Weight Particle Swarm Optimization Research Articles

A new localization method based on improved particle swarm optimization for wireless sensor networks

Wireless sensor network (WSN) node localisation technology based on received signal strength indication (RSSI) is widely used as it does not need additional hardware devices. The ranging accuracy of RSSI is poor, and the particle swarm optimisation (PSO) algorithm can effectively improve the positioning accuracy of RSSI. However, the particle swarm diversity of the PSO algorithm is easy to lose quickly and fall into local optimal solution in the iterative process. Based on the convergence conditions and initial search space characteristics of the PSO algorithm in WSN localisation, an improved PSO algorithm (improved self-adaptive inertia weight particle swarm optimisation [ISAPSO]) is proposed. Compared with the other two PSO location estimation algorithms, the ISAPSO location estimation algorithm has good performance in positioning accuracy, power consumption and real-time performance under different beacon node proportions, node densities and ranging errors.

Collision-free motion planning for the litchi-picking robot

In unstructured environments, picking robots could collide with branches, thus reducing the success rate of picking. In this study, collision-free motion planning is performed in two steps based on the binocular stereo vision information of the environment. First, according to the 3D information collected in the stereo vision environment, the improved adaptive weight particle swarm optimization (APSO) algorithm is adopted to solve the inverse kinematics problem of robots and obtain the collision-free picking posture. Second, to address the limitations of the Bi-RRT algorithm in a high-dimensional environment, such as randomness and slow convergence speed, the target gravity concept and adaptive coefficient adjustment method are introduced to the Bi-RRT algorithm, which is called the AtBi-RRT algorithm. The AtBi-RRT algorithm is used to determine the collision-free path of the robot. Simulation results indicate that the APSO algorithm can swiftly obtain the proper collision-free picking posture, and the average path-determination time of the AtBi-RRT algorithm is 4.24 s. The success rate of path determination is 100% for the laboratory’s picking scene. Experimental results verify that the proposed collision-free motion-planning method allows the picking robot to avoid obstacles in the workspace and efficiently complete the picking task.

Solving Economic Load Dispatch Problem UsingParticle Swarm Optimization Technique

Economic load dispatch (ELD)is one of the important problems ofpower system operation. Conventional methods like Lambda iteration methodare not efficientfor complex ELD problems. Particle swarm optimization is preferred in ELD problem due to its high performance.The Inertia Weight PSO and Constriction Factor PSO algorithms are performed on threeunit and sixunit systems. The analysis of ELD problem is performed by Conventional method and PSO method. In this paper,losses are neglected in the ELD problem. PSO algorithm obtains the best solution forELD problem.

Reliability prediction-based improved dynamic weight particle swarm optimization and back propagation neural network in engineering systems

Aiming at the problem of low accuracy of reliability prediction, a back propagation neural network (BPNN) model is developed. In the process of reliability prediction, a dynamic weight particle swarm optimization-based sine map (SDWPSO) method including a novel inertial weight update strategy is developed. This new strategy introduced a linear decreasing parameter in the sine-map, which enables particles to perform a fine search at a very low speed in the later stage of the search and greatly improves the convergence speed of the algorithm. Furthermore, a hybrid model named SDWPSO-BPNN is created to improve the reliability prediction accuracy in engineering systems. The proposed SDWPSO approach is compared with four algorithms using fourteen benchmark functions to verify the effectiveness. The experimental results indicate that SDWPSO has a better search ability than the other algorithms. Then, the hybrid SDWPSO-BPNN is applied to predict the reliability of turbocharger and industrial robot systems, respectively. The obtained results manifest that the SDWPSO-BPNN is more powerful than that of SVM and ANN methods for reliability prediction in engineering.

Parameter identification of Hammerstein–Wiener nonlinear systems with unknown time delay based on the linear variable weight particle swarm optimization

This paper deals with the parameter estimation of Hammerstein–Wiener (H–W) nonlinear systems which have unknown time delay. The linear variable weight particle swarm method is formulated for such time delay systems. This algorithm transforms the nonlinear system identification issue into a function optimization issue in the parameter space, then utilizes the parallel searching ability of the particle swarm optimization and the iterative identification technique to realize the simultaneous estimation of all parameters and the unknown time delay. Finally, parameters in the linear submodule, nonlinear submodule and the time delay are separated from the optimum parameter. Moreover, two illustrative examples are exhibited to evaluate the effectiveness of the proposed method. The simulation results demonstrate that the derived method has fast convergence speed and high estimation accuracy for estimating H–W systems with unknown time delay, and it is applied to the identification of the bed temperature systems.

Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries

Capacity estimation is an essential task for battery manage systems to ensure the safety and reliability of lithium-ion batteries. Considering the uncertainty of charging and discharging behavior in practical usage, this paper presents a one-dimensional convolution neural network (1D CNN)-based method that takes random segments of charging curves as inputs to perform capacity estimation for lithium-ion batteries. To improve the robustness and accuracy of the proposed 1D CNN network, a linear decreasing weighted particle swarm optimization algorithm is utilized to optimize the partial hyperparameters of neural network. Experimental data from two sets of batteries with different nominal capacities are employed for verification purpose. It is proved that the proposed method is feasible to provide accurate estimations on capacity degradation for both kinds of batteries. Furthermore, effects of length and relative position of segments on the capacity estimation are also investigated. The analysis results show that a more precise estimation of the battery capacity is prone to be obtained from the segment with a longer length or lower initial SOC.

Research on vehicle stability region under critical driving situations with static bifurcation theory

A strategy for solving the static bifurcation points of the 5DOF vehicle nonlinear system is proposed to research the stability region of the system. The bifurcation characteristics of the system is changed by the coupling of the steering and the engine braking torques under the high-speed emergency steering conditions, and the corresponding vehicle stability region must be redefined. The stability region of the vehicle which is influenced by the engine braking torque is determined with the static bifurcation theory, and the equilibrium points of the 5DOF vehicle system are solved with the phase space method and the random weight particle swarm optimization (RWPSO) algorithm. The vehicle stability is verified with different initial longitudinal speeds and different steering angles, and the simulation results validate the effectiveness of the stability region under the critical driving conditions. The study is conducive to the development of the active safety control systems and the application of nonlinear system dynamics in the automotive field. Furthermore, it provides the theoretical support for the application of the vehicle-handling stability.

Transmission congestion management considering voltage stability margin

This paper presents a solution for the transmission congestion management considering voltage stability issues using optimal generation rescheduling. While practicing congestion management using optimization techniques, the control variables remain under their upper or lower limits but it may lead to the lowered level of voltage security after optimization. To counterbalance this adverse effect, a modified objective function has been used. The reactive power generation rescheduling and reactive support from capacitors have been incorporated along with active power generation rescheduling to manage congestion as well as to improve the network voltage stability margin. The Random Inertia Weight Particle Swarm Optimization (RANDIW-PSO) algorithm has been employed in this paper to obtain optimized solutions. The proposed methodology is tested on the New-England test system for different realistic scenarios. The results confirm a noteworthy decline in congestion cost along with the improvement in network voltage stability margin. Moreover, system performance has been improved in terms of system power losses, increased reactive power reserve at generators and voltage profile.

Research on identification of unbalance parameters of rotor with multi-plane using improved particle swarm optimization

Based on Bentley RK4 test rig, the method of nonlinear particle swarm optimization (PSO) is used to solve inverse problem instead of previous methods, which reduces identification complexity and avoids the ill posed problem caused by the singular equation in the process of solving. This paper realized model-based standard particle swarm optimization (SPSO) algorithm after modelling of the finite element model of rotor system and creating the optimization variables and individual fitness function, and then established asynchronous adaptive particle swarm optimization with optimizing speed weight and search scope. In order to further improve algorithm accuracy, chaos weighted particle swarm optimization and double chaos particle swarm optimization (DCPSO) are established. The simulation results of four methods show that DCPSO can realize accurate identification of unbalanced parameters with an average error of 2.86%. The effectiveness and rationality of this algorithm are verified from the speed of 240–3000 r/min. Particularly, at 2040 r/min, the percentage of amplitude decrease in two measuring points is 94.07% and 95.93%, which has achieved excellent vibration suppression effect.

