Simulated Annealing Particle Swarm Optimization Research Articles

Vibration control by using active electromagnetic shunt damper

It is well-known that the traditional electromagnetic shunt damping (EMSD) techniques are limited by the damping force of electronic components and require a negative resistance (NR) shunt circuit to enhance performance. However, the NR shunt circuit could lead to the EMSD system being unstable. Addressing this, this study proposes an advanced control system that employs active control technology combined with EMSD for vibration control. We first developed a dimensionless mathematical model of the control system, which was then finely tuned using an adaptive simulated annealing particle swarm optimization algorithm. Subsequently, the relationship between control gain and optimal shunt circuit parameters was predicted using a BP neural network. Finally, the proposed Active-EMSD (AEMSD) was experimentally verified. The experimental results demonstrate that the proposed AEMSD not only surpasses traditional thresholds but also excels in isolating low-frequency vibrations. Compared to traditional EMSD, the proposed AEMSD showed improved effectiveness.

Simulated annealing-particle swarm optimization initial orbit determination method for too-short-arcs

The initial orbit determination (IOD) of a non-cooperative satellite through space-based optical angle-only measurement data is a fundamental task. However, due to the lack of relative distance information of the targets, traditional methods have problems with poor accuracy and robustness in orbit determination results. The application of particle swarm optimization algorithm for IOD under optical too-short-arcs (TSA) measurement data has achieved good results. However, there is still a problem of convergence to local extremum. This article proposes a simulated annealing-particle swarm optimization (SA-PSO) hierarchical algorithm, which combines the efficient searchability of particle swarm optimization with the fine searchability of simulated annealing algorithm to achieve robust and precise optimization of non-cooperative targets orbit elements. The experimental results show the SA-PSO algorithm significantly improves the accuracy of orbit elements prediction compared to the particle swarm optimization algorithm and proves its effectiveness.

CSAPSO-BPNN-Based Modeling of End Airbag Stiffness of Nursing Transfer Robot

The use of nursing transfer robots is a vital solution to the problem of daily mobility difficulties for semi-disabilities. However, the fact that care-receivers have different physical characteristics leads to force concentration during human–robot interaction, which affects their comfort. To address this problem, this study installs an array of double wedge-shaped airbags onto the end-effector of a robot, and analyses airbag mechanical properties. Firstly, this study performed the mechanical testing and data collection of the airbag, including its external load and displacement, at various gas masses. Then, the performance of the Back Propagation (BP) neural network is improved using chaos (C) theory and simulated annealing particle swarm optimization (SAPSO), resulting in the establishment of the CSAPSO-BP neural network. By this method, a fitting model is developed to determine the mechanical parameters of the wedge-shaped airbag stiffness, and the fitting relation of external load–displacement is obtained. Data analyses show that the wedge-shaped airbag stiffness increases quadratically, linearly, and with a constant rate as the gas mass increases. The airbag stiffness regulation and model describe its three distinct phases with quadratic, linear, and linear invariant characteristics as the gas mass changes. These findings contribute to the structural optimization of airbags.

Multiobjective-energy centric honey badger optimization based routing for wireless body area network

Wireless Body Area Network (WBAN) is an interconnection of tiny biosensors that are organized in/on several parts of the body. The developed WBAN is used to sense and transmit health-related data over the wireless medium. Energy efficiency is the primary challenges for increasing the life expectancy of the network. To address the issue of energy efficiency, one of the essential approaches i.e., the selection of an appropriate relay node is modelled as an optimization problem. In this paper, energy efficient routing optimization using Multiobjective-Energy Centric Honey Badger Optimization (M-ECHBA) is proposed to improve life expectancy. The proposed M-ECHBA is optimized by using the energy, distance, delay and node degree. Moreover, the Time Division Multiple Access (TDMA) is used to perform the node scheduling at transmission. Therefore, the M-ECHBA method is used to discover the optimal routing path for enhancing energy efficiency while minimizing the transmission delay of WBAN. The performances of the M-ECHBA are analyzed using life expectancy, dead nodes, residual energy, delay, packets received by the Base Station (BS), Packet Loss Ratio (PLR) and routing overhead. The M-ECHBA is evaluated with some classical approaches namely SIMPLE, ATTEMPT and RE-ATTEMPT. Further, this M-ECHBA is compared with the existing routing approach Novel Energy Efficient hybrid Meta-heuristic Approach (NEEMA), hybrid Particle Swarm Optimization-Simulated Annealing (hPSO-SA), Energy Balanced Routing (EBR), Threshold-based Energy-Efficient Routing Protocol for physiological Critical Data Transmission (T-EERPDCT), Clustering and Cooperative Routing Protocol (CCRP), Intelligent-Routing Algorithm for WBANs namely I-RAW, distributed energy-efficient two-hop-based clustering and routing namely DECR and Modified Power Line System (M-POLC). The dead nodes of M-ECHBA for scenario 3 at 8000 rounds are 4 which is less when compared to the dead nodes of EBR.

Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands

Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users’ dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified.

Novel Active Disturbance Rejection‐Based Sliding‐Mode Control for Permanent Magnet Linear Synchronous Motor Drives

Active disturbance rejection control (ADRC) has been widely used in the permanent magnet linear synchronous motor (PMLSM) drive system for its strong anti‐disturbance ability and fast response speed. However, a large number of adjustment parameters add difficulties to its application. To solve this problem, a novel sliding mode‐based active disturbance rejection control (SM‐ADRC) with autotuned parameters is proposed in this paper. In this control strategy, the SM is incorporated with extended state observer (ESO) and nonlinear state error feedback (NLSEF) to reduce the number of adjustment parameters. The simulated annealing particle swarm optimization (SAPSO) algorithm is carried out to achieve the optimal configuration and realize parameter self‐tuning. The experimental results of the PMLSM speed servo system demonstrate the proposed control method has better suppression of chattering effect, disturbance rejection ability and fast dynamic response. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Disassembly Sequence Optimization of Lithium Battery Pack Based on Priority Constraint Graph

With the rapid development of the new energy vehicle power industry, the power system of automobiles is about to usher in a wave of retirement. At present, most of power lithium battery packs are disassembled violently, which lack a reasonable disassembly order. This causes a waste of time and cost during the disassembly process. Some harmful parts will also pollute the environment. In order to improve the disassembly efficiency of retired lithium-ion battery packs, the disassembly sequence planning problem (DSP) based on priority constraint graph is studied in this paper. After analyzing disassembly characteristics, the priority constraint graph is constructed as the constraint matrix. Considering the disassembly efficiency, a mathematical model is established to optimize the direction and the number of tool changes. An improved Simulated Annealing Particle Swarm Optimization (SA-PSO) algorithm is designed, which is to update the disassembly sequence by the population update method. The particles are assisted to jump out of the local optimum by the Metropolis criterion of SA algorithm. An adaptive method is proposed for the PSO inertia weight ω. In order to verify the validity of the model and algorithm, an example is introduced to compare SA-PSO with PSO and SA algorithm. The results show that the improved algorithm performs well in the evaluation index of fitness. The convergence rate is fast and more stable.

Research on the Calculation Model and Control Method of Initial Supporting Force for Temporary Support in the Underground Excavation Roadway of Coal Mine

This paper proposes a temporary support system for improving the efficiency and safety of underground roadway excavation in coal mines. Firstly, this study establishes a calculation model for the initial supporting force of the excavation of roadway temporary support and a gray system-based automatic prediction model for the initial supporting force level, based on the mechanism of temporary support controlling the roof. These models enable the prediction of the required initial supporting force at different locations along the roadway’s temporary support area, thereby providing a basis for controlling the initial supporting force of the temporary support system. To achieve efficient and adaptive control of the initial supporting force of temporary supports at different locations, this study designs a support force controller based on Simulated Annealing Particle Swarm Optimization Proportional-Integral-Derivative (SAPSO-PID). This study establishes a mathematical model for the hydraulic cylinder pressure system controlled by the temporary support overflow valve and conducts a stability analysis and model verification. The study constructs a simulation control system for the initial supporting force based on SAPSO-PID using the combined simulation platform of AMESim and Matlab. The simulation results demonstrate that the proposed support force control system efficiently achieves adaptive control of the initial supporting force of temporary supports. An experimental system in the underground roadway of a coal mine is constructed to validate the results of the simulation analysis.

Combination optimization of green energy supply in data center based on simulated annealing particle swarm optimization algorithm

At present, the high energy consumption of data centers based on grid power supply not only brings huge direct cost of electricity, but also indirectly produces a lot of greenhouse gases, which affects the natural environment. Academia and industry are beginning to introduce clean renewable energy sources such as wind and solar power into data centers to reduce operating costs and environmental damage by building new green data centers. To solve this problem, this study considers the use of waste heat for refrigeration while taking natural gas power generation into account, and introduces wind energy as a green energy source. On the premise of considering the response level of data centers, the two resources are combined and deployed to improve resource utilization and reduce energy consumption costs. Aiming at the instability of wind power generation, a particle swarm energy scheduling optimization algorithm based on simulated annealing algorithm was proposed by combining simulated annealing algorithm and particle swarm optimization algorithm. The research shows that, considering the response level of data centers, the use of natural gas and wind energy as the main energy supply can effectively reduce the overall energy consumption of data centers.

Stability analysis and control parameter optimization of multi-VSG parallel grid-connected system

The virtual synchronous generator (VSG) is emerging as an attractive solution for controlling the grid-connected inverter when the renewable energy has a high penetration level into the grid. This study delves into the stability analysis and parameter optimization of multi-VSG parallel grid-connected systems. Consequently, a small-signal model (SSM) for the multi-VSG parallel grid-connected system is developed to facilitate system analysis. Utilizing this model, the feasible parameter regions are determined by applying the Routh-Hurwitz criterion, enabling the identification of key factors contributing to the system's oscillation mode. Subsequently, a time-domain model is employed to ascertain the dynamic feasible region index, which aids in delineating the scope of parameter optimization. Furthermