Technique Particle Swarm Research Articles

A multi-granularity combined prediction model based on fuzzy trend forecasting and particle swarm techniques

In spite of the impressive diversity of multi-factors fuzzy time series models, there is still a burning need to develop models that can handle the uncertainty inherent in certain data with some certain methods and obtain high forecasting accuracy. This paper proposes a novel multi-granularity combined prediction model for multi-factors fuzzy time series forecasting. The proposed model utilizes the clustering algorithm to generate different lengths of intervals, and forecasts the fuzzy trend in different granular spaces. It calculates the final forecasted value by using the forecasted fuzzy trend in each granular space and the optimal weighting vector obtained by particle swarm techniques. The proposed model can transform the uncertain problem to a certain problem through the granular computing theory. The experiments show that the presented model can not only obtain higher forecasting accuracy than several existing methods to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the civilian unemployment rate, but also capture and interpret the fuzzy trend.

A New Bias Field Estimation Method based on Adapted PSO Method

It is hard to segmentation brain MR images for the bias fields. In this paper, a new fuzzy anisotropic diffusion function is presented to reduce the effect of the noise. We use Legendre polynomial functions to reconstruct the bias field, which make the entropy of the recovered image be smallest. But it needs to compute a lot of parameters to reconstruct the bias. The traditional method uses the gradient descending method to compute the parameters. The method plunges into local best easily. In order to deal with this problem, Particle swarm optimization (PSO) method is analyzed. A new particle swarm technique is proposed that incorporates initial location information and use mutate operation make the particles away from local maxima. The experiments show that the new method can get accurate result robustly.

Optimization of the Antenna Location for Relay Cooperative System by APSO

The channel capacity of a relay cooperative system with Maximum Ratio Combining (MRC) in a realistic indoor ultra-wideband communication is investigated by ray-tracing technique and Asynchronous Particle Swarm Optimization (APSO). We deploy amplify-and-forward relay to enhance the magnitude of signal and the propagation distance for a library indoor environment. When the transmitter is set in the center of this library environment without using relay, the outage probability of channel capacity is 49 %. The outage probabilities can be reduced to 18 % by using MRC of the cooperative relay, when the transmitter and relay are fixed in the center of the right half part and left half part respectively. Moreover, APSO is employed to search the best locations of the transmit antenna and relay antenna for reducing the outage probability. The outage probability can be reduced to zero by APSO algorithm in the numerical results.

Refined microstructure of compo cast nanocomposites: the performance of combined neuro-computing, fuzzy logic and particle swarm techniques

Aluminum metal matrix composites (MMCs) reinforced with nanoceramics are ideal materials for the manufacture of lightweight automotive and other commercial parts. Adaptive neuro-fuzzy inference system combined with particle swarm optimization method is implemented in this research study in order to optimize the parameters in processing of aluminum MMCs. In order to solve the problems associated with poor wettability, agglomeration and gravity segregation of nanoparticles in the melt, a mixture of alumina and aluminum particles was used as the reinforcement instead of raw nanoalumina. Microstructural characterization shows dendritic microstructure for the sand cast and non-dendritic microstructure for the compo cast samples. The fine equiaxed structure of compo cast nanocomposite arises from copious nucleation triggered by a pressure.

Optimization of operation parameters of refrigeration cycle using particle swarm and NLP techniques

In this paper, the two mixed refrigerant refrigeration cycles were proposed to be replaced by pure ethylene cycle in the olefin plant of the Tabriz petrochemical complex. Both these components composition of refrigerant and the compressor operations pressures are the key design parameters in the mixed refrigerant refrigeration systems. The purpose of the paper is to present a systematic method based on a combination of mathematical methods and thermodynamic viewpoint to optimize mixed refrigerant cycles parameters. Particle swarm optimization and non-linear programming techniques were employed to optimize the parameters of cycles. Results show that the particle swarm optimization is superior to the NLP optimization techniques in finding the values of optimizing variables.

Solution of the 2-dimensional Bratu problem using neural network, swarm intelligence and sequential quadratic programming

In this paper, stochastic techniques have been developed to solve the 2-dimensional Bratu equations with the help of feed-forward artificial neural networks, optimized with particle swarm optimization (PSO) and sequential quadratic programming (SQP) algorithms. A hybrid of the above two algorithms, referred to as the PSO-SQP method is also studied. The original 2-dimensional equations are solved by first transforming them into equivalent one-dimensional boundary value problems (BVPs). These are then modeled using neural networks. The optimization problem for training the weights of the network has been addressed using particle swarm techniques for global search, integrated with an SQP method for rapid local convergence. The methodology is evaluated by applying on three different test cases of BVPs for the Bratu equations. Monte Carlo simulations and extensive analyses are carried out to validate the accuracy, convergence and effectiveness of the schemes. A comparative study of proposed results is made with available exact solution, as well as, reported numerical results.

Fuzzy Neural Network Technique for System State Forecasting

In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.

A preferential digital–optical correlator optimized by particle swarm technique for multi-class face recognition

An optimized preferential digital–optical correlator is proposed for multi-class face recognition, where false rejection rate and false acceptance rate are improved by incorporating the information preferentially of both intra-class and other class face images for a given database during the synthesis of the correlator filter. Class compactness of both types of classes is made which makes the system more robust to distortion tolerance as well as misclassification. The optimization of trade-off parameters for both constrained and unconstrained type is carried out by particle swarm technique where particle vectors are considered as the correlator parameters to be optimized. Results on standard face databases have established that the performance of proposed correlator is better and more robust than the other existing classes of correlation filters. A digital–optical hybrid hardware architecture is developed and the performance for fast multi-class face recognition is compared with another existing hardware. The main contribution of the paper is related to the selection of optimum filter parameters and hardware testing in the two digital–optical hybrid architectures for multi-class face recognition.

Soft Computing Approach To Automatic Test Pattern Generation For Sequential Vlsi Circuit

Due to the constant development in the integrated circuits, the automatic test pattern generation problem become more vital for sequential vlsi circuits in these days. Also testing of integrating circuits and systems has become a difficult problem. In this paper we have discussed the problem of the automatic test sequence generation using particle swarm optimization(PSO) and technique for structure optimization of a deterministic test pattern generator using genetic algorithm(GA).

Optimal Static State Estimation Using hybrid Particle Swarm-Differential Evolution Based Optimization

In this paper, swarm optimization hybridized with differential evolution (PSO-DE) technique is proposed to solve static state estimation (SE) problem as a minimization problem. The proposed hybrid method is tested on IEEE 5-bus, 14-bus, 30-bus, 57-bus and 118-bus standard test systems along with 11-bus and 13-bus ill-conditioned test systems under different simulated conditions and the results are compared with the same, obtained using standard weighted least square state estimation (WLS-SE) technique and general particle swarm optimization (GPSO) based technique. The performance of the proposed optimization technique for SE, in terms of minimum value of the objective function and standard deviations of minimum values obtained in 100 runs, is found better as compared to the GPSO based technique. The statistical error analysis also shows the superiority of the proposed PSO-DE based technique over the other two techniques.

Robust design of broadband EUV multilayer beam splitters based on particle swarm optimization

A robust design idea for broadband EUV multilayer beam splitters is introduced that achieves the aim of decreasing the influence of layer thickness errors on optical performances. Such beam splitters can be used in interferometry to determine the quality of EUVL masks by comparing with a reference multilayer. In the optimization, particle swarm techniques were used for the first time in such designs. Compared to conventional genetic algorithms, particle swarm optimization has stronger ergodicity, simpler processing and faster convergence.

Variable ordering for shared binary decision diagrams targeting node count and path length optimisation using particle swarm technique

This study presents a particle swarm optimisation (PSO)-based approach to optimise node count and path length of the binary decision diagram (BDD) representation of Boolean function. The optimisation is achieved by identifying a good ordering of the input variables of the function. This affects the structure of the resulting BDD. Both node count and longest path length of the shared BDDs using the identified input ordering are found to be much superior to the existing results. The improvements are more prominent for larger benchmarks. The PSO parameters have been tuned suitably to explore a large search space within a reasonable computation time.

Particle swarm algorithm for cost allocation of major operational materials in an alloy steel plant

This paper brings out the application of particle swarm technique for allocation of the major components of process cost to various qualities of alloy steel. The cost of the major operational materials such as refractories, electrodes and nipples, ingot moulds etc., cannot be directly allocated to each quality. Since the consumption of operational materials does not only depend on the time but also on the quality of the steel manufactured, the approach envisages the application of particle swarm algorithm to estimate the consumption pattern for each quality manufactured, so that a realistic quality-wise cost can be arrived at. Key words: Cost allocation, operational materials, alloy steel, particle swarm.

A real-time algorithm for the determination of R744 systems optimal high pressure

In this paper the optimal energy efficiency and high pressure problem in single stage transcritical carbon dioxide vapour compression units is addressed. A real-time model-based optimisation algorithm for the optimal (or quasi-optimal, close to the optimal) pressure determination is developed as a more efficient and robust solution than literature approximated ones. The problem is solved by a model estimating the system performance and by measuring its boundary conditions. The model is obtained by an on-line artificial neural network identification technique and the optimisation problem is worked out by a particle swarm technique. The proposed algorithm is dynamically tested by simulation, considering the performance of a supply water temperature controlled carbon dioxide heat pump. It appears to successfully approximate the optimal cycle discharge pressure showing an average daily pressure deviation of 0.9×105Pa over a two years simulation period, corresponding to 1.1% increase in the cumulated energy consumption, compared to the optimal pressure solution.

Multi-objective collaborative multidisciplinary design optimization using particle swarm techniques and fuzzy decision making

This article focuses on the efficient multi-objective particle swarm optimization algorithm to solve multidisciplinary design optimization problems. The objective is to extend the formulation of collaborative optimization which has been widely used to solve single-objective optimization problems. To examine the proposed structure, racecar design problem is taken as an example of application for three objective functions. In addition, a fuzzy decision maker is applied to select the best solution along the pareto front based on the defined criteria. The results are compared to the traditional optimization, and collaborative optimization formulations that do not use multi-objective particle swarm optimization. It is shown that the integration of multi-objective particle swarm optimization into collaborative optimization provides an efficient framework for design and analysis of hierarchical multidisciplinary design optimization problems.

Particle Swarm Optimization of Passive Filters for Industrial Plants in Distribution Networks

Single-tuned passive filters are considered one of the most effective and economical means of harmonic mitigation. One important factor to consider while designing passive filters is the source voltage harmonics. This article presents a novel approach based on a particle swarm technique to optimize the design of the single-tuned passive filters for industrial plants in distribution networks. The filter design problem is formulated as a non-linear programming problem, where the main objective is to minimize the load voltage total harmonic distortion, taking into account the source voltage harmonics. The optimal filter parameters are determined using particle swarm optimization with the static penalty constraint handling approach. The particle swarm optimization algorithm performance is tested on ten constrained non-linear programming benchmark cases and a previous filter design problem. Initial results show that single-tuned passive filters can fail to maintain harmonic levels within standard levels in the presence of voltage source harmonics. Thus, a new additional problem formulation is presented to determine the maximum allowable source voltage harmonics beyond which a single-tuned passive filter would fail to maintain voltage and current harmonics within the recommended levels. Simulation results are presented to illustrate the importance of taking into account source voltage harmonics in filter design.

Particle Swarm Optimization for Automatic Calibration of Large Scale Water Quality Model (CE-QUAL-W2): Application to Karkheh Reservoir, Iran

An automatic calibration of water quality model is developed in this research. Automatic calibration as the process to determine the parameters appearing in the equations of a 2-dimensional, hydrodynamic, and water quality models (CE-QUAL-W2) is carried out with Particle Swarm technique as an optimization tool. In the calibration of the CE-QUAl-W2 model, evaporation as a significant parameter influences the thermal profile and water surface elevation in reservoir, simultaneously. Therefore to consider the simultaneous effects of the water temperature variations on water surface elevation in the reservoir, a multi objective technique is used to minimize the weighted sum of total deviations of temperature from field data at check points on monitoring days and those of water surface elevation on daily monitoring period. The proposed approach overcomes the high computational efforts required if a conventional calibration search technique was used, while retaining the quality of the final calibration results. The automatic calibration approach is applied in temperature and water budget calibration of Karkheh reservoir in Iran. Applying the proposed automatic calibration approach, shows the produced results by the CE-QUAL-W2 model with the calibrated coefficients agree closely with a set of field data.

Identification of Asynchronous Machine Parameters by Evolutionary Techniques

Four evolutionary techniques (scatter search, evolutionary programming, ant colony, and particle swarm algorithms) were used for off-line identification of three phase asynchronous machine parameters. Optimization techniques were then tested on two distinct machines. In order to evaluate how much good the achieved machines parameters obtained, experimental and simulation input-output behaviors are presented for each method. The performances in term of objective function and convergence time prove the effectiveness of this class of optimization methods.

EVOLVING DIGITAL CIRCUITS USING HYBRID PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTION

This paper presents the evolution of combinational logic circuits by a new hybrid algorithm known as the Differential Evolution Particle Swarm Optimization (DEPSO), formulated from the concepts of a modified particle swarm and differential evolution. The particle swarm in the hybrid algorithm is represented by a discrete 3-integer approach. A hybrid multi-objective fitness function is coined to achieve two goals for the evolution of circuits. The first goal is to evolve combinational logic circuits with 100% functionality, called the feasible circuits. The second goal is to minimize the number of logic gates needed to realize the feasible circuits. In addition, the paper presents modifications to enhance performance and robustness of particle swarm and evolutionary techniques for discrete optimization problems. Comparison of the performance of the hybrid algorithm to the conventional Karnaugh map and evolvable hardware techniques such as genetic algorithm, modified particle swarm, and differential evolution are presented on a number of case studies. Results show that feasible circuits are always achieved by the DEPSO algorithm unlike with other algorithms and the percentage of best solutions (minimal logic gates) is higher.

An Approach to Multimodal Biomedical Image Registration Utilizing Particle Swarm Optimization

Biomedical image registration, or geometric alignment of two-dimensional and/or three-dimensional (3D) image data, is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Registration based on intensity values usually requires optimization of some similarity metric between the images. Local optimization techniques frequently fail because functions of these metrics with respect to transformation parameters are generally nonconvex and irregular and, therefore, global methods are often required. In this paper, a new evolutionary approach, particle swarm optimization, is adapted for single-slice 3D-to-3D biomedical image registration. A new hybrid particle swarm technique is proposed that incorporates initial user guidance. Multimodal registrations with initial orientations far from the ground truth were performed on three volumes from different modalities. Results of optimizing the normalized mutual information similarity metric were compared with various evolutionary strategies. The hybrid particle swarm technique produced more accurate registrations than the evolutionary strategies in many cases, with comparable convergence. These results demonstrate that particle swarm approaches, along with evolutionary techniques and local methods, are useful in image registration, and emphasize the need for hybrid approaches for difficult registration problems.