Search

The diverse engineering and scientific applications are stated through complex and high-order systems. The significant difficulties of these systems are the complications of modeling, analyzing, and controlling. It is easier to examine simpler models for more physical insights than more complex models and result in lower-ordering controllers that are easier to implement. The model order reduction (MOR) was used to simplify the computational difficulty of such complications and was later developed intensively for use with increasingly CDS. In this paper, a new Modified Chaos Particle Swarm Optimization (MCPSO) technique is employed to get a reduced-order model of a large scale system and design a Linear Quadratic Regulator (LQR) based controller. The mod uses the combination of advantages of basic PSO algorithms and chaotic algorithms. It becomes an excellent algorithm with fast convergence, few control parameters, simple execution, and avoidance of local extremes. In addition to combining the chaotic algorithm, CPSO also improved the weight parameter w, adjusting it to the dynamic attenuation direction. First, efficient reduced-order model parameters are obtained for original higher-order systems based on the MCPSO. Then linear quadratic regulator (LQR (controller parameters optimized for the reduced-order model. The goodness of the proposed method is evaluated through a numerical example. The experimental results indicate that the proposed technique’s reduced order model provides an excellent close approximation to the original system.

In order to solve the problem of non-optimal when Particle Swarm Optimization (PSO) optimizes least square support vector machine (LSSVM), a short-term load forecasting method based on Chaos theory is proposed.Firstly, chaos theory is introduced into the prediction model to improve the particle swarm algorithm, and then PSO combined with chaos theory is used to optimize the parameters of LSSVM. Finally, the method is applied to short-term load forecasting, and the forecasting results are obtained through Matlab simulation training. The experimental simulation shows that the method can not only reduce the possibility of the algorithm falling into local extremum, but also improve the learning ability, thus improving the accuracy of prediction.

Uncertainty is critical to the tunnel design. In this study, a novel reliability-based design (RBD) method was developed by integrating the rock-support interaction and its uncertainties for rock tunnels. In this method, the rock-support interactions were analyzed based on a convergence-confinement method. Uncertainties were estimated by reliability analysis using Excel Solver. Chaotic particle swarm optimization (CPSO) was adopted to search the optimal tunnel design parameters based on the rock-support interaction analysis. The proposed method for estimating the reliability index and determining the tunnel support parameters was introduced in detail. To illustrate the proposed method, it was applied to two tunnels with rock-bolt and combined support systems. The results indicated that the method could obtain accurate solutions with a dramatically improved computing efficiency, guaranteeing the tunnel stability at the same time. The developed method provides an excellent way to deal with the uncertainty in tunnel design. It was proved to be an efficient and effective method for the support designs of rock tunnels.

This paper presents an approach to the design of an optimal control strategy for plug-in hybrid electric vehicles (PHEVs) incorporating Internet of Vehicles (IoVs). The optimal strategy is designed and implemented by employing a mobile edge computing (MEC) based framework for IoVs. The thresholds in the optimal strategy can be instantaneously optimized by chaotic particle swarm optimization with sequential quadratic programming (CPSO-SQP) in the mobile edge computing units (MECUs). The vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication are adopted in IoV to collect traffic information for a CPSO-SQP based optimization and transmit the optimized control commands to vehicle from MECUs. To guarantee real-time optimal performance, the communication delay in V2V and V2I is decreased via an alternative iterative optimization algorithm (AIOA) approach. The simulation results demonstrate the superior performance of the novel optimal control strategy for PHEV with 9% improvement, compared with the original strategy.

In this paper, we deal with the particle swarm optimization (PSO) which is one of metaheuristic methods for global optimization. In this paper, we propose a PSO method using a chaotic system, where...

Vibration signals contain abundant information which can reflect the running state of high-speed trains. Accurate vibration signal prediction can provide references for anomaly detection of the gearbox in high-speed trains. This paper develops a hybrid model combining ensemble empirical mode decomposition (EEMD) with auto regression (AR) and support vector regression (SVR) models. First, the EEMD method is applied to decompose the vibration acceleration signal of gearbox. Second, AR models are employed to predict the intrinsic mode functions and the outputs are aggregated as the final result of AR. Third, reconstruct phase space and establish SVR models to predict the components; The predictions are aggregated as the final result of SVR. Finally, the results predicted using the AR and SVR models are weighted and summed together, with the weights being optimized by the chaotic particle swarm optimization algorithm. The actual operation monitoring data are used to validate the hybrid model. Data analysis demonstrates that the proposed method has better approximation compared with the AR model, the SVR model and the RBF neural network model.

Transportation of electrification has become a hot issue in recent decades and the large-scale deployment of electric vehicles has yet to be actualized. This article proposes a powertrain parameter optimization design approach based on chaotic particle swarm optimization algorithm. To improve the driving and economy performance of pure electric vehicles, chaotic particle swarm optimization algorithm is adopted in this study to optimize principal parameters of vehicle power system. Vehicle dynamic performance simulations were carried out in the Cruise software, and the simulation results before and after optimization were compared. Simulation results show that optimized vehicles by chaotic particle swarm optimization can meet the expected dynamic performance and the driving range has been greatly improved. Meanwhile, it is also viable that the parameters of the optimal objective function can achieve the purpose of balancing the driving performance and economic performance, which provides a reference for the development of vehicle dynamic performance.

In recent years, researchers have worked to find multiple methods to overcome the classification problems they encountered. In this paper, both the Chaotic Particle Swarm Optimization (CPSO) algorithm are connected with the Adaptive Fuzzy Inference System (AFIS) model, through two stages, the first stage, CPSO algorithm is used to tune parameters of the fuzzy inference system model. In the second stage, the fuzzy inference system model is constructed according to the optimum parameters that are found by the CPSO algorithm. The proposed CPSO-AFIS algorithm demonstrated efficacy and efficiency compared to the standard algorithm Adaptive Neuro Fuzzy Inference System (ANFIS).

Aiming at the problem that the average temperature control system of nuclear reactor coolant under variable conditions is nonlinear and time-varying, and the traditional fixed model cannot meet the control requirements, an improved generalized predictive control method with adaptive identification of model parameters is proposed. First, the structure of the coolant average temperature model is determined through mechanism derivation, and the forgetting factor recursive least square algorithm is used to identify the model parameters online and in real time. Secondly, the chaotic particle swarm algorithm is used to optimize the control increment during rolling optimization. The simulation is carried out on the MATLAB platform, and the results show that the tracking ability of the average coolant temperature is significantly better than the traditional PI control under variable conditions.

Magnetically controlled shape memory alloy (MSMA) has a mechanical–electromagnetic inverse characteristic. Based on MSMA inverse characteristics, an identification algorithm on MSMA sensor model parameter is proposed in this paper. When removing the pre-pressure system, the improved sensor structure is proved to be more effectiveness through theoretical and experimental research. In the absence of pre-pressure, chaotic particle swarm optimization algorithm is used to identification each parameter of MSMA sensor model, the model parameter of the sensor is obtained under different experimental conditions. Comparison the experimental and calculated values of the induced voltage, the correctness of model parameter algorithm is verified by this method. The research work lays a foundation for theoretical research of MSMA sensor.

Taking full advantages of the L1-norm support vector machine and the L2-norm support vector machine, a new improved double regularization support vector machine is proposed to analyze the datasets with small samples, high dimensions and high correlations in the parts of the variables. A kind of smooth function is used to approximately overcome the disdifferentiability of the L1-norm and the steepest descent method is used to solve the model. But the parameters of this model bring some trouble about the accuracy of the experiments. By use of the characteristics of chaotic systems, we propose a chaotic particle swarm optimization to select the parameters in the model. Experiments show the improvement gains the better effects.

Considering the hub airports are traffic transfer points, the congestion is easily happened. The cost caused by congestion will rise significantly. The hub-and-spoke airline network optimization model with congestion cost is de- signed. In the actual operations of airlines, such problem is difficult to be solved by using the classical optimization meth- ods. This paper presents a Particle Swarm Optimization (PSO) algorithm. To improve the performance of standard PSO algorithm and avoid trapping into local excellent result, a chaos PSO algorithm of traffic volume multi-path assignment is presented. Empirical analysis shows that optimization design with congestion cost can avoid the excessive congestion phenomenon in the hub nodes. The proposed algorithm can solve the non-linear network optimization problem efficiently.

Municipal Solid Waste (MSW) is an increasing concern at any municipality in the world, and is one of the primary factors that contribute greatly to the rising of climate change and global warming. MSW collection and disposal especially in the context of developing countries are indeed the urgent requirements for the sustainable development of environment and landscape, which rule over the quality-of-life and life expectancy of human being. In this paper, we concentrate on MSW collection at Danang city, which is one of four largest municipalities in Vietnam having high quantity of the average waste load per person and is bearing negative impacts of climate change such as severe weather conditions and natural disasters as a result. A novel vehicle routing model for the MSW collection problem at Danang city is presented. A novel hybrid method between Chaotic Particle Swarm Optimization and ArcGIS is proposed to generate optimal solutions from the vehicle routing model of Danang. Experimental results on the real dataset of Danang show that the proposed hybrid method obtains better total collected waste quantity than the relevant ones including the manual MSW collection procedure that is currently applied at this city.

Aiming at the EEG signal radio frequency control problem of EEG signal radio control wheeled robot traversing multiple objective points, a new method of EEG signal radio frequency control that integrates the bi-objective chaotic particle swarm optimization (BCPSO) is put forward. This method transforms the selection of objective points into the EEG signal radio frequency control problem and makes optimization by using the ant colony algorithm. The EEG signal control function between the two objective points is defined, and the particle swarm optimization algorithm is applied for optimization. Given the premature phenomenon of particle swarm optimization algorithm, the inverse learning strategy is introduced into the particle swarm optimization algorithm, and the inertia weight and learning factor of the particle swarm optimization algorithm are improved. The performance test results show that the bi-objective chaotic particle swarm optimization algorithm can effectively prevent the premature particle phenomenon and improve the optimization capacity and stability of the particle swarm optimization algorithm.

Model combat information conditions are always uncertain and varied, the uncertain interval theory is therefore introduced into the research of multiple fighters suppressing the integrated air defense system (IADS) problem. Considering that the combat information conditions are uncertain intervals, the payoff function of the game for multiple fighters suppressing the IADS is modeled. Using the operation rules for interval numbers and the possibility degree, an improved chaotic particle swarm optimization (CPSO) is designed to solve the proposed model so as to obtain the optimal game solution. Comparison simulations are performed to analyze the influence of the weapons consumption and the distances of non-escaped zone and jamming on air combat result. Simulation results suggest that Nash equilibrium is achieved and verify the effectiveness of the proposed method.

This study evaluates additional chaotic maps combined with particle swarm optimization (PSO) to detect SNP barcodes using high-dimensional genomic data.We used nine chaotic maps to improve PSO and compared the searching ability amongst all versions of CPSO.The efficacy evaluation of both computational methods was based on the statistical values from the chi-square test (2).The results showed that chaotic maps could improve the searching ability of PSOs that have been trapped in a local optimum.Our results indicate that the Sinai chaotic map combined with PSO is more effective at detecting potential SNP barcodes in both the XOR and ZZ disease models. ObjectiveEvolutionary algorithms could overcome the computational limitations for the statistical evaluation of large datasets for high-order single nucleotide polymorphism (SNP) barcodes. Previous studies have proposed several chaotic particle swarm optimization (CPSO) methods to detect SNP barcodes for disease analysis (e.g., for breast cancer and chronic diseases). This work evaluated additional chaotic maps combined with the particle swarm optimization (PSO) method to detect SNP barcodes using a high-dimensional dataset. Methods and materialNine chaotic maps were used to improve PSO method results and compared the searching ability amongst all CPSO methods. The XOR and ZZ disease models were used to compare all chaotic maps combined with PSO method. Efficacy evaluations of CPSO methods were based on statistical values from the chi-square test (2). ResultsThe results showed that chaotic maps could improve the searching ability of PSO method when population are trapped in the local optimum. The minor allele frequency (MAF) indicated that, amongst all CPSO methods, the numbers of SNPs, sample size, and the highest 2 value in all datasets were found in the Sinai chaotic map combined with PSO method. We used the simple linear regression results of the gbest values in all generations to compare the all methods. Sinai chaotic map combined with PSO method provided the highest values (0.32 in XOR disease model and 0.04 in ZZ disease model) and the significant p-value (p-value

An evolutionary approach of parking space guidance based upon a novel Chaotic Particle Swarm Optimization (CPSO) algorithm is proposed. In the newly proposed CPSO algorithm, the chaotic dynamics is combined into the position updating rules of Particle Swarm Optimization to improve the diversity of solutions and to avoid being trapped in the local optima. This novel approach, that combines the strengths of Particle Swarm Optimization and chaotic dynamics, is then applied into the route optimization (RO) problem of parking lots, which is an important issue in the management systems of large-scale parking lots. It is used to find out the optimized paths between any source and destination nodes in the route network. Route optimization problems based on real parking lots are introduced for analyzing and the effectiveness and practicability of this novel optimization algorithm for parking space guidance have been verified through the application results.

Wide-area PSS (power system stabilizer) and HVDC (High voltage direct current) modulation are effective damping controllers in power system. With conventional parameter tuning method, the damping of the non-dominate modes would be impaired or new poor damping mode would be triggered. In this paper, the CPSO (chaotic particle swarm optimization) algorithm is used to tune the parameters of damping controller. The aim of the optimization is to provide desired damping ratio for all oscillation modes. A case study verifies the effectiveness of the proposed parameter optimization method.

A rational approximation method of the fractional-order derivative and integral operators is proposed. The turning frequency points are fixed in each frequency interval in the standard Oustaloup approximation. In the improved Oustaloup method, the turning frequency points are determined by the adaptive chaotic particle swarm optimization (PSO). The average velocity is proposed to reduce the iterations of the PSO. The chaotic search scheme is combined to reduce the opportunity of the premature phenomenon. Two fitness functions are given to minimize the zero-pole and amplitude-phase frequency errors for the underlying optimization problems. Some numerical examples are compared to demonstrate the effectiveness and accuracy of this proposed rational approximation method.

 Abstract—In this paper, an intelligent PID controller based on Chaotic Particle Swarm Optimization (CPSO) algorithm for Ball and Hoop system is designed. In this system, two goals are tracked; the first one implies on set-point tracking, and the second one includes both set-point tracking and disturbance rejection. The classical methods of PID tuning such as Ziegler-Nichols are based on trial and error, and generally, their responses have a high settling time and overshoot; however, the proposed CPSO-PID controller determines the parameters of PID controller automatically and intelligently by minimizing the integral absolute error (IAE). The simulation results on Ball and Hoop system show that the proposed CPSO-PID controller leads to superior performance compared to Ziegler-Nichols method in both set-point tracking and disturbance rejection in terms of rise time, settling time, maximum overshoot, and the integral of absolute error (IAE) performance criterion.

In this paper, An Intelligent PID Controller has been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared control signal (ITAESCS) for a DC motor. The parameters of PID controller, automatically and intelligently are determined by Chaotic Particle Swarm Optimization (CPSO) Algorithm. The experimental results demonstrate that the performance of proposed Intelligent CPSO-PID controller is superior to the conventional Ziegler-Nichols method in terms of settling time, maximum overshoot and ITAESCS.

Optimum prediction is a difficult problem, because there are no optimal models for all forecasting problems. In this paper, the authors attempt to find the high precision prediction for grey forecasting model (GM). Considering that chaotic particle swarm optimization algorithm (CPSO) will not get into local optimum and is easy to implement, the paper develops an approach for grey forecasting model, which is particularly suitable for small sample forecasting, based on chaotic particle swarm optimization and optimal input subset which is a new concept. The input subset of traditional time series consists of the whole original data, but the whole original does not always reflect the internal regularity of time series, so the new optimal subset method is proposed to better reflect the internal characters of time series and improve the prediction precision. The numerical simulation result of financial revenue demonstrates that developed algorithm provides very remarkable results compared to traditional grey forecasting model for small dataset forecasting.

Support Vector Machine (SVM) is a new modeling method. It has shown good performance in many field and mostly outperformed neural networks. The parameter selection should to be done before training SVM. Modified particle swarm optimization (POS) was adpoted to select parameters of SVM. It is shown by simulation that the modified POS algorithm can derive a set of optimal parameters of SVM. Compared with neural networks, SVM model possess some advantages such as simple structure, fast convergence speed with high generalization ability.

MEMS (Micro Electro Mechanical System) gyroscopes have been widely applied to various fields, but MEMS gyroscope random drift has nonlinear and non-stationary characteristics. It has attracted much attention to model and compensate the random drift because it can improve the precision of inertial devices. This paper has proposed to use wavelet filtering to reduce noise in the original data of MEMS gyroscopes, then reconstruct the random drift data with PSR (phase space reconstruction), and establish the model for the reconstructed data by LSSVM (least squares support vector machine), of which the parameters were optimized using CPSO (chaotic particle swarm optimization). Comparing the effect of modeling the MEMS gyroscope random drift with BP-ANN (back propagation artificial neural network) and the proposed method, the results showed that the latter had a better prediction accuracy. Using the compensation of three groups of MEMS gyroscope random drift data, the standard deviation of three groups of experimental data dropped from 0.00354°/s, 0.00412°/s, and 0.00328°/s to 0.00065°/s, 0.00072°/s and 0.00061°/s, respectively, which demonstrated that the proposed method can reduce the influence of MEMS gyroscope random drift and verified the effectiveness of this method for modeling MEMS gyroscope random drift.

Path planning plays an extremely important role in the design of UCAVs to accomplish the air combat task fleetly and reliably. The planned path should ensure that UCAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. In this paper, a Fitness-scaling Adaptive Chaotic Particle Swarm Optimization (FAC-PSO) approach was proposed as a fast and robust approach for the task of path planning of UCAVs. The FAC-PSO employed the fitness-scaling method, the adaptive parameter mechanism, and the chaotic theory. Experiments show that the FAC-PSO is more robust and costs less time than elite genetic algorithm with migration, simulated annealing, and chaotic artificial bee colony. Moreover, the FAC-PSO performs well on the application of dynamic path planning when the threats cruise randomly and on the application of 3D path planning.

Currently, the method of optimizing the wavelet neural network with particle swarm plays a certain role in improving the convergence speed and accuracy; however, it is not a good solution for problems of turning into local extrema and poor global search ability. To solve these problems, this paper, based on the particle swarm optimization, puts forward an improved method, which is introducing the chaos mechanism into the algorithm of chaotic particle swarm optimization. Through a series of comparative simulation experiments, it proves that applying this algorithm to optimize the wavelet neural network can successfully solve the problems of turning into local extrema, and improve the convergence speed of the network, in the meantime, reduce the output error and improve the search ability of the algorithm. In general, it helps a lot to improve the overall performance of the wavelet neural network.