Game algorithm based on link quality: Wireless sensor network routing game algorithm based on link quality

Aiming at the problems of low data transmission efficiency and uneven energy consumption caused by unreliable link communication in the routing process of wireless sensor networks, this article designs a routing game algorithm based on link quality. In this article, the index for evaluating link quality is defined first. Then, the link quality, node residual energy, and minimum jump transmission strategy are integrated into the utility function to establish a game model to determine the best next hop transmission node. Finally, the routing optimal transmission path is obtained according to the analysis of the existence of Nash equilibrium in the game. In the simulation experiment, the influence of the change of link quality parameters on the performance of the algorithm is analyzed, and the proposed algorithm is compared with non-linear weight particle swarm optimization (NWPSO) algorithm and Low Energy Adaptive Clustering Hierarchy-Improvement (LEACH-IMPT) algorithm in three aspects: the number of surviving nodes, network lifetime, and network energy consumption. The results show that the network lifetime of this method is 16.8% longer than that of LEACH-IMPT algorithm and 7.5% longer than that of NWPSO algorithm. This shows that the algorithm can effectively balance the network energy consumption and prolong the network life cycle. In addition, according to the routing path obtained in the simulation experiment, the optimality of its link quality is verified in the real experimental environment, and the experimental results prove the feasibility of the method in this article in practice.

Study on conical source transient electromagnetic based on AWPSO algorithm

The transient electromagnetic conical field source device can effectively reduce the inductance between the coils and improve the resolution of the small device to detect the shallow underground layer. However, the conventional inversion method requires an initial model and has the disadvantage of low inversion precision. Combined with the results of previous studies, this paper proposed an adaptive weighted particle swarm optimization (AWPSO) algorithm based on Sigmoid function through the analysis and research of PSO, which solved the problem of artificial initial model and low precision of inversion results. By calculating the test function, the algorithm can effectively improve the searching ability of particle swarm optimization algorithm. Several different typical geoelectric models are designed, and the algorithm is applied to data inversion of transient electromagnetic geoelectric model. Combined with the measured data of a hot spring practice area, the inversion calculation and interpretation of the sounding data were carried out, and the inversion calculation results were confirmed by the geological and hydrological data. The results show that the proposed particle swarm optimization algorithm has a high inversion fitting degree to the theoretical model and the measured data, achieves the separation of the apparent resistivity anomalies in the shallow subsurface, and improves the resolution and interpretation accuracy of the measured data in the conical field source transient electromagnetic method.

Improved Metaheuristic Optimization Algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction

As carriers of green energy, proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells are complex and nonlinear multivariate systems. For simulation analysis, optimization control, efficacy prediction, and fault diagnosis, it is crucial to rapidly and accurately establish reliability modules and extract parameters from the system modules. This study employed three types of particle swarm optimization (PSO) algorithms to find the optimal parameters of two energy models by minimizing the sum squared errors (SSE) and roots mean squared errors (RMSE). The three algorithms are inertia weight PSO, constriction PSO, and momentum PSO. The obtained calculation results of these three algorithms were compared with those obtained using algorithms from other relevant studies. This study revealed that the use of momentum PSO enables rapid convergence (under 30 convergence times) and the most accurate modeling and yields the most stable parameter extraction (SSE of PEMFC is 2.0656, RMSE of PV cells is 8.839 · 10−4). In summary, momentum PSO is the algorithm that is most suitable for system parameter identification with multiple dimensions and complex modules.

Feature Extraction based Breast Cancer Detection using WPSO with CNN

The cancer reports of the past few years in India says that 30% cases have breast cancer and moreover it may increase in near future. It is added that in every two minutes, one woman is diagnosed and one expires in every nine minutes. Early diagnosis of cancer saves the lives of the individuals affected. To detect breast cancer in early stages, micro calcifications is considered as one key symptom. Several scientific investigations were performed to fight against this disease for which machine learning techniques can be extensively used. Particle swarm optimization (PSO) is recognized as one among several efficient and promising approach for diagnosing breast cancer by assisting medical experts for timely and apt treatment. This paper uses weighted particle swarm optimization (WPSO) approach for extracting textural features from the segmented mammogram image for classifying micro calcifications as normal, benign or malignant thereby improving the accuracy. In the breast region, tumour part is extracted using optimization methods. Here, Convolutional Neural Networks (CNNs) is proposed for detecting breast cancer which reduces the manual overheads. CNN framework is constructed for extracting features efficiently. This designed model detects the cancer regions in mammogram (MG) images and rapidly classifies those regions as normal or abnormal. This model uses MG images which were obtained from various local hospitals.

Wind Turbine Bearing Incipient Fault Diagnosis Based on Adaptive Exponential Wavelet Threshold Function With Improved CPSO

Signal processing is of vital importance to the incipient fault diagnosis and the safety running of wind turbines. To adaptively eliminate noise and retain the underlying fault characteristic signal, an adaptive exponential wavelet threshold denoising method based on chaotic dynamic weight particle swarm optimization with sigmoid-based acceleration coefficients (SBAC-CDWPSO) is proposed in this paper. Firstly, a high-order continuous differentiable adaptive exponential threshold function (AETF) based on stein unbiased risk estimation is put forward to improve the defects of the traditional threshold functions. Secondly, the sine map and the sigmoid-based acceleration coefficients are applied for the velocity updating mechanism in particle swarm optimization (PSO). Meanwhile, the dynamic weight, the acceleration coefficient and the best-so-far position are introduced to update the new position with the previous position and the velocity in PSO. Moreover, the gaussian mutation strategy is added, which can effectively maintain the diversity of the swarm and get rid of local optimization. Thirdly, the SBAC-CDWPSO is used to optimize the threshold iteration process in AETF, which can greatly improve the iteration speed of the optimal threshold, and enhance the noise reduction effect. Experimental results showed that the signal-to-noise ratio of our proposed method was higher and the root mean square error was smaller comparing with the pre-existing algorithms. Moreover, wind turbine generator bearing fault diagnosis classification results illustrated that the fault diagnosis rate of the proposed denoising algorithm was up to 96.67%, indicating that the proposed method has great potential in the incipient fault diagnosis of wind turbine bearings.

Adaptive inertia weight particle swarm optimisation for a multi-objective capacitated vehicle routing problem with time window in air freight forwarding

Adaptive inertia weight particle swarm optimisation for a multi-objective capacitated vehicle routing problem with time window in air freight forwarding

A Flexible Vibration Probe Based on PZT Low-Frequency Drive Mode

Precision contact probes have a huge demand in industrial manufacturing due to their high accuracy. However, the passive measurement mode of the existing probe easily causes wear of the probe ball and loss of accuracy during continuous contact scanning. In this paper, a PZT(piezoelectric actuators)-driven flexible probe is proposed with the dynamic time-domain function of the system response from low frequency to high drive frequency. The low-frequency measurement mode is established based on the linear response of the triangle wave. In order to avoid the trajectory nonlinearity of the stylus ball driven by PZT, the nonlinear mapping of the PZT hysteresis model is constructed by the BP neural network, and the linearly weighted particle swarm optimization (PSO) algorithm is used to realize the suppression of the response nonlinearity. Finally, the measurement experiment of the PZT-driven probe is conducted to reveal the measurement characteristics of low-frequency mode as well as measurement capability compared with commercial sensors.

Design and Optimization of a Hybrid Excitation System for Magnetically Driven Rotating DC Arc Plasma Generators

To eliminate the disadvantages of both the uncontrollability in the magnetic field of a permanent magnet excitation system and the high energy consumption of an electric excitation system in a traditional magnetically rotating arc plasma generator, a novel topology of a hybrid permanent magnet and electrically excited coil system is proposed. The proposed system will generate a large enough magnetic field to drive the arc rotation with a minimum consumption of materials and electric energy in the normal operation duty, and will guarantee a nearly zero magnetic field in the arc triggering stage. To optimize the hybrid excitation system, a comprehensive analysis and an optimization methodology; by combining finite element analysis, the moving the least squares approximation and an adaptive weighted particle swarm optimization, are proposed. Finally, a prototype hybrid excitation system is optimized with promising results in views of both saving a huge amount of electric power consumptions and ensuring a nearly zero magnetic field in the arc triggering.

A detailed study about CDW-PSO, BWO and GM-CPSO methods on continuous function optimization

Gauss map based chaotic particle swarm optimization (GM-CPSO) is a state-of-the-art method involving the necessary chaotic map for PSO and has proved itself in global optimization, hybrid classifier design, etc. GM-CPSO can outperform recent techniques such as chaotic dynamic weight PSO (CDW-PSO), outclassing 20 optimization methods. However, the behavior of GM-CPSO on continuous characterized functions is unknown. As the main aim, this paper comprehensively determines the performance of GM-CPSO specifically on continuous function optimization. Black widow optimization (BWO) includes a non-stable population size that cannot be fixed. In BWO, the population can increase without any intervention during iterations, which prevents an objective comparison of the method with other methods. Thus, as the second aim, a new viewpoint on BWO population size selection is suggested for an objective comparison of the method. In various disciplines, stochastic optimization is inevitable to efficiently perform function optimization. Here, the necessary question concerns with which method the best convergence and performance can be achieved. As the third aim, we evaluate three state-of-the-art optimization methods to answer this question. To realize all of these aims, GM-CPSO is compared with CDW-PSO and BWO methods by using 10 continuous benchmark functions to perform a detailed comparison and reveal which one can achieve reliable scores on low-, middle-, and high-dimensional problems. Fitness-based comparisons, computation time analysis, and convergence-based evaluations are presented to determine the robustness of algorithms. As a result, GM-CPSO arises as the most remarkable method, especially for the middle-and high-dimensional continuous functions.

Prediction and optimization of isentropic efficiency of vortex pump under full operating conditions in Organic Rankine Cycle waste heat recovery system based on deep learning and intelligent algorithm

The isentropic efficiency of vortex pump in Organic Rankine Cycle (ORC) system has an important influence on the performance of the system. In this paper, vortex pump experimental data and deep learning are combined to construct an experimental data-driven isentropic efficiency prediction model of vortex pump under full operating conditions. The activation function and the number of hidden layer nodes in the model are filtered by nested screening technique. Through bilinear interpolation algorithm, the influence of vortex pump operation parameters on the isentropic efficiency is analyzed. In addition, the optimization boundary is selected in the four-dimensional space. Finally, the deep learning prediction model is combined with Linear Decreasing Inertia Weight Particle Swarm Optimization (LDIWPSO) to predict and optimize the maximum isentropic efficiency of vortex pump under full operating conditions. Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Coefficient of Determination (R-square) are combined to evaluate the prediction accuracy of the model. The prediction and optimization results show that the maximum isentropic efficiency of vortex pump can reach 22.89%. The combination of deep learning and LDIWPSO can predict and optimize the maximum isentropic efficiency for vortex pump with high precision. It also provides a reference for the maximum value of vortex pump isentropic efficiency in theoretical analysis and numerical simulation.

Hardware in the Loop Testing of Adaptive Inertia Weight PSO-Tuned LQR Applied to Vehicle Suspension Control

This paper presents an adaptive inertia weight particle swarm optimization (AIWPSO) employed for solving the multiobjective weight optimization problem of LQR applied for the vehicle active suspension system (ASS). To meet the competing control objectives of ASS including the ride comfort, road handling, and suspension travel, the state feedback controller design for ASS is formulated as an optimization problem and an improved PSO is employed for finding the optimal weights of the linear-quadratic regulator (LQR). Specifically, for solving the premature convergence of the particles and imbalance between exploration and exploitation capabilities of PSO, an adaptive inertia weight that updates the velocity of the particles based on the success rate is used. The efficacy of the AIWPSO-tuned LQR is experimentally tested on a quarter-car ASS plant using the hardware in loop (HIL) testing for an uneven road surface. Experimental results highlight that, compared to conventional PSO-tuned LQR, the proposed scheme can significantly minimize the vehicle body acceleration due to irregular road profile while guaranteeing the minimum tire friction for passenger safety. The ISO 2361-1 standards adopted to evaluate the ride and health criteria substantiate that the proposed scheme reduces the vibration dose value by 25.34% for a bumpy road profile. Moreover, the cumulative power spectral density (CPSD) of vehicle body acceleration assessed in both low- and high-frequency regions manifests the significant improvement in the ride comfort.