Chaotic Particle Swarm Optimization Research Articles

Parameter Identification of DFIG Converter Control System Based on WOA

The converter is an important component of a wind turbine, and its control system has a significant impact on the dynamic output characteristics of the wind turbine. For the double-fed induction generator (DFIG) converter, the control parameter identification method is proposed. In this paper, a detailed dynamic model of DFIG with the converter is built, and the trajectory sensitivity method is used to study the observation points that are sensitive to the change of control parameters as the observation quantity for control parameter identification; the Whale Optimization Algorithm (WOA) is used to study the converter control system parameters that dominate the output characteristics of DFIG in the dynamic full-process simulation. To validate the proposed method, four classical test functions are used to verify the effectiveness of the algorithm, and the control parameters are identified by setting a three-phase grounded short-circuit fault under maximum power point tracking (MPPT), and the identification results are compared with particle swarm optimization (PSO) and chaotic particle swarm optimization (CPSO) to show the superiority of the proposed method. The final results show that the proposed WOA can identify the control system parameters faster and more accurately.

Modeling for wind-thermal combined bidding considering bilateral tail information

The stochastic output of wind power will lead to the penalty of bidding deviation in the spot market and bring bidding risk, restricting the participation of wind power in market competition. The combined bidding strategy for multiple power generation stakeholders can help alleviate the output uncertainty of wind power, reducing the bidding deviation penalty. This paper aims at the modeling method for wind-thermal combined bidding in the spot power market. Conditional value at risk (CVaR) is used to describe the bidding risk of the combined bidding. To better reflect the bidding risk, the bilateral tail information of the upper and lower deviations is modeled according to the revenue function. The uncertainties of wind power output and the clearing price in the day-ahead market are considered, and the risk decision-making model for wind-thermal combined bidding is established. The model is solved by the chaotic particle swarm optimization algorithm with constraint handling (EDFC-PSO). The results show that the proposed modeling method can describe the risk indicators in detail, providing a better reference for market bidding. Also, the combination of the wind farm and the thermal power unit can reduce the risk of wind power in bidding and improve its profitability in the day-ahead market. Besides, the risk speculation of the wind farm can be found when facing the inevitable increased risks. It means the behaviors of the market participants can be better explained using the proposed method in this paper.

Reactor Temperature Prediction Method Based on CPSO-RBF-BP Neural Network

A neural network model based on a chaotic particle swarm optimization (CPSO) radial basis function-back propagation (RBF-BP) neural network was suggested to improve the accuracy of reactor temperature prediction. The training efficiency of the RBF-BP neural network is influenced to some degree by the large randomness of the initial weight and threshold. To address the impact of initial weight and threshold uncertainty on the training efficiency of the RBF-BP combined neural network, this paper proposes using a chaotic particle swarm optimization algorithm to correct the RBF-BP neural network’s initial weight and threshold, as well as to optimize the RBF-BP neural network to speed up the algorithm and improve prediction accuracy. The measured temperature of the reactor acquired by on-site enterprises was confirmed and compared to the predicted results of the BP, RBF-BP, and PSO-RBF-BP neural network models. Finally, Matlab simulation tests were performed, and the experimental data revealed that the CPSO-RBF-BP combined neural network model suggested in this paper had a root-mean-square error of 17.3%, an average absolute error of 11.4%, and a fitting value of 99.791%. Prediction accuracy and efficiency were superior to those of the BP, RBF-BP, and PSO-RBF-BP models. The suggested model’s validity and feasibility were established. The study findings may provide some reference values for the reactor’s temperature prediction.

Quartet trade-off for regional water resources allocation optimization with multiple water sources: A decentralized bi-level multi-objective model under hybrid uncertainty

Multiple uncertainties from water resource allocation have posed new challenges to balanced regional development. On the one hand, in the context of global water shortage crisis, groundwater, as an important water supply module, is facing severe risk of shortage. On the other hand, the regional authorities need to coordinate the contradictory water demand brought by the heterogeneous water use objectives from multiple water use levels, and these demands are also subject to complex uncertainties. How to mitigate the risks posed by uncertainties, ensure regional water security and balance participants, is attracting the keen attention of decision-makers around the world. Therefore, this study establishes a decentralized bi-level multi-objective regional water resources allocation (RWRA) optimization model under hybrid uncertainty, aiming at an optimal quartet trade-off of equity, sustainability, equilibrium, and efficiency. To deal with the hybrid uncertainty in the model, water demand is set as a random fuzzy number, and the groundwater constraint is combined with fuzzy chance-constrained programming (FCCP). Then, the decentralized bi-level multi-objective model is solved by the bi-level interactive method based on the satisfactory solution incorporated with the chaotic-particle swarm optimization (CPSO) algorithm. The proposed model and solution approach are then applied to the Dongjiang River Basin in Guangdong Province of China to verify the validity and utility of the model. The results show that (1) the model can effectively meet the different objectives requirements of regional authority and sub-areas; (2) groundwater constraint parameters has a significant impact on the model results, especially the Gini coefficient decreases by 13.6% when groundwater is used as a water source supplement; (3) from single-level model to decentralized bi-level model, the satisfactory balance of the two levels increases from 0.1457 to 0.793, which indicates that the decentralized bi-level model makes the two levels more balanced than the single-level model. Based on the results, some management recommendations for participants are finally provided.

Research on Water Resources Saving Based on Chaotic Particle Swarm Optimization

To improve the saving level of water resources, and the optimal allocation model of water resources is constructed, and the chaotic particle swarm optimization is established to solve the optimal allocation model. Firstly, the relevant researches are summarized, and the main contributions of this research are given. Secondly, optimal allocation conception of water resources is discussed, and then the corresponding optimal allocation model is established. Thirdly, the chaotic particle swarm optimization model is established, and the analysis procedure of the proposed algorithm is designed. Finally, optimal allocation analysis of water resources is carried out, and optimal allocation plans of water resources are confirmed, results show that the proposed optimal model and its solving algorithm can effectively obtain the best effect.

Optimization of Support Vector Machines for Prediction of Parkinson’s Disease

Abstract As in all fields, technological developments have started to be used in the field of medical diagnosis, and computer-aided diagnosis systems have started to assist physicians in their diagnosis. The success of computer-aided diagnosis methods depends on the method used; dataset, pre-processing, post-processing, etc. differ according to the processes. In this study, parameter optimization of support vector machines was performed with four different methods currently used in the literature to assist the physician in diagnosis. The success of each method was tested on two different Parkinson’s datasets and the results were compared within themselves and with the literature. According to the results obtained, the highest accuracy rates vary depending on the dataset and optimization method. While Improved Chaotic Particle Swarm Optimization achieved high success in the first dataset, Bat Algorithm achieved higher success in the other dataset. While the successful results obtained are better than some studies in the literature, they are at a level that can compete with some studies.

Global-guidance chaotic multi-objective particle swarm optimization method for pneumatic suspension handling and ride quality enhancement on the basis of a thermodynamic model of a full vehicle

In this paper, the parameters of the validated suspension system model of a full-vehicle are tuned through design sensitivity analyses, and then Multi-Objective Particle Swarm Optimization (MOPSO) is used to enhance vehicle ride comfort, which is the vertical whole-body vibrations, and handling features, that is, roll motion and road holding, simultaneously. The parameters of this thermodynamic-based pneumatic suspension system model are comprised of the air spring reservoir volume, orifice resistance, initial volume, and pressure of the pneumatic springs. To enhance the convergence rate, computational times, and diversity of the swarm particles, we have incorporated chaotic dynamics into the MOPSO using the Logistic Map chaotic method to initialize the population and also employed the leader-based global guidance techniques to conduct the potential solutions in each iteration. The analysis of the proposed modeling and optimization results show that the suspension system has been reasonably boosted in terms of vehicle handling and ride comfort. Quantitatively, the RMS acceleration and pitch angle has been reduced by about 71% and 57%, respectively, showing a substantial improvement in passenger comfort. Furthermore, the proposed approach caused an increase in tire road-holding force by about 148% and a reduction of roll angle by 33% which results in an enhancement in vehicle handling, boosting vehicle driving safety.

Shearer reliability prediction using support vector machine based on chaotic particle swarm optimization algorithm

Shearer reliability prediction using support vector machine based on chaotic particle swarm optimization algorithm

WACPN: A Neural Network for Pneumonia Diagnosis.

Community-acquired pneumonia (CAP) is considered a sort of pneumonia developed outside hospitals and clinics. To diagnose community-acquired pneumonia (CAP) more efficiently, we proposed a novel neural network model. We introduce the 2-dimensional wavelet entropy (2d-WE) layer and an adaptive chaotic particle swarm optimization (ACP) algorithm to train the feed-forward neural network. The ACP uses adaptive inertia weight factor (AIWF) and Rossler attractor (RA) to improve the performance of standard particle swarm optimization. The final combined model is named WE-layer ACP-based network (WACPN), which attains a sensitivity of 91.87±1.37%, a specificity of 90.70±1.19%, a precision of 91.01±1.12%, an accuracy of 91.29±1.09%, F1 score of 91.43±1.09%, an MCC of 82.59±2.19%, and an FMI of 91.44±1.09%. The AUC of this WACPN model is 0.9577. We find that the maximum deposition level chosen as four can obtain the best result. Experiments demonstrate the effectiveness of both AIWF and RA. Finally, this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models. Our model will be distributed to the cloud computing environment.

Multi-agent-based collaborative regulation optimization for microgrid economic dispatch under a time-based price mechanism

• This paper provides a multi-agent based coordinated dispatch strategy for the economic dispatch of the microgrid under a time-based price mechanism. • We develop a microgrid optimization model for the microgrid operation process, which includes battery regulation and user satisfaction. • The established optimization model is solved using a MACPSO algorithm, and the agent communication mechanism in the microgrid is examined. • The performance comparison using different algorithms (PSO, IPSO, MAPSO and MACPSO) has been presented. • The proposed multi-agent coordinated dispatch strategy is validated by designing a multi-agent system simulation environment and using the Java agent development (JADE) framework. The economic optimal dispatch of a microgrid is a challenging task with significant economic and social implications. Under a time-based price mechanism, this paper proposes a multi-agent-based coordinated dispatch strategy for the microgrid's economic dispatch. The information between the agents in the microgrid can be fed back in time to reduce microgrid operation failures by coordinated control between the demand-side load, the battery, and the power generation side micro-sources. The output of other micro-sources is then coordinated using an appropriate battery charging and discharging plan based on the characteristics of the time-based electricity price mechanism. Following that, an economical microgrid operation model is established and solved using a multi-agent chaotic particle swarm optimization (MACPSO) algorithm, which considers user satisfaction. Finally, a multi-agent system (MAS) simulation environment is built using the Java agent development (JADE) framework. The proposed microgrid with multi-agent coordinated control can effectively improve its operation efficiency, according to a comparative analysis of examples.

Optimal Reactive Power Dispatch Using Improved Chaotic PSO Algorithm with the Wingbeat Frequency

Abstract The importance of reactive power to the economy and security of power systems cannot be overemphasized. For instance, Transmission losses increase when reactive power is unevenly distributed on transmission network; and power quality is affected as well. The cheapest way of reducing these transmission lines losses is via reactive power dispatch approach. This study therefore proposes an Improved Chaotic Particle Swarm Optimization algorithm (ICPSO) with the primary aim of reducing real power transmission line losses while adhering to system constraints. Although the traditional PSO has a fast convergence speed, it falls easily into local optimum and it is slow at the later stage of convergence. The ICPSO is proposed in this research to overcome these shortcomings. The approach combines PSO with chaotic map which increases particles’ diversity, allowing particles to explore the search region more; and a wingbeat frequency component which helps to sustain the rate of convergence of particles. MATPOWER 7.1 in MATLAB 2019a environment was utilized for the implementation. The purported algorithm was examined on IEEE14 and IEEE30 Test Beds respectively. When tried out on IEEE14 Test bed, real power loss was reduced from 13.393 MW to 12.260 MW; whereas real power transmission line loss was brought down from 17.557 MW to 15.977 MW when tried out on IEEE30 Test Bed. In terms of reducing real power transmission lines losses, the simulation results show that the proposed approach performs better when compared with other algorithms.

Optimal Power Scheduling of Hydropower with Co-Located Floating Solar and Wind Generation Using Stochastic Weight Tradeoff Chaotic Particle Swarm Optimization

The increased use of solar and wind power generation worldwide has elevated the frequency and voltage regulation concerns due to their intermittent nature. Installing floating solar PV panels (FPV) and wind (WT) farms, at the same location as hydroelectric power plants (HPPs), should present a much cheaper option than using a battery storage. However, the operating strategy for the new co-located power plant needs to be revised from the conventional HPP operation. Hence, a novel optimal power scheduling strategy for co-located FPV-WT-HPP power plant using stochastic weight tradeoff chaotic particle swarm optimization (SWTC-PSO) is proposed in this paper. The objective is to either maximize the upper reservoir water or minimize the spill water. Test results obtained on the system with five major HPPs, two thermal, and one combined-cycle power plants ascertain that the proposed method can generate a significant amount of reservoir water saving up to 30%, which can later be used in the high demand period. The solar and wind energy penetration can be increased by up to 28% without disturbing frequency and voltage regulation using the co-located approach. Finally, the proposed modified HPP controller that uses the feedback signals from FPV and WT, can smooth out the combined co-located plant power output, leading to a dispatchable and firm power generation solution.

Mix Ratio of High-strength Concrete Based on Chaotic Particle Swarm Optimization

The optimization of the mix ratio of high-strength and high-performance concrete needs to solve the variable design problem of many factors. The purpose of this paper is to study the optimal design of high-strength concrete mix proportions based on chaotic particle swarm optimization. This paper introduces the research background and significance of this paper, the research status of deep learning prediction, concrete compressive strength prediction and intelligent design of concrete mix ratio at home and abroad, and introduces the relevant theoretical basis, mainly including the concept of high strength concrete and chaotic particle swarm algorithm. A multi-objective optimization model for high-strength and high-performance concrete based on experimental data is proposed. Each part of the model is introduced in detail, and the system established by the intelligent design model of concrete mix ratio proposed in this paper is firstly introduced briefly, then the various functions of the system are explained, and finally the system is given method, and select a real building concrete production mix data set for testing. The optimized high-strength and high-performance concrete has higher strength and a slump of 216.4mm.

Variable Fractional-Order Equivalent Circuit Model for Lithium-Ion Battery via Chaotic Adaptive Fractional Particle Swarm Optimization Method

A variable fractional-order equivalent circuit model is proposed to accurately describe the dynamic characteristics of lithium-ion batteries (LIBs). Firstly, a fractional impedance model (FIM) is established, such that the fractional-order (FO) is a polynomial function of the LIB state of charge (SOC). Then, a chaotic adaptive fractional particle swarm optimization (CAFPSO) method is derived to identify the parameters of the FIM. Experiments reveal the reliability of the novel approach through the root-mean-squared error (RMSE) and the mean absolute error (MAE) of the LIB terminals voltage, yielding the values 8.99 mV and 4.56 mV, respectively. This translates into accuracy improvements of 22.5% and 34.4% for the particle swarm optimization (PSO) algorithm and 57.9% and 72.8% for the adaptive fractional particle swarm optimization (AFPSO) algorithm, respectively.

An intelligent multivariate block steganography framework with enhanced PSO based on chaos mapping

To safeguard private information, image steganography is extensively used. Research is focused on ways to enhance steganographic technologies so that they may increase compression ratio while maintaining steganography image integrity. Because of its essential qualities such as security, scalability, and robustness, Steganography is a preferred way of communicating protected secret information to prevent hacking and misuse. This proposed research offers a steganography approach based on Enhanced Chaotic Particle Swarm Optimization (ECPSO), which uses chaos theory to determine the optimal pixel positions in the cover picture to hide confidential information when keeping the steganography quality in the images. Both the cover and secret pictures are separated into blocks to increase hiding capacity, with each component storing a sufficient quantity of secret data by mapping the pixels. The suggested ECPSO-Stegano system has better results with the criteria of Mean Square Error (MSE) of 0.00018%, Peak-Signal-to-Noise-Ratio (PSNR) of 79.66%, Bit Error Rate (BER) of 0.45% in average, and Structural Similarity Index (SSI) of 0.98 in average for various input size. It’s also robust to statistical threats.

Fault Diagnosis in the Brushless Direct Current Drive Using Hybrid Machine Learning Models

The brushless direct current (BLDC) motor drive is gaining popularity due to its excellent controllability and high efficiency. This paper introduces a fault diagnosis method for open circuit (OC) and short circuit (SC) BLDC motor drives using a hybrid classifier with hybrid optimization. Features such as current, voltage, speed, and torque are considered as the training data. The features are extracted by discrete wavelet transform (DWT) and then employed to train the classifiers to distinguish between fault types and values of response parameters using the support vector machine and Naive Bayes classifier (SVM-NB). To further improve the performance of the system, hybrid chaotic particle swarm optimization (CPSO) algorithms and teaching-learning-based optimization (TLBO) are used. This method is capable of detecting and recognizing the type of faults in the BLDC motor. The developed approach is implemented on the MATLAB/SIMULINK for OC, SC, and no-fault conditions. These hybrid algorithms provide better performance compared to existing approaches with respect to sensitivity, accuracy, and specificity. This improved model achieves about 98.8% accuracy.

Multi-energy coordinated and flexible operation optimization and revenue reallocation models for integrated micro energy system considering seasonal and daily load characteristics of different buildings

For pursuing economic and environmental-friendly goals, this paper constructs a regional integrated micro energy system where electricity, heat, cooling, and gas energies are interacted and integrated among buildings. First, independent and collaborative operations of micro energy systems are proposed. Second, seasonal and daily power, heat, and cooling loads of a residential building, a business building, and a mall are depicted. Third, in order to minimize the operation cost, energy consumption and CO2 emission, a multi-energy coordinated flexible operation optimization model of integrated micro energy system is established, and the chaotic particle swarm optimization algorithm is applied to solve the optimization model. A benefit evaluation model is built based on perspectives of energy, economy, and environment to evaluate the optimization results. Then, Shapley method is improved by integrating it with cloud focus theory to more fairly allocate optimized benefits, and influencing indexes for adjusting the weights of participants are proposed. Finally, a case study is conducted. The results indicate that: (1) the integrated micro energy system enabled surplus energy inter-supply among the subsystems, thus realizing interconnection and energy complementarity between micro energy systems; (2) seasonal load characteristics showed the differences among the building subsystems, where the winter load showed more flexibility in energy conversion and inter-supply among the subsystems than the summer load; (3) the improved Shapley method are more effective and fairer for benefit allocation, based on different level of importance of participants; (4) Chaotic particle swarm optimization algorithm are more superior in terms of calculation efficiency and accuracy for optimization solutions.

Time Optimal Trajectory Planning Algorithm for Robotic Manipulator Based on Locally Chaotic Particle Swarm Optimization

Optimal trajectory planning is a fundamental problem in the area of robotic research. On the time-optimal trajectory planning problem during the motion of a robotic arm, the method based on segmented polynomial interpolation function with a locally chaotic particle swarm optimization (LCPSO) algorithm is proposed in this paper. While completing the convergence in the early or middle part of the search, the algorithm steps forward on the problem of local convergence of traditional particle swarm optimization (PSO) and improved learning factor PSO (IFPSO) algorithms. Finally, simulation experiments are executed in joint space to obtain the optimal time and smooth motion trajectory of each joint, which shows that the method can effectively shorten the running time of the robotic manipulator and ensure the stability of the motion as well.

Wind-field identification for parafoils based on deep Q-learning iterative inversion

Powered parafoils are flexible aircraft with high load capacities and low costs and are often used in long-term surveillance and airdrop missions. As a flexible aircraft, powered parafoil flights are easily affected by winds. However, wind-field identification is difficult for low-to-medium speed unmanned aerial vehicles (UAV), which makes it challenging for UAVs such as powered parafoils to perform targeted wind compensation. To observe and compensate for this, we must accurately identify the wind field for the powered parafoil. In this study, we designed a deep Q-learning iterative inversion (DQII) algorithm for wind-field identification to concretely analyze wind-field identification through model inversion. Using a deep Q-learning (DQ) inverting dynamic model, a powered parafoil can identify the wind field within 2 s. In addition, iterative chaotic particle swarm optimization was performed based on DQ inversion, which achieved a high-precision wind-field identification effect. Simulation experiments showed that, compared with the traditional dead reckoning (DR) algorithm, DQII can reduce the error by 80%–90%. In a powered parafoil homing experiment, DQII achieved a wind-field identification effect that was significantly better than that of the DR algorithm.

Chaotic self-governing particle swarm optimization for marine propeller design

Chaotic self-governing particle swarm optimization for marine propeller design