This paper present, a new Velocity Feedback Adaptive Particle Swarm Optimization (VFAPSO) algorithm for Congestion Management (CM) using optimal re-scheduling of both real and reactive power generation with capacitor reactive support. The optimal rescheduling of powers in a pool model is formulated as a constrained nonlinear optimization problem. The paper proposes the application of VFAPSO alg...

For many years globally convergent probability-one homotopy methods have been remarkably successful on difficult realistic engineering optimization problems, most of which were attacked by homotopy methods because other optimization algorithms failed or were ineffective. Convergence theory has been derived for a few particular problems, and considerable fixed point theory exists, but generally ...

In this paper, we study various global optimization methods for designing QMF (quadrature mirror lter) lter banks. We formulate the design problem as a nonlinear constrained optimization problem, using the reconstruction error as the objective, and other performance metrics as constraints. This formulation allows us to search for designs that improve over the best existing designs. We present N...

Abstract. In this paper, a modified gradient projection method is proposed to solve the nonlinear constrained optimization problems, where the search direction is obtained by combing feasibility with descent. In addition, it is pointed out that, for linear constrained optimization problems, this method may be simplified and viewed as the modified version to Rosen’s method. The theoretical analy...

This paper presents a Particle Swarm Optimization (PSO) algorithm for constrained nonlinear optimization problems. In PSO, the potential solutions, called particles, are "flown" through the problem space by learning from the current optimal particle and its own memory. In this paper, preserving feasibility strategy is employed to deal with constraints. PSO is started with a group of feasible so...

Convex optimization techniques are widely used in the design and analysis of communication systems and signal processing algorithms. In this paper a novel recurrent neural network is presented for solving nonlinear strongly convex equality constrained optimization problems. The proposed neural network is based on recursive quadratic programming for nonlinear optimization, in conjunction with ho...

I. A Brief History of Optimization Research: The history of optimization of realvalued non-linear functions (including linear ones), unconstrained or constrained, goes back to Gottfried Leibniz, Isaac Newton, Leonhard Euler and Joseph Lagrange. However, those mathematicians often assumed differentiability of the optimand as well as constraint functions. Moreover, they often dealt with the equal...

Some theoretical investigations of large angle attitude manoeuvres have been based on the app l i ca ti on of Lyapunov' s stability theory. With this method r1ortensen derived stable control laws for performing attitude manoeuvres with either thrusters or reaction wheels. Previous results were idealized in that they i qnored the non l i neari ti es inherent in the opera ti on of both types of a...