Fuzzy Particle Swarm Optimization forManufacturing Systems

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

The main difficulty associated with decentralized energy sources is that they generally do not participate in adjustment to these systems (voltage control, frequency,

We propose a new combined approach using particle swarm optimization (PSO) and fuzzy inference penalty, which will be helpful to elevate the hard inverse problem of 3D

Abstract : In this paper, we propose a new approach using particle swarm optimization (PSO) for image reconstructions, in which we use the fuzzy inference penalty instead of

If we take J(f) as a fitness function to optimize by the PSO we will express the Value of the prior term in MAP criteria with fuzzy model in our method FP PSO (fuzzy penalty

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

To overcome the above problems when using the multi- population method, CPSO employs a global search method to detect promising sub-regions and a hierarchical clustering method

By combining the PV module and converter model with the fuzzy logic controller, we created the complete Maximum Power Point Tracker (MPPT) model, using it to tune the fuzzy