure but it takes longer time to converge. This paper introduces a two-step SIB method that combines the advantages of two SIB methods. The first step via SIB 2.0 serves as a preliminary study to determine the optimal particle size and the second step via SIB 1.0 serves as a follow-up study to obtain the optimal solution. This method is applied to the search of optimal minimum energy design and the result outperforms the results from both SIB 1.0 and SIB 2.0.
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spelling pubmed-73547902020-07-13 A Two-Step Approach to the Search of Minimum Energy Designs via Swarm Intelligence Phoa, Frederick Kin Hing Tsai, Tzu-Chieh Advances in Swarm Intelligence Article Recently, Swarm Intelligence Based (SIB) method, a nature-inspired metaheuristic optimization method, has been widely used in many problems that their solutions fall in discrete and continuous domains. SIB 1.0 is efficient to converge to optimal solution but its particle size is fixed and pre-defined, while SIB 2.0 allows particle size changes during the procedure but it takes longer time to converge. This paper introduces a two-step SIB method that combines the advantages of two SIB methods. The first step via SIB 2.0 serves as a preliminary study to determine the optimal particle size and the second step via SIB 1.0 serves as a follow-up study to obtain the optimal solution. This method is applied to the search of optimal minimum energy design and the result outperforms the results from both SIB 1.0 and SIB 2.0. 2020-06-22 /pmc/articles/PMC7354790/ http://dx.doi.org/10.1007/978-3-030-53956-6_4 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Phoa, Frederick Kin Hing
Tsai, Tzu-Chieh
A Two-Step Approach to the Search of Minimum Energy Designs via Swarm Intelligence
title A Two-Step Approach to the Search of Minimum Energy Designs via Swarm Intelligence
title_full A Two-Step Approach to the Search of Minimum Energy Designs via Swarm Intelligence
title_fullStr A Two-Step Approach to the Search of Minimum Energy Designs via Swarm Intelligence
title_full_unstemmed A Two-Step Approach to the Search of Minimum Energy Designs via Swarm Intelligence
title_short A Two-Step Approach to the Search of Minimum Energy Designs via Swarm Intelligence
title_sort two-step approach to the search of minimum energy designs via swarm intelligence
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354790/
http://dx.doi.org/10.1007/978-3-030-53956-6_4
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