Particle Swarm Optimization algorithm is based on iterative optimization tools, system initialization of a group of random solutions, through iterative search for the optimal value. The basic idea of the fuzzy C-means clustering algorithm is to determine each sample data belonging to a certain degree of clustering, and the degree of membership of sample data is grouped into a cluster. Favor opt...

In cellular networks, the contradiction between traffic demands and network resources is very prominent, also mobile users can move inside the heterogeneous networks from cell to another cell. Aiming at solving these issues, efficient handover (HO) mechanisms are more and more concerned to enhance mobility management. In this paper a new handover optimization algorithm for long term evaluation ...

In this paper, we propose a hybridization of electromagnetism-like mechanism (EM) and particle swarm optimization algorithm (PSO) algorithms to design the proposed functional-link based Petri recurrent fuzzy neural system (FLPRFNS) for application of nonlinear system control. The FLPRFNS has a TSK-type fuzzy consequent part which uses functional-link based orthogonal basis functions and a Petri...

In this paper, a new approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the ant colony optimization (ACO) is proposed. On-line adaptive fuzzy identification is introduced to identify the system parameters. These parameters are used to calculate the objective function based on a predictive approach and structure of RST control. Then the optimization problem is sol...

This study presents a new evolutionary learning algorithm to optimize the parameters of the neural fuzzy classifier (NFC). This new evolutionary learning algorithm is based on a hybrid of bacterial foraging optimization and particle swarm optimization. It is thus called bacterial foraging particle swarm optimization (BFPSO). The proposed BFPSO method performs local search through the chemotacti...

A fuzzy graph is a symmetric binary fuzzy relation on a fuzzy subset. The concept of fuzzy sets and fuzzy relations was introduced by L.A.Zadeh in 1965cite{zl} and further studiedcite{ka}. It was Rosenfeldcite{ra} who considered fuzzy relations on fuzzy sets and developed the theory of fuzzy graphs in 1975. The concepts of fuzzy trees, blocks, bridges and cut nodes in fuzzy graph has been studi...

An on line Fuzzy Logic Controller (FLC) realization with Genetic Algorithm (GA) optimization for Automatic Generation Control (AGC) system is developed. By rearranging the multi area AGC system into integration of the SISO cascade loops, the simple FLC Decision Table algorithm could be used in the complex AGC system. This Decision Table looking up algorithm for FLC with GA optimization is suita...

fuzzy linear programming models and methods has been one ofthe most and well studied topics inside the broad area of soft computing. itsapplications as well as practical realizations can be found in all the real worldareas. in this paper a basic introduction to the main models and methods infuzzy mathematical programming, with special emphasis on those developedby the authors, is presented. as ...

in this paper, we study the finitely many constraints of the fuzzyrelation inequality problem and optimize the linear objectivefunction on the region defined by the fuzzy max-product operator.simplification operations have been given to accelerate theresolution of the problem by removing the components having noeffect on the solution process. also, an algorithm and somenumerical and applied exa...

In this paper, we illustrate a novel optimization approach based on Multi-objective Particle Swarm Optimization (MOPSO) and Fuzzy Ant Colony Optimization (FACO). The basic idea is to combine these two techniques using the best particle of the Fuzzy Ant algorithm and integrate it as the best local Particle Swarm Optimization (PSO), to formulate a new approach called hybrid MOPSO with FACO (H-MOP...