Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm
This paper considers the problem of constrained multi-objective non-linear optimization of planetary gearbox based on hybrid metaheuristic algorithm. Optimal design of planetary gear trains requires simultaneous minimization of multiple conflicting objectives, such as gearbox volume, center distance...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/3/1107 |
| _version_ | 1797407418926235648 |
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| author | Miloš Sedak Božidar Rosić |
| author_facet | Miloš Sedak Božidar Rosić |
| author_sort | Miloš Sedak |
| collection | DOAJ |
| description | This paper considers the problem of constrained multi-objective non-linear optimization of planetary gearbox based on hybrid metaheuristic algorithm. Optimal design of planetary gear trains requires simultaneous minimization of multiple conflicting objectives, such as gearbox volume, center distance, contact ratio, power loss, etc. In this regard, the theoretical formulation and numerical procedure for the calculation of the planetary gearbox power efficiency has been developed. To successfully solve the stated constrained multi-objective optimization problem, in this paper a hybrid algorithm between particle swarm optimization and differential evolution algorithms has been proposed and applied to considered problem. Here, the mutation operators from the differential evolution algorithm have been incorporated into the velocity update equation of the particle swarm optimization algorithm, with the adaptive population spacing parameter employed to select the appropriate mutation operator for the current optimization condition. It has been shown that the proposed algorithm successfully obtains the solutions of the non-convex Pareto set, and reveals key insights in reducing the weight, improving efficiency and preventing premature failure of gears. Compared to other well-known algorithms, the numerical simulation results indicate that the proposed algorithm shows improved optimization performance in terms of the quality of the obtained Pareto solutions. |
| first_indexed | 2024-03-09T03:41:13Z |
| format | Article |
| id | doaj.art-1f8ca22eb60a45e1953e0bc958560376 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T03:41:13Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1f8ca22eb60a45e1953e0bc9585603762023-12-03T14:39:32ZengMDPI AGApplied Sciences2076-34172021-01-01113110710.3390/app11031107Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution AlgorithmMiloš Sedak0Božidar Rosić1Machine Design Department, Faculty of Mechanical Engineering, University of Belgrade, Belgrade 11000, SerbiaMachine Design Department, Faculty of Mechanical Engineering, University of Belgrade, Belgrade 11000, SerbiaThis paper considers the problem of constrained multi-objective non-linear optimization of planetary gearbox based on hybrid metaheuristic algorithm. Optimal design of planetary gear trains requires simultaneous minimization of multiple conflicting objectives, such as gearbox volume, center distance, contact ratio, power loss, etc. In this regard, the theoretical formulation and numerical procedure for the calculation of the planetary gearbox power efficiency has been developed. To successfully solve the stated constrained multi-objective optimization problem, in this paper a hybrid algorithm between particle swarm optimization and differential evolution algorithms has been proposed and applied to considered problem. Here, the mutation operators from the differential evolution algorithm have been incorporated into the velocity update equation of the particle swarm optimization algorithm, with the adaptive population spacing parameter employed to select the appropriate mutation operator for the current optimization condition. It has been shown that the proposed algorithm successfully obtains the solutions of the non-convex Pareto set, and reveals key insights in reducing the weight, improving efficiency and preventing premature failure of gears. Compared to other well-known algorithms, the numerical simulation results indicate that the proposed algorithm shows improved optimization performance in terms of the quality of the obtained Pareto solutions.https://www.mdpi.com/2076-3417/11/3/1107multi-objective optimizationplanetary gear trainsgear efficiencyparticle swarm optimizationdifferential evolution |
| spellingShingle | Miloš Sedak Božidar Rosić Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm Applied Sciences multi-objective optimization planetary gear trains gear efficiency particle swarm optimization differential evolution |
| title | Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm |
| title_full | Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm |
| title_fullStr | Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm |
| title_full_unstemmed | Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm |
| title_short | Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm |
| title_sort | multi objective optimization of planetary gearbox with adaptive hybrid particle swarm differential evolution algorithm |
| topic | multi-objective optimization planetary gear trains gear efficiency particle swarm optimization differential evolution |
| url | https://www.mdpi.com/2076-3417/11/3/1107 |
| work_keys_str_mv | AT milossedak multiobjectiveoptimizationofplanetarygearboxwithadaptivehybridparticleswarmdifferentialevolutionalgorithm AT bozidarrosic multiobjectiveoptimizationofplanetarygearboxwithadaptivehybridparticleswarmdifferentialevolutionalgorithm |