It has proposed a kind of hybrid method based on intuitionistic fuzzy set theory and particle swarm optimization (PSO) algorithm-based neural network (NN). We apply it to the integrative damage effect assessment of battlefield target. Firstly, we improve PSO algorithm, propose the adaptive inertia factor and excellence selection mechanism, introduce inter-partition particle swarm initialization...

Considering the fact that BP Neural Network has the defects of being easily falling into partial extreme value, to avoid the limitation of particle swarm algorithm we come up with an improved BP Neural Network applied in Neural Network training. This method means using nonlinear decreasing weight factor to change the fundamental ways of PSO first, and then using the developed PSO ways to optimi...

This Paper presents a comparative study of Genetic Algorithm method (GA) and Particle swarm optimization (PSO) method to determine the optimal proportional-integral-derivative (PID) controller parameters, for load frequency control in a single area power system. Comparing with conventional Proportional–Integral (PI) method and the proposed PSO the performance of the controller is improved for t...

This paper presents a novel improved fuzzy particle swarm optimization (IFPSO) algorithm to the intelligent identification and control of a dynamic system. The proposed algorithm estimates optimally the parameters of system and controller by minimizing the mean of squared errors. The particle swarm optimization is enhanced intelligently by using a fuzzy inertia weight to rationally balance the ...

An enhanced particle swarm optimization (EPSO) algorithm is proposed. To improve convergence accuracy and velocity, we introduce a quadratic interpolation method and perturbation to personal best particles in EPSO. Then, a design procedure based on the EPSO is proposed for the design and optimization of equal split broadband microstrip Wilkinson power dividers (MWPDs). A set of numerical exampl...

In the standard PSO algorithm, each particle in swarm has the same inertia weight settings and its values decrease from generation to generation, which can induce the decreasing of population diversity. As a result, it may fall into the local optimum. Besides, the decreasing of weights values is restricted by the maximum evolutionary generation, which has an influence on the convergence speed a...

Laser ultrasonic defect detection and classification has been widely used in engineering and material defect detection, so detecting and classifying the defect targets accurately is significant. In order to obtain the higher classification accuracy, an improved support vector machine (SVM) based on particle swarm optimization algorithm is used as classifier in this paper. To search the optimal ...

his paper presents the review on image edge detection by using particle swarm optimization and gravitational search algorithm. The paper present the various works done by particle swarm optimization and gravitational search algorithm in the field of digital image processing. By observing the previous works the two optimization algorithms particle swarm optimization algorithm and gravitational s...

Considering the standard particle swarm optimization (PSO) has the shortcomings of low convergence precision in job shop scheduling problems, the job shop scheduling solution is presented based on improved particle swarm optimization (A-PSO). In this paper, the basic theory of A-PSO is described. Also, the coding and the selection of parameters as well as the decoding of A-PSO are studied. It u...

this paper presents a fuzzy decision-making approach to deal with a clustering supplier problem in a supply chain system. during recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. however, the nature of these decisions is usually complex and unstructured. in general, many quantitative and qualitative factors, such as quality, price, and fl...