In order to optimize the parameters of fractional order PID controller of complex system, an adaptive particle swarm optimization (PSO) method is proposed to realize the parameters adjustment. In this algorithm, the tuning particle population is divided into three subgroups firstly, and through introducing the swarm-aggregation degree factor and the evolution speed factor of particle, dynamical...

This paper introduces the application of particle swarm optimization techniques to generalized adaptive nonlinear and reeursive fdter structures. Particle swarm optimization (PSO) is a population based optimization algorithm, similar to the genetic algorithm (GA), that performs a struetored randomized search of an unknown parameter space by manipulating a population of parameter estimates to co...

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...

This paper introduces the application of particle swarm optimization techniques to generalized adaptive nonlinear and recursive filter structures. Particle swarm optimization (PSO) is a population based optimization algorithm, similar to the genetic algorithm (GA), that performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to c...

Abstract An advanced hybrid algorithm ( h aDEPSO) is proposed in this paper for small- and large-scale engineering design optimization problems. Suggested advanced, differential evolution (aDE) particle swarm (aPSO) integrated with aDEPSO. In aDE a novel, mutation, crossover selection strategy introduced, to avoid premature convergence. And aPSO consists of novel gradually varying parameters, e...

Model identification is one of the most important requirements for advanced control systems. Fast response and quick disturbance recovery of an advanced controller [1, 2] cannot be achieved without an accurate model of the plant. Although after years of development linear methods for model identification [3 5] have become mature, nonlinear identification methods are still needed in high perform...

In order to resolve the multi-parameter optimization problem of finite impulse response (FIR) digital filter design, a quantum bee colony algorithm (QBCA) is proposed. The proposed QBCA is a multi-dimensional search algorithm for optimization of real numbers, and uses mechanisms of quantum evolution to update the positions of different quantum bees. Computer simulations have showed that FIR dig...