Particle Swarm Optimization Of Fuzzy Penalty For 3D Image Reconstruction In X-Ray

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

Particle Swarm Optimization Of Fuzzy Penalty For 3D Image Reconstruction In X-Ray Tomography.. A.M.T.Gouicem 1 , M.Yahi 1 , A.Taleb-Ahmed 2 ,

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

The PSO algorithm is employed so as the particles can find the optimal position, lead particles to high probability area, prevent the particles degradation; then genetic algorithm

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

In this paper, one behavior of particle swarms from the domain of physics is integrated into PSO and a compound particle swarm optimization (CPSO) is proposed to address

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