This paper introduces a new approach for designing an adaptive fuzzy model predictive control (AFMPC) using the Particle Swarm Optimization (PSO) algorithm. The system to be controlled is modeled by a Takagi-Sugeno fuzzy inference system whose parameters are identified using recursive least square algorithm. These parameters are used to calculate the objective function based on predictive appro...

cementation factor is a critical parameter, which affects water saturation calculation. in carbonate rocks, due to the sensitivity of this parameter to pore type, water saturation estimation has associated with high inaccuracy. hence developing a reliable mathematical strategy to determine these properties accurately is of crucial importance. to this end, genetic algorithm pattern search is emp...

In this paper we attempt to integrate production scheduling and distribution routing problems to minimize the total cost of the supply chain under uncertain environments. The particle swarm optimization method is used to select the location of the distribution center, combining it with the tabu search algorithm to solve vehicle distribution route problem. Especially, the location of each partic...

in this paper, a location allocation (la) problem in construction and demolition (c&d;) waste management (wm) is studied. a bi-level model for this problem under a fuzzy random environment is presented where the upper level is the governments who sets up the processing centers, and the lower level are the administrators of different construction projects who control c&d; waste and the after tre...

Image colour classification and Image segmentation using comprehensive learning particle swarm optimization (CLPSO) technique was developed by Parag Puranik, Dr. P.R. Baja, Prof. P.M. Palsodkar [1], the aim was to produce a fuzzy system for colour classification and image segmentation with least number of rules and minimum error rate. In this paper we propose exponential particle swarm optimiza...

To deal with the problem of premature convergence of the fuzzy c-means clustering algorithm based on particle swarm optimization, which is sensitive to noise and less effective when handling the data set that dimensions greater than the number of samples, a novel fuzzy c-means clustering method based on the enhanced Particle Swarm Optimization algorithm is presented. Firstly, this approach dist...

this paper proposes a method to solve multi-objective problems using improved particle swarm optimization. we propose leader particles which guide other particles inside the problem domain. two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. the first one is based on the mean of the m optimal particles and the second one is based on appoin...

Clustering is a popular data analysis and data mining technique. In this paper, a novel chaotic particle swarm fuzzy clustering (CPSFC) algorithm based on chaotic particle swarm (CPSO) and gradient method is proposed. Fuzzy clustering model optimization is challenging, in order to solve this problem, adaptive inertia weight factor (AIWF) and iterative chaotic map with infinite collapses (ICMIC)...

Image segmentation refers to the technology to segment the image into different regions with different characteristics and to extract useful objectives, and it is a key step from image processing to image analysis. Based on the comprehensive study of image segmentation technology, this paper analyzes the advantages and disadvantages of the existing fuzzy clustering algorithms; integrates the pa...

This paper proposes a combination of particle swarm optimization (PSO) and Q-value based safe reinforcement learning scheme for neuro-fuzzy systems (NFS). The proposed Q-value based particle swarm optimization (QPSO) fulfills PSO-based NFS with reinforcement learning; that is, it provides PSO-based NFS an alternative to learn optimal control policies under environments where only weak reinforce...