Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Robotics and Computer-Integrated Manufacturing
Parametric design optimization of 2-DOF R-R planar manipulator-A design of experiment approach
Robotics and Computer-Integrated Manufacturing
Kinematic design of a six-DOF parallel-kinematics Machine with decoupled-motion architecture
IEEE Transactions on Robotics
Real-Valued Compact Genetic Algorithms for Embedded Microcontroller Optimization
IEEE Transactions on Evolutionary Computation
Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An Optimization Methodology for Neural Network Weights and Architectures
IEEE Transactions on Neural Networks
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks
An efficient two-step solution for vision-based pose determination of a parallel manipulator
Robotics and Computer-Integrated Manufacturing
Robotics and Autonomous Systems
A 6-DOF reconfigurable hybrid parallel manipulator
Robotics and Computer-Integrated Manufacturing
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Optimizing the system stiffness and dexterity of parallel manipulators by adjusting the geometrical parameters can be a difficult and time-consuming endeavor, especially when the variables are diverse and the objective functions are excessively complex. However, optimization techniques that are based on artificial intelligence approaches can be an effective solution for addressing this issue. Accordingly, this paper describes the implementation of genetic algorithms and artificial neural networks as an intelligent optimization tool for the dimensional synthesis of the spatial six degree-of-freedom (DOF) parallel manipulator. The objective functions of system stiffness and dexterity are derived according to kinematic analysis of the parallel mechanism. In particular, the neural network-based standard backpropagation learning algorithm and the Levenberg-Marquardt algorithm are utilized to approximate the analytical solutions of system stiffness and dexterity. Subsequently, genetic algorithms are derived from the objective functions described by the trained neural networks, which model various performance solutions. The multi-objective optimization (MOO) of performance indices is established by searching the Pareto-optimal frontier sets in the solution space. Consequently, the effectiveness of this method is validated by simulation.