This paper can reasonably identify the connection weight and threshold of NN and improve capability of true problem solution. This paper also applies PSO-BP NN model into classification of fabric flaws, decomposes the fabric image at single layer and extract the sub-image in horizontal and vertical direction to represent longitudinal and latitudinal texture by using orthogonal wavelet transform...

This paper proposes an improved particle swarm optimization (PSO). In order to increase the efficiency, suggestions on parameter settings is made and a mechanism is designed to prevent particles fall into the local optimal. To evaluate its effectiveness and efficiency, this approach is applied to multimodal function optimizing tasks. 16 benchmark functions were tested, and results were compared...

This paper considers the use of randomly generated directed graphs as neighborhoods for particle swarm optimizers (PSO) using fully informed particles (FIPS), together with dynamic changes to the graph during an algorithm run as a diversity-preserving measure. Different graph sizes, constructed with a uniform out-degree were studied with regard to their effect on the performance of the PSO on o...

The author proposes an extension of particle swarm optimization (PSO) for solving interval-valued optimization problems and applies the extended PSO to evolutionary training of neural networks (NNs) with interval weights. In the proposed PSO, values in the genotypes are not real numbers but intervals. Experimental results show that interval-valued NNs trained by the proposed method could well a...

The particle swarm optimization (PSO), now widely used in the electromagnetics community, is a robust evolutionary method based on the property of swarm intelligence. This paper focuses on the random variable effect in the PSO algorithm, and two random distribution functions, namely, the uniform distribution and Gaussian distribution, are studied and compared in details. It is revealed through ...

In this paper, a fuzzy controllers type Takagi_Sugeno is optimized by method of Particle Swarm Optimization (PSO). This algorithm automatically adjust the membership function of fuzzy controllers to control a trajectory of nonholonomic mobile robot that involves path trajectory using two optimized fuzzy controllers one for speed control and the other for azimuth control. The mobile robot is mod...

In the last decade, Particle Swarm Optimization (PSO) has grown in popularity as one important method for optimization, compared to recent Differential Evolution (DE) and Harmony Search (HS). In this paper a bibliometric study is presented, carried out on the PSO research literature from 2000 to 2010. The Thomson Reuters Web of Science (WoS) was used to collect publication records and analyzed ...

.To solve the ‘soft-field’ nature and the ill-posed problem in electrical capacitance tomography (ECT) technology, this paper first presents a preconditioned conjugate gradient iterative algorithm for electrical capacitance tomography (PCG), Then, the results of the PCG algorithm using PSO method for imaging gray scale compensation. Streszczenie. Artykuł przedstawia algorytm PCG (preconditioned...

Transportation Network Design Problem (TNDP) aims to select the best project sets among a number of new projects. Recently, metaheuristic methods are applied to solve TNDP in the sense of finding better solutions sooner. PSO as a metaheuristic method is based on stochastic optimization and is a parallel revolutionary computation technique. The PSO system initializes with a number of random solu...

Metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. In the present study an attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic; particle swarm optimization or PSO. Hybridization is a method of combining two (or...