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General Information
Editor-in-chief
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2020 Vol.10(3): 92-96 ISSN: 2010-3697
DOI: 10.7763/IJMO.2020.V10.753

Performance Comparison in the Optimisation of a Parallel Robot Using Particle Swarm Optimisation

Catalin Boanta and Cornel Brisan
Abstract—Parallel robots have many industrial applications due to their well-known advantages as high operational speeds, stiffness and accelerations. One the other hand, their workspace is reduced compared to the size of the elements of the robot. Frequently, the design of parallel robots implies a large amount of variables and nonlinear equations. This is why, a human designer generally applies optimisation algorithms in order to obtain specific properties of the robot. If the number of variables involved in the optimisation is too high, the required computational times may be extremely increased, aspect that for some applications is unacceptable. This is why, the aim of this paper is to analyse the performance comparison in terms of efficiency and computational times of an optimisation problem with several numbers of variables included in the optimisation. The variable define the geometrical characteristics of a parallel robot used for a solid waste selection system. Also, the optimisation problem is implemented using a heuristic algorithm, namely the Particle Swarm Optimization.

Index Terms—Robots, optimisation, particle swarm optimisation, workspace.

Cătălin Boanta and Cornel Brișan are with the Technical University of Cluj-Napoca, Romania (e-mail: boanta_catalin@yahoo.com, cornel.brisan@mdm.utcluj.ro).

[PDF]

Cite: Catalin Boanta and Cornel Brisan, "Performance Comparison in the Optimisation of a Parallel Robot Using Particle Swarm Optimisation," International Journal of Modeling and Optimization vol. 10, no. 3, pp. 92-96, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0). 
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