Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. In this paper, we present new variants of Particle Swarm Optimization (PSO) specifically designed to work well in dynamic environments. The main idea is to extend the single population PSO and Charged Particle Swarm Optimization (CPSO) methods by constructi...

The design of planar multi-layer coatings with high absorption for a desired frequency and angle range is presented. The design technique is based on Particle swarm optimization (PSO). PSO is an evolutionary optimization algorithm based on the bird fly. Several design cases are presented. Numerical results that are compared with the existing in the literature show the advantages of this approac...

In this paper we apply Particle Swarm Optimization (PSO) algorithm to the training process of a Multilayer Perceptron (MLP) on the problem of localizing a mobile GSM network terminal inside a building. The localization data includes the information about the average GSM and WiFi signals in each of the given (x,y,floor) coordinates from more than two thousand points inside a five story building....

A novel global hybrid algorithm for feedforward neural networks p. 9 Study on relationship between NIHSS and TCM-SSASD based on the BP neural network multiple models method p. 17 Application of back-propagation neural network to power transformer insulation diagnosis p. 26 Momentum BP neural networks in structural damage detection based on static displacements and natural frequencies p. 35 Defo...

In this paper, a recurrent functional neural fuzzy network (RFNFN) with symbiotic particle swarm optimization (SPSO) is proposed for solving identification and prediction problems. The proposed RFNFN model has feedback connections added in the membership function layer that can solve temporal problems. Moreover, an efficient learning algorithm, called symbiotic particle swarm optimization (SPSO...

In this paper, we propose five data fusion schemes for the Internet of Things (IoT) scenario,which are Relief and Perceptron (Re-P), Relief and Genetic Algorithm Particle Swarm Optimization (Re-GAPSO), Genetic Algorithm and Artificial Neural Network (GA-ANN), Rough and Perceptron (Ro-P)and Rough and GAPSO (Ro-GAPSO). All the schemes consist of four stages, including preprocessingthe data set ba...

This paper introduces a hybrid metaheuristic, the Variable Neighborhood Particle Swarm Optimization (VNPSO), consisting of a combination of the Variable Neighborhood Search (VNS) and Particle Swarm Optimization(PSO). The proposed VNPSO method is used for solving the multi-objective Flexible Job-shop Scheduling Problems (FJSP). The details of implementation for the multi-objective FJSP and the c...

As the computer technology improves rapidly, the scale of software has increased greatly, which makes it more and more difficult to find a bug in software. As a result, the enhancement of software quality and reliability has become an important task in the field of software engineering. Test is an important step that guarantees software quality and reliability. We put forward a novel multi-obje...

Traditional classification methods based on asymptotic theory for multi-spectral remote sensing images need the infinite training samples, which is impossible to be satisfied. And it has massive data information. Support vector classification(SVC) based on small samples overcomes above problems. However, the parameters determining its structure need to be optimized. For that, three optimization...

nowadays, by successful application of on time production concept in other concepts like production management and storage, the need to complete the processing of jobs in their delivery time is considered a key issue in industrial environments. unrelated parallel machines scheduling is a general mood of classic problems of parallel machines. in some of the applications of unrelated parallel mac...