Chaotic Particle Swarm Optimization Research Articles

Real-time Path Planning Strategy for UAV Based on Improved Particle Swarm Optimization

Unmanned Aerial Vehicle (UAV) path planning is divided into off-line static path planning and real-time dynamic path planning. The former one is applied to the ideal situation that the terrain has been clear, and there is no unexpected situation in flight. Actually, however, the flight situation is very complex, we have to adopt real-time path planning based on off-line static path planning. To meet the demand of real-time dynamic path planning, this paper proposes a dynamic path planning strategy of adaptive chaotic particle swarm optimization (PSO) algorithm, which owns both good global and local search ability. The simulation shows that the path planning strategy this paper proposes, basically, meets the needs of real-time path planning. Moreover, it has better performance than other algorithm.

Pure harmonics extracting from time-varying power signal based on improved empirical mode decomposition

Harmonic decomposition makes a great impact on power system operation, especially devices with frequency converters are widely used in modern society. In this paper, a hybrid method based on improved empirical mode decomposition enhanced with masking signals is presented to extract single-frequency harmonics from disturbed power signals accurately. The parameters for building masking signals are optimized by cooperative chaotic particle swarm optimization, where the Logistic chaos and cooperative evolution are employed to improve the convergence accuracy and avoid trapping into local minima. For improving the performance further, the improved fast Fourier transform based on Nuttall window and harmonics pre-extracting procedure are introduced to enhance the decomposition accuracy and reduce the instantaneous magnitude error in extracting time-varying power signal. The synthetic and field experiments demonstrate that the proposed method reveals significant improvements in the integrality and decomposition accuracy of harmonics extracted from time-varying power signal.

Multi-Swarm Chaotic Particle Swarm Optimization for Protein Folding

Multi-Swarm Chaotic Particle Swarm Optimization for Protein Folding

Development of CMOS Image Sensor System based on Chaos Particle Swarm

Particle swarm optimization algorithm in solving complex functions, such as slow convergence, accuracy is not high, easily falling into local optimum problem. Based on the chaos optimization is introduced into particle swarm optimization algorithm, given the chaotic particle swarm optimization algorithm. In order to improve the image quality of CMOS image sensor, the image of the main noise source and the distortion of image analysis, this paper present the development of CMOS image sensor system based on chaos particle swarm. Computer CMOS image sensor image quality simulation, the results show that chaotic particle swarm optimization algorithm convergence performance is better than that of particle swarm optimization algorithm.

Prediction of CO2 Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method

AbstractTo replace costly and time‐consuming experimentation in laboratory, a novel solubility prediction model based on chaos theory, self‐adaptive particle swarm optimization (PSO), fuzzy c‐means clustering method, and radial basis function artificial neural network (RBF ANN) is proposed to predict CO2 solubility in polymers, hereafter called CSPSO‐FC RBF ANN. The premature convergence problem is overcome by modifying the conventional PSO using chaos theory and self‐adaptive inertia weight factor. Fuzzy c‐means clustering method is used to tune the hidden centers and radial basis function spreads. The modified PSO algorithm is employed to optimize the RBF ANN connection weights. Then, the proposed CSPSO‐FC RBF ANN is used to investigate solubility of CO2 in polystyrene (PS), polypropylene (PP), poly(butylene succinate) (PBS) and poly(butylene succinate‐co‐adipate) (PBSA), respectively. Results indicate that CSPSO‐FC RBF ANN is an effective method for gas solubility in polymers. In addition, compared with conventional RBF ANN and PSO ANN, CSPSO‐FC RBF ANN shows better performance. The values of average relative deviation (ARD), squared correlation coefficient (R2) and standard deviation (SD) are 0.1071, 0.9973 and 0.0108, respectively. Statistical data demonstrate that CSPSO‐FC RBF ANN has excellent prediction capability and high‐accuracy, and the correlation between prediction values and experimental data is good.

An Elimination Method of Polarization-Induced Phase Shift and Fading in Dual Mach–Zehnder Interferometry Disturbance Sensing System

We proposed an improved polarization control method to reduce polarization-induced phase shift (PIPS) and polarization-induced fading (PIF) in a dual Mach-Zehnder interferometry (DMZI) disturbance sensing system. PIPS and PIF will seriously affect the positioning accuracy of the DMZI sensor. This polarization control method uses two PCs to control the state of polarization at the inputs of clockwise Mach-Zehnder interferometer and counter-clockwise Mach-Zehnder interferometer, respectively, based on chaotic particle swarm optimization algorithm, which has advantages of high speed and easy implementation. We experimentally demonstrated that the polarization method can overcome the effect of PIPS and PIF, improve the positioning accuracy of the DMZI sensor efficiently with positioning error of ±20 m after polarization control. Compared with the traditional polarization control method in DMZI based on the criterion of visibility, the positioning error of the sensor using our proposed method has been reduced at least an order of magnitude.

Prediction of Ozone Concentration in Semi-Arid Areas of China Using a Novel Hybrid Model

The semi-arid regions of China support delicately balanced ecosystems that are likely to be significantly affected by small changes in the level of atmospheric ozone. To predict ozone concentrations in such areas, this paper proposes a new hybrid forecasting model called an ARMT-CPSO-BP neural network, which is based on the association rule mining technique (ARMT), a chaotic particle swarm optimization algorithm (CPSO) and a back-propagation (BP) neural network (CPSO-BP neural network). This paper first uses the ARMT to find correlations between meteorological variables and ozone concentrations. The significant correlation coefficients are then fed into the CPSO-BP neural network to obtain a prediction. Finally, the predicted results are compared with the results obtained from the BP neural network and the regression model. The comparison shows that the proposed hybrid model is superior to both the BP neural network and the regression model. The hybrid model reduces the square root mean square error (RMSE) by 31.6% compared to the BP model and by 23.8% compared to the regression model. The hybrid model is promising for forecasting ozone concentration in arid and semi-arid areas.

Digital Circuit Design using Chaotic Particle Swarm Optimization Assisted by Genetic Algorithm

Optimal digital circuit design could be very beneficial for reducing the expenses of electronic devices. But since it is a complicated problem in general, utilization of sophisticated algorithm for this task seems necessary. In this paper a new method based on combining particle swarm optimization, genetic algorithm and chaotic search is proposed for digital circuit design. The inclusion of wiring minimization in objective function is also proposed and investigated.Keywords: Digital Circuits, Particle Swarm Optimization, Genetic Algorithm, Chaotic Local Search, Wiring Minimization.

Analysis and Design of E-Shaped Dual-Frequency Microstrip Antenna Based on CPSO Algorithm

In order to solve the problem of design and optimization for E-shaped patch microstrip antenna, chaotic particle swarm optimization (CPSO) algorithm was proposed to assist the procedure. First, the E-shaped antennas model was created, and then, the parameters of this antenna were adjusted according to the CPSO algorithm, and the procedure was repeated until the antennas capabilities meet the requirements. The simulation results showed that, the optimized model of such antenna, was capable of dual-frequency operation in 1.8GHz and 2.4GHz, and it was also capable of wide bandwidth, could meet the requirements very well.

Application and Comparison of PSO, its Variants and HDE Techniques to Emission/Economic Dispatch

The conventional particle swarm optimization (PSO) algorithm known for its global searching capabilities has been successfully applied to emission/economic dispatch (EED) problems. In this paper, the applicability of PSO and its four variants, namely, self-adaptive PSO, dispersed PSO, chaotic PSO and new PSO to emission/economic dispatch problems has been presented. Furthermore, the employability of hybrid differential evolution (HDE) technique to EED problems is also proved here. Two numerical examples are tested—one having 9 units, and limits on SO2 and NO x emissions and the other having 6 units with NO x emission and B-loss co-efficients. Results obtained are compared with those reported in literature. The comparison of the results shows that the conventional PSO, its variants and HDE algorithm converge to optimal or near optimal solutions in the two examples tested in this paper. But the convergence time in HDE is far greater when compared with PSO and its variants.

Operon Prediction Using Chaos Embedded Particle Swarm Optimization

Operons contain valuable information for drug design and determining protein functions. Genes within an operon are co-transcribed to a single-strand mRNA and must be coregulated. The identification of operons is, thus, critical for a detailed understanding of the gene regulations. However, currently used experimental methods for operon detection are generally difficult to implement and time consuming. In this paper, we propose a chaotic binary particle swarm optimization (CBPSO) to predict operons in bacterial genomes. The intergenic distance, participation in the same metabolic pathway and the cluster of orthologous groups (COG) properties of the Escherichia coli genome are used to design a fitness function. Furthermore, the Bacillus subtilis, Pseudomonas aeruginosa PA01, Staphylococcus aureus and Mycobacterium tuberculosis genomes are tested and evaluated for accuracy, sensitivity, and specificity. The computational results indicate that the proposed method works effectively in terms of enhancing the performance of the operon prediction. The proposed method also achieved a good balance between sensitivity and specificity when compared to methods from the literature.

Study on Chaotic Particle Swarm Optimization Algorithm in Solution of Logistics Scheduling Problem

Study the basic theory and process of chaos particle swarm optimization (PSO) algorithm, improve the basic PSO algorithm by introducing the self-adaptive inertia weighting factor method. Construct the mathematical model of basic logistics scheduling to complete the simulation analysis experiments. Experiment results show that self-adaptive chaos particle swarm optimization algorithm is effective and feasible to solve the logistics scheduling model problem.

Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization

A novel intelligent fault diagnosis model based on multi-kernel support vector machine (MSVM) with chaotic particle swarm optimization (CPSO) for roller bearing fault diagnosis is proposed. Multi-kernel support vector machine is a powerful new tool for roller bearing fault diagnosis with small sampling, nonlinearity and high dimension. Chaotic particle swarm optimization is developed in this study to determine the optimal parameters for MSVM with high accuracy and great generalization ability. Moreover, the feature vectors for fault diagnosis are obtained from vibration signal that preprocessed by time-domain, frequency-domain and empirical mode decomposition (EMD) and the typical manifold learning method LTSA is used to select salient features. The experimental results indicate that this proposed approach is an effective method for roller bearing fault diagnosis, which has more strong generalization ability and can achieve higher diagnostic accuracy than that of the single kernel SVM or the MSVM which parameters are randomly extracted.

Particle swarm optimization with time-varying acceleration coefficients for the multidimensional knapsack problem

The multidimensional knapsack problem (MKP) is a difficult combinatorial optimization problem, which has been proven as NP-hard problems. Various population-based search algorithms are applied to solve these problems. The particle swarm optimization (PSO) technique is adapted in our study, which proposes two novel PSO algorithms, namely, the binary PSO with time-varying acceleration coefficients (BPSOTVAC) and the chaotic binary PSO with time-varying acceleration coefficients (CBPSOTVAC). The two proposed methods were tested using 116 benchmark problems from the OR-Library to validate and demonstrate the efficiency of these algorithms in solving multidimensional knapsack problems. The results were then compared with those in the other two existing PSO algorithms. The simulation and evaluation results showed that the proposed algorithms, BPSOTVAC and CBPSOTVAC, are superior over the other methods according to its success rate, mean absolute deviation, mean absolute percentage error, least error, and standard deviation.

Predictive modeling of surface roughness in lenses precision turning using regression and support vector machines

Slow tool servo (STS) turning is superior in machining precision and in complicated surface. However, STS turning is a complex process in which many variables can affect the desired results. This paper focuses on surface roughness prediction in lenses STS turning. An exponential model, based on the five main cutting parameters including tool nose radius, feed rate, depth of cut, C-axis speed, and discretization angle, for surface roughness prediction of lenses is developed by means of orthogonal experiment regression analysis. Meanwhile, a prediction model of surface roughness based on least squares support vector machines (LS-SVM) with radial basis function is constructed. Orthogonal experiment swatches are studied, and chaotic particle swarm optimization and leave-one-out cross-validation are applied to determine the model parameters. The comparison of LS-SVM model and exponential model is also carried out. Predictive LS-SVM model is found to be capable of better predictions for surface roughness and has absolute fraction of variance R 2 of 0.99887, the mean absolute percent error e M of 8.96 %, and the root mean square error e R of 10.68 %. The experimental results and prediction of LS-SVM model show that effects of tool nose radius and feed rate are more significant than that of depth of cut on surface roughness of lenses turning.

Cognitive Radio Decision Engine Based on Binary Chaotic Particle Swarm Optimization

A novel Cognitive Radio (CR) decision engine based on Binary Chaotic Particle Swarm Optimization (BCPSO) is presented to enable robust and spectrally-efficient transmission in this paper. The BCPSO algorithm is used to adjust transmitter parameters so that the proposed CR decision engine can adjust the transmission scheme to adapt to the instantaneous channel condition. The obtained results in the Orthogonal Frequency Division Multiplexing (OFDM) system are compared with those of Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO) algorithm and the superiority of the proposed algorithm in convergence and accuracy over other algorithms is demonstrated. The proposed CR decision engine can select the optimal transmission scheme in real time and have the versatility for multiple applications with different Quality of Service (QoS) requirements.

Smith Prediction Monitor AGC System Based on CPSO Self-Tuning Pi Control

In accordance with the feature of pure delay in monitor AGC system for hot rolling mill, a new self-tuning PI Smith prediction controller based on chaotic particle swarm optimization (CPSO) is developed. The algorithmic principle and design method of new controller are given. Based on the typical monitor AGC model of tandem hot mill, the analysis of dynamic performance for traditional PI Smith prediction controller and CPSO self-tuning PI Smith prediction controller is done by MATLAB. The simulation results indicate that CPSO self-tuning PI Smith prediction controller has faster response and higher static accuracy than traditional PI Smith prediction controller.

Study on Optimal Scale and Layout of Electric Vehicle Charging Stations Based on Chaotic PSO Algorithm

The index of investment cycle cost (ICC) and users convenience of electric vehicles are presented. Based on the aim to minimizing investing unit and user fees, fix a charging station model in scales and layouts. ICC consist initial investments, cost of network losses and investments for new transmission lines. Convenience is measured by electrical power consumption expense from users to the charging station. Adding constraint of grid security and economical efficiency, the optimization aim function was set which comprehensively considers investors and users cost and operation of power grid. Chaos Particle Swarm Optimization Algorithm (CPSO) was used to settle it. By empirical study of certain planning area, the proposed model and algorithm are proved to be scientific and effective.

Digital Circuit Design using Chaotic Particle Swarm Optimization Assisted by Genetic Algorithm

Optimal digital circuit design could be very beneficial for reducing the expenses of electronic devices. But since it is a complicated problem in general, utilization of sophisticated algorithm for this task seems necessary. In this paper a new method based on combining particle swarm optimization, genetic algorithm and chaotic search is proposed for digital circuit design. The inclusion of wiring minimization in objective function is also proposed and investigated.

UPFC Device Application on Power System Oscillations to Improve the Damping Performance

UPFC is considered as an important modern device in the flexible ac transmission systems family that provides the controllability and flexibility for transmission lines. It is also capable of enhancing the stability of the power system by the addition of a supplementary damping controller, which can be installed on any control channel of the UPFC inputs to implement the task of power oscillation damping controller. This paper presents the application of UPFC to enhance damping of low frequency oscillations by the simultaneous coordinated design between power system stabilizer and different UPFC supplementary damping controller in order to identify the design that provided the most robust damping performance in a single machine infinite bus. The parameters of the damping controller were tuned in the individual and coordinated design by using a chaotic particle swarm optimization algorithm that optimized the given eigenvalue-based objective function. The results analysis reveals that the proposed coordinated designs have high ability in damping Low-frequency oscillations and improve the system damping over their individual control responses. In addition, the coordinated design PSS & δE provides superior performance in comparison to the all coordinated designs.