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Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis

Many-objective truss structure problems from small to large-scale problems with low to high design variables are investigated in this study. Mass, compliance, first natural frequency, and buckling factor are assigned as objective functions. Since there are limited optimization methods that have been...

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Autores principales: Panagant, Natee, Kumar, Sumit, Tejani, Ghanshyam G., Pholdee, Nantiwat, Bureerat, Sujin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160598/
https://www.ncbi.nlm.nih.gov/pubmed/37152671
http://dx.doi.org/10.1016/j.mex.2023.102181
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author Panagant, Natee
Kumar, Sumit
Tejani, Ghanshyam G.
Pholdee, Nantiwat
Bureerat, Sujin
author_facet Panagant, Natee
Kumar, Sumit
Tejani, Ghanshyam G.
Pholdee, Nantiwat
Bureerat, Sujin
author_sort Panagant, Natee
collection PubMed
description Many-objective truss structure problems from small to large-scale problems with low to high design variables are investigated in this study. Mass, compliance, first natural frequency, and buckling factor are assigned as objective functions. Since there are limited optimization methods that have been developed for solving many-objective truss optimization issues, it is important to assess modern algorithms performance on these issues to develop more effective techniques in the future. Therefore, this study contributes by investigating the comparative performance of eighteen well-established algorithms, in various dimensions, using four metrics for solving challenging truss problems with many objectives. The statistical analysis is performed based on the objective function best mean and standard deviation outcomes, and Friedman's rank test. MMIPDE is the best algorithm as per the overall comparison, while SHAMODE with whale optimisation approach and SHAMODE are the runners-up. • A comparative test to measure the efficiency of eighteen state-of-the-practice methods is performed. • Small to large-scale truss design challenges are proposed for the validation. • The performance is measured using four metrics and Friedman's rank test.
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spelling pubmed-101605982023-05-06 Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis Panagant, Natee Kumar, Sumit Tejani, Ghanshyam G. Pholdee, Nantiwat Bureerat, Sujin MethodsX Computer Science Many-objective truss structure problems from small to large-scale problems with low to high design variables are investigated in this study. Mass, compliance, first natural frequency, and buckling factor are assigned as objective functions. Since there are limited optimization methods that have been developed for solving many-objective truss optimization issues, it is important to assess modern algorithms performance on these issues to develop more effective techniques in the future. Therefore, this study contributes by investigating the comparative performance of eighteen well-established algorithms, in various dimensions, using four metrics for solving challenging truss problems with many objectives. The statistical analysis is performed based on the objective function best mean and standard deviation outcomes, and Friedman's rank test. MMIPDE is the best algorithm as per the overall comparison, while SHAMODE with whale optimisation approach and SHAMODE are the runners-up. • A comparative test to measure the efficiency of eighteen state-of-the-practice methods is performed. • Small to large-scale truss design challenges are proposed for the validation. • The performance is measured using four metrics and Friedman's rank test. Elsevier 2023-04-18 /pmc/articles/PMC10160598/ /pubmed/37152671 http://dx.doi.org/10.1016/j.mex.2023.102181 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Computer Science
Panagant, Natee
Kumar, Sumit
Tejani, Ghanshyam G.
Pholdee, Nantiwat
Bureerat, Sujin
Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis
title Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis
title_full Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis
title_fullStr Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis
title_full_unstemmed Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis
title_short Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis
title_sort many‑objective meta-heuristic methods for solving constrained truss optimisation problems: a comparative analysis
topic Computer Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160598/
https://www.ncbi.nlm.nih.gov/pubmed/37152671
http://dx.doi.org/10.1016/j.mex.2023.102181
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