Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism
This study presents the application of seven recent meta-heuristic optimization algorithms to automate design of disk cam mechanism with translating roller follower regarding four follower motion laws. The algorithms are: salp swarm algorithm (SSA), moth–flame optimization (MFO), ant lion optimizer...
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| Published in | Knowledge-based systems Vol. 191; p. 105237 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
05.03.2020
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0950-7051 1872-7409 1872-7409 |
| DOI | 10.1016/j.knosys.2019.105237 |
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| Abstract | This study presents the application of seven recent meta-heuristic optimization algorithms to automate design of disk cam mechanism with translating roller follower regarding four follower motion laws. The algorithms are: salp swarm algorithm (SSA), moth–flame optimization (MFO), ant lion optimizer (ALO), multi verse optimizer (MVO), grey wolf optimizer (GWO), evaporation rate water cycle algorithm (ER-WCA), and mine blast algorithm (MBA). The optimum cam design problem is formulated with three objectives including the minimum congestion, maximum performance, and maximum strength resistance of the cam. Moreover, the effect of selecting follower motion law on the optimal design of mechanism is investigated. The computational results clearly indicate that the utilized algorithms are very competitive in structural design optimization, especially MBA, ER-WCA, MFO and GWO techniques. Among the four follower motion laws, the polynomial 3-4-5 degree is the best one. |
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| AbstractList | This study presents the application of seven recent meta-heuristic optimization algorithms to automate design of disk cam mechanism with translating roller follower regarding four follower motion laws. The algorithms are: salp swarm algorithm (SSA), moth–flame optimization (MFO), ant lion optimizer (ALO), multi verse optimizer (MVO), grey wolf optimizer (GWO), evaporation rate water cycle algorithm (ER-WCA), and mine blast algorithm (MBA). The optimum cam design problem is formulated with three objectives including the minimum congestion, maximum performance, and maximum strength resistance of the cam. Moreover, the effect of selecting follower motion law on the optimal design of mechanism is investigated. The computational results clearly indicate that the utilized algorithms are very competitive in structural design optimization, especially MBA, ER-WCA, MFO and GWO techniques. Among the four follower motion laws, the polynomial 3-4-5 degree is the best one. |
| ArticleNumber | 105237 |
| Author | Abderazek, Hammoudi Yildiz, Ali Riza Mirjalili, Seyedali |
| Author_xml | – sequence: 1 givenname: Hammoudi surname: Abderazek fullname: Abderazek, Hammoudi organization: Applied Precision Mechanics Laboratory, Institute of Optics and Precision Mechanics, Setif-1-University, Algeria – sequence: 2 givenname: Ali Riza surname: Yildiz fullname: Yildiz, Ali Riza email: aliriza@uludag.edu.tr organization: Department of Automotive Engineering, Bursa Uludağ University, Görükle, Bursa, Turkey – sequence: 3 givenname: Seyedali surname: Mirjalili fullname: Mirjalili, Seyedali organization: Center for Artificial Intelligence Research and Optimization, Torrens University Australia, 90 Bowen Terrace, Fortitude Valley, Brisbane, QLD 4006, Australia |
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| Keywords | Follower motion law Moth-Flame optimizer Salp Swarm Optimizer Algorithms Metaheuristics Cam mechanism Grey wolf optimizer Multi verse optimizer Ant lion optimizer Optimization |
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| SubjectTerms | Algorithms Ant lion optimizer Cam mechanism Design optimization Evaporation rate Follower motion law Grey wolf optimizer Heuristic methods Metaheuristics Moth-Flame optimizer Multi verse optimizer Optimization Optimization algorithms Polynomials Salp Swarm Optimizer Structural design |
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| Title | Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism |
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