in this paper, a novel method is developed for designing the output feedback controller for static synchronous series compensator (sssc). in the proposed method, the problem of selecting the output feedback gains for the sssc controllers is changed into an optimization problem with a time domain-based objective function.then, it is solved by using the particle swarm optimization (pso) algorithm...

in this paper, the gain in ld-celp speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (pso) algorithms to optimize the structure and parameters of neural networks. elman, multi-layer perceptron (mlp) and fuzzy artmap are the candidate neural models. the optimized number of nodes in the first and second hidden layers of el...

estimation of parameters of a hydrologic model is undertaken using a procedure called “calibration” in order to obtain predictions as close as possible to observed values. this study aimed to use the particle swarm optimization (pso) algorithm for automatic calibration of the hec-hms hydrologic model, which includes a library of different event-based models for simulating the rainfall-runoff pr...

In order to optimize the deployment of wireless sensor network nodes, and avoid energy consumption increase due node redundancy uneven coverage, multi-objective mathematical optimization problem area coverage is transformed into a function problem. Aiming at rate, dormancy rate uniformity, idea genetic algorithm mutation introduced based on discrete binary particle swarm global optimal speed mu...

Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the d...

[ISSN: 2045-7057] www.ijmse.org 16 Abstract— This paper aims to present a new simple algorithm for solving the distribution network reconfiguration (DNR) problem. This algorithm is a simple modification to the binary particle swarm optimization (BPSO) called selective particle swarm optimization (SPSO). The search space for proposed algorithm is a set of branches (switches) which are normally c...

Bayesian network is a directed acyclic graph. Existing Bayesian network learning approaches based on search & scoring usually work with a heuristic search for finding the highest scoring structure. This paper describes a new data mining algorithm to learn Bayesian networks structures based on an immune binary particle swarm optimization (IBPSO) method and the Minimum Description Length (MDL) pr...

As a useful data mining technique biclustering identifies local patterns from gene expression data. A bicluster of a gene expression dataset is a subset of genes which exhibit similar expression patterns along a subset of conditions. In this paper a new method is introduced based on greedy search algorithm combined with the evolutionary technique particle swarm optimization for the identificati...

we present a novel and e±cient scheme that selects a minimal set of effective features and channels for detecting the p300 component of the event-related potential in the brain-computer interface (bci) paradigm. for obtaining a minimal set of effective features, we take the truncated coe±cients of discrete daubechies 4 wavelet, and for selecting the effective eeg channels, we utilize an improve...