Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems
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,...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/160672 |
| _version_ | 1811679177280061440 |
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| author | Gupta, Shubham Abderazek, Hammoudi Yıldız, Betül Sultan Yildiz, Ali Riza Mirjalili, Seyedali Sait, Sadiq M. |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Gupta, Shubham Abderazek, Hammoudi Yıldız, Betül Sultan Yildiz, Ali Riza Mirjalili, Seyedali Sait, Sadiq M. |
| author_sort | Gupta, Shubham |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:05:00Z |
| format | Journal Article |
| id | ntu-10356/160672 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:05:00Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1606722022-07-29T08:20:50Z Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems Gupta, Shubham Abderazek, Hammoudi Yıldız, Betül Sultan Yildiz, Ali Riza Mirjalili, Seyedali Sait, Sadiq M. School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Optimization Metaheuristic Algorithms 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. 2022-07-29T08:20:50Z 2022-07-29T08:20:50Z 2021 Journal Article Gupta, S., Abderazek, H., Yıldız, B. S., Yildiz, A. R., Mirjalili, S. & Sait, S. M. (2021). Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems. Expert Systems With Applications, 183, 115351-. https://dx.doi.org/10.1016/j.eswa.2021.115351 0957-4174 https://hdl.handle.net/10356/160672 10.1016/j.eswa.2021.115351 183 115351 en Expert Systems with Applications © 2021 Elsevier Ltd. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Optimization Metaheuristic Algorithms Gupta, Shubham Abderazek, Hammoudi Yıldız, Betül Sultan Yildiz, Ali Riza Mirjalili, Seyedali Sait, Sadiq M. Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems |
| title | Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems |
| title_full | Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems |
| title_fullStr | Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems |
| title_full_unstemmed | Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems |
| title_short | Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems |
| title_sort | comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems |
| topic | Engineering::Electrical and electronic engineering Optimization Metaheuristic Algorithms |
| url | https://hdl.handle.net/10356/160672 |
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