A two-stage mono-and multi-objective method...
This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.
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Información adicional
| Campo | Valor |
|---|---|
| Última actualización de los datos | 21 de octubre de 2025 |
| Última actualización de los metadatos | 21 de octubre de 2025 |
| Creado | 21 de octubre de 2025 |
| Formato | HTML |
| Licencia | No se ha provisto de una licencia |
| Id | b06d077f-5ac0-40ae-a6f3-a22c65ee9e99 |
| Package id | 3e419269-b661-421a-8ea5-302dae65fd05 |
| State | active |