Particle Swarm Optimization-based Approach Research Articles

Particle Swarm Optimization-based approach for parameterization of power capacitor models fed by harmonic voltages

The modeling of power capacitors has great importance on the allocation of such devices in power distribution systems. Many models based on electrical circuits are found in the literature, but their proper parameterization is difficult when these capacitors are fed by harmonic voltages. In this context, the objective of this paper is to propose a methodology to determine, by means of a Particle Swarm Optimization (PSO) algorithm, the parameters of three different capacitor models supplied by voltages with harmonic distortions. In such a methodology, the PSO algorithm determines the parameters of each model by comparing a reference current obtained in laboratory tests with the calculated current flowing through its terminals, considering four different voltage waveforms and two types of metallized polypropylene power capacitors (all-film and impregnated). This approach is intended to be applicable as a prior tool to support capacitor allocation studies in power distribution systems. The results show that the PSO is able to efficient and effectively estimate the unknown parameters of such models. The replicability of results provided by the PSO and the robustness of the proposed methodology are demonstrated by means of statistical analyses. The Mean Squared Error (MSE) between the reference and the calculated currents for ten consecutive executions of the proposed methodology considering each waveform, model and capacitor varied between 3.049 and 0.032A2, with variation coefficients smaller than 1%; to validate the choice of the PSO algorithm, a comparison of MSE values provided by the PSO and four nonlinear programming methods is also shown.

Brain response pattern identification of fMRI data using a particle swarm optimization-based approach

Many neuroscience studies have been devoted to understand brain neural responses correlating to cognition using functional magnetic resonance imaging (fMRI). In contrast to univariate analysis to identify response patterns, it is shown that multi-voxel pattern analysis (MVPA) of fMRI data becomes a relatively effective approach using machine learning techniques in the recent literature. MVPA can be considered as a multi-objective pattern classification problem with the aim to optimize response patterns, in which informative voxels interacting with each other are selected, achieving high classification accuracy associated with cognitive stimulus conditions. To solve the problem, we propose a feature interaction detection framework, integrating hierarchical heterogeneous particle swarm optimization and support vector machines, for voxel selection in MVPA. In the proposed approach, we first select the most informative voxels and then identify a response pattern based on the connectivity of the selected voxels. The effectiveness of the proposed approach was examined for the Haxby’s dataset of object-level representations. The computational results demonstrated higher classification accuracy by the extracted response patterns, compared to state-of-the-art feature selection algorithms, such as forward selection and backward selection.

Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings

Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse suddenly. All attempts are directed to avoid structural failure as it leads to human life danger as well as wasting time and property. Using traditional methods for predicting structural failure of the RC buildings will be time-consuming and complex. Recent research proved the artificial neural network (ANN) potentiality in solving various real-life problems. The traditional learning algorithms suffer from being trapped into local optima with a premature convergence. Thus, it is a challenging task to achieve expected accuracy while using traditional learning algorithms to train ANN. To solve this problem, the present work proposed a particle swarm optimization-based approach to train the NN (NN-PSO). The PSO is employed to find a weight vector with minimum root-mean-square error (RMSE) for the NN. The proposed (NN-PSO) classifier is capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. A database of 150 multistoried buildings’ RC structures was employed in the experimental results. The PSO algorithm was involved to select the optimal weights for the NN classifier. Fifteen features have been extracted from the structural design, while nine features have been opted to perform the classification process. Moreover, the NN-PSO model was compared with NN and MLP-FFN (multilayer perceptron feed-forward network) classifier to find its ingenuity. The experimental results established the superiority of the proposed NN-PSO compared to the NN and MLP-FFN classifiers. The NN-PSO achieved 90 % accuracy with 90 % precision, 94.74 % recall and 92.31 % F-Measure.

A Particle Swarm Optimization-Based Approach for Predicting Maximum Radiated Emission From PCBs With Dominant Radiators

In this paper, an approach is proposed to predict the maximum far-field radiated emission based on phaseless near-field scanning. The maximum radiated emission from printed circuit boards is assumed to be produced by dominant magnetic dipoles located by the near-field measurement. The particle swarm optimization technique is then employed to identify the complex moment components of the dipoles. Once the dominant dipoles at each emission frequency are determined, the maximum far-field radiated emission in the interested frequency range can be calculated using the method of moments. The proposed approach allows a quick assessment of far-field radiated emission precompliance in a wide frequency range by near-field scanning. Experimental results are presented to show its feasibility and effectiveness.

Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach

Mines, quarries, and construction sites face environmental damages due to blasting environmental impacts such as ground vibration and air overpressure. These phenomena may cause damage to structures, groundwater, and ecology of the nearby area. Several empirical predictors have been proposed by various scholars to estimate ground vibration and air overpressure, but these methods are inapplicable in many conditions. However, prediction of ground vibration and air overpressure is complicated as a consequence of the fact that a large number of influential parameters are involved. In this study, a hybrid model of an artificial neural network and a particle swarm optimization algorithm was implemented to predict ground vibration and air overpressure induced by blasting. To develop this model, 88 datasets including the parameters with the greatest influence on ground vibration and air overpressure were collected from a granite quarry site in Malaysia. The results obtained by the proposed model were compared with the measured values as well as with the results of empirical predictors. The results indicate that the proposed model is an applicable and accurate tool to predict ground vibration and air overpressure induced by blasting.

A particle swarm optimization-based approach to achieve optimal design and operation strategy of standalone hybrid energy systems

As a cost-effective and reliable alternative to supply remote areas, standalone hybrid energy systems (HESs) are recently under investigation to address various concerns associated with technical, financial, and environmental issues. This paper presents a comprehensive algorithm that can simultaneously optimize the component size, operation strategy, and slope of the photovoltaic panels of a standalone HES using an improved variant of particle swarm optimization (PSO), designated as the passive congregation PSO. A new operation strategy is proposed based on the set points of the control system. The optimization algorithm determines the optimal values of the set points to efficiently optimize the HES operation. The applicability and effectiveness of the proposed method are investigated through some numerical analyses performed on a practical remote area in Iran. In doing so, the proposed method is applied to various HES configurations and the results are compared with those obtained using the existing methods. Several load growth and wind speed scenarios are considered, and their impacts on the optimization results are examined.

Collaborative production planning with production time windows and order splitting in make-to-order manufacturing

In this paper, we study a generalized production planning problem, that simultaneously investigates the two decisions that play critical roles in most firms, namely, production planning and order splitting and assignment. The problem takes into consideration the production time windows and capacities. We formulate the integrated problem as a linear mixed-integer program with a minimized total cost. A particle swarm optimization-based approach is developed to address the problem. Extensive computational experiments show that the proposed approach outperforms a commercial optimization package. Some managerial insights are also explored and reported. Finally, concluding remarks and future research directions are provided.

Hierarchical adaptive neuro-fuzzy inference systems trained by evolutionary algorithms to model plasma spray coating process

A novel architecture of hierarchical adaptive neuro-fuzzy inference systems was developed, which was tuned using a genetic algorithm and particle swarm optimization algorithm, separately. It was used to establish input-output relationships of a plasma spray coating process. The parameters, namely primary gas flow rate, stand-off distance, powder flow rate and arc current were considered as inputs of the process and the quality of coating was represented using three responses, such as its thickness, porosity and microhardness. Particle swarm optimization-based approach was found to perform better than the genetic algorithm-based approach on some test cases.

Optimal Allocation and Sizing of Active Power Line Conditioners Using a New Particle Swarm Optimization-based Approach

Optimal allocation and sizing of active power line conditioners in a distribution network is a discrete and non-linear problem. Heuristic-based optimization methods are reliable approaches for solving these types of problems, particularly for a large-scale distribution network. However, the optimization parameters in heuristic methods can influence the final solution significantly so that if these parameters are selected improperly, the risk of trapping in local minima may be remarkable. In this article, a new method—modified particle swarm optimization—is proposed for optimal planning of active power line conditioners in a distribution network that is polluted by non-linear loads. In this method, external particle swarm optimization is used to determine the main particle swarm optimization parameters accurately. To validate the performance of the proposed method, a 6-bus and the IEEE 18-bus test systems are employed. Different scenarios are studied, and the results are compared with those obtained by other optimization methods, such as conventional particle swarm optimization, genetic algorithm, and discrete non-linear programming. The results illustrate higher accuracy of the proposed modified particle swarm optimization and its applicability for solving discrete and non-linear problems, such as active power line conditioner planning.

Development of a fuzzy goal programming model for optimization of lead time and cost in an overlapped product development project using a Gaussian Adaptive Particle Swarm Optimization-based approach

The aim of this paper is to present a model-based methodology to estimate the optimal amount of overlapping and communication policy with a view to minimizing product development lead time and cost. In the first step of methodology, the underlying two factors are considered in order to formulate mathematically a multi-objective function for a complete product development project. To add these objectives, incommensurate in nature, a fuzzy goal programming-based approach is adopted as the second step. In order to attain the optimal solution of formulated objective function, this paper introduces a novel approach, “Gaussian Adaptive Particle Swarm Optimization” (GA-PSO), which is embedded with two beneficial attributes: (1) Gaussian probability distribution, and (2) Time-Varying Acceleration Coefficients strategy. An illustrative hypothetical example of mobile phones is detailed to demonstrate the proposed model-based methodology. Experiments are performed on an underlying example, and computational results are reported to support the efficacy of the proposed model.

Particle swarm optimization-based approach for accurate evaluation of upconversion parameters in Er^3+-doped fibers

In this Letter, a method for recovering homogeneous upconversion coefficients (HUCs) in Er(3+)-doped glasses and erbium-activated devices is illustrated. It is based on a particle swarm optimization (PSO) approach. The HUCs are calculated on the basis of known values of optical gain evaluated in different pumping conditions. The obtained numerical results proof that the proposed technique provides solutions that are very close to the expected values. Therefore the method constitutes a tool for the design and optimization of efficient rare-earth doped lasers and optical amplifiers. This approach can be considered a feasible and valid alternative method in the field of material science and optical engineering for determining HUCs and avoiding the employment of expensive equipment for the measurement of ion-ion interaction parameters.

Particle swarm optimization-based approach for optical finite impulse response filter design.

This study presents what is to our knowledge a new and efficient method for the design of an optical finite impulse response (FIR) filter by employing a particle swarm optimization technique. With the method proposed, the design of an optical FIR filter, which is able to provide an arbitrary spectrum output based on crystal birefringence, could be implemented with good performance and high efficiency. The design procedure is discussed. A typical example of a green/magenta filter used in a liquid crystal on silicon projection display is included to demonstrate the feasibility and efficiency of this method in this design process as compared with simulated annealing.