Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm
| Autor: | Božidar Rosić, Miloš Sedak |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Optimization problem Computer science Population 02 engineering and technology Multi-objective optimization lcsh:Technology lcsh:Chemistry 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering General Materials Science education Instrumentation Metaheuristic lcsh:QH301-705.5 Fluid Flow and Transfer Processes education.field_of_study particle swarm optimization differential evolution lcsh:T Process Chemistry and Technology General Engineering Pareto principle Particle swarm optimization gear efficiency Hybrid algorithm lcsh:QC1-999 Computer Science Applications multi-objective optimization lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Differential evolution 020201 artificial intelligence & image processing lcsh:Engineering (General). Civil engineering (General) lcsh:Physics planetary gear trains |
| Zdroj: | Applied Sciences, Vol 11, Iss 1107, p 1107 (2021) Applied Sciences Volume 11 Issue 3 |
| ISSN: | 2076-3417 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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