Security-Constrained Unit Commitment Planning Using Particle Swarm Optimization

A common question when optimization algorithms are proposed is the com- putational complexity (also called computational cost or effort, CE) [9]. Usu- ally, the CE of swarm methods

The pseudo-code of the ADAP-PSO implemented is described in Algorithm 1 and its flowchart is represented in Figure 1, where M is the number of particles in the swarm, L

About the convergence of PSO, Van Den Bergh and Engelbrecht (2006) looked at the trajectories of the particles and proved that each particle converges to a stable point

In this paper, we proposed a new tracking framework based on Particle Swarm Optimization formalism and, demonstrated on both, synthetic and real data, that our algorithm outper-

As results indicated, use particle swarm optimization (PSO) method for optimizing a fuzzy logic controller (FLC) for manufacturing system is better than that of fuzzy logic

As results indicated, use particle swarm optimization (PSO) method for optimizing a fuzzy logic controller (FLC) for manufacturing system is better than that of fuzzy logic

The fitness function for a particle is defined as the squared error between the measured values and the calculated ones (ob- tained by considering the associated position) of a

Though different mutation operators give a different performance on different test problems, the adaptive muta- tion operator shows a balanced performance on all test problems and