A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
In this study, the application of the single input single output (SISO) neural generalized predictive control (NGPC) and SISO generalized predictive control (GPC) of a three joint robotic manipulator are presented. The sinusoidal and cubic trajectory principles were used for position reference and velocity reference trajectories. NGPC-SISO algorithm performs better than GPC-SISO algorithm for both trajectories. The GPC-SISO robotic manipulator control results have better values in the case of the sinusoidal trajectory, but the NGPC-SISO robotic manipulator control results for both the cubic and sinusoidal trajectory are almost similar.
A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs. A scalar measure of the efficiency of each participating unit is thereby provided, along with methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs. Equivalences are established to ordinary linear programming models for effecting computations. The duals to these linear programming models provide a new way for estimating extremal relations from observational data. Connections between engineering and economic approaches to efficiency are delineated along with new interpretations and ways of using them in evaluating and controlling managerial behavior in public programs.