Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems
dc.contributor.author | Gupta, Shubham | |
dc.contributor.author | Abderazek, Hammoudi | |
dc.contributor.author | Mirjalili, Seyedali | |
dc.contributor.author | Sait, Sadiq M. | |
dc.contributor.buuauthor | Yıldız, Betül Sultan | |
dc.contributor.buuauthor | Yıldız, Ali Rıza | |
dc.contributor.department | Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği. | |
dc.contributor.researcherid | AAL-9234-2020 | tr_TR |
dc.contributor.researcherid | F-7426-2011 | tr_TR |
dc.contributor.scopusid | 57094682600 | tr_TR |
dc.contributor.scopusid | 7102365439 | tr_TR |
dc.date.accessioned | 2024-01-22T12:32:45Z | |
dc.date.available | 2024-01-22T12:32:45Z | |
dc.date.issued | 2021-11-30 | |
dc.description.abstract | Determining the solution for real mechanical design problems is a challenging task when using the newly developed and efficient swarm intelligence algorithms. There are so many difficulties to be addressed, including but not limited to mixed decision variables, diverse constraints, inherent errors, conflicting objectives, and numerous locally optimal solutions. This work analyzes the behavior of nine metaheuristic algorithms, namely, salp swarm algorithm (SSA), multi-verse optimizer (MVO), moth-flame optimizer (MFO), atom search optimi-zation (ASO), ecogeography-based optimization (EBO), queuing search algorithm (QSA), equilibrium optimizer (EO), evolutionary strategy (ES) and hybrid self-adaptive orthogonal genetic algorithm (HSOGA). The efficiency of these algorithms is evaluated on eight mechanical design problems using the solution quality and convergence analysis, which verifies the wide applicability of these algorithms to real-world application problems. | en_US |
dc.identifier.citation | Yıldız, B. S. vd. (2021). "Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems". Expert Systems with Applications, 183. | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.eswa.2021.115351 | |
dc.identifier.issn | 0957-4174 | |
dc.identifier.issn | 1873-6793 | |
dc.identifier.scopus | 2-s2.0-85113428089 | tr_TR |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S095741742100779X | |
dc.identifier.uri | https://hdl.handle.net/11452/39221 | |
dc.identifier.volume | 183 | tr_TR |
dc.identifier.wos | 000692066300006 | tr_TR |
dc.indexed.scopus | Scopus | en_US |
dc.indexed.wos | SCIE | en_US |
dc.language.iso | en | en_US |
dc.publisher | Pergamon-Elsevier Science Ltd | en_US |
dc.relation.collaboration | Yurt içi | tr_TR |
dc.relation.collaboration | Yurt dışı | tr_TR |
dc.relation.collaboration | Sanayi | tr_TR |
dc.relation.journal | Expert Systems with Applications | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi | tr_TR |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Optimization | en_US |
dc.subject | Metaheuristic algorithms | en_US |
dc.subject | Mechanical design problems | en_US |
dc.subject | Exploration | en_US |
dc.subject | Exploitation | en_US |
dc.subject | Differential evolution | en_US |
dc.subject | Genetic algorithms | en_US |
dc.subject | Quality control | en_US |
dc.subject | Algorithm for solving | en_US |
dc.subject | Design problems | en_US |
dc.subject | Mechanical design | en_US |
dc.subject | Metaheuristic optimization | en_US |
dc.subject | Optimisations | en_US |
dc.subject | Optimization algorithms | en_US |
dc.subject | Optimizers | en_US |
dc.subject | Constrained optimization | en_US |
dc.subject.scopus | Metaheuristics; Fireflies; Chiroptera | en_US |
dc.subject.wos | Computer science, artificial intelligence | en_US |
dc.subject.wos | Engineering, electrical & electronic | en_US |
dc.subject.wos | Operations research & management science | en_US |
dc.title | Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems | en_US |
dc.type | Article | en_US |
dc.wos.quartile | Q1 | en_US |
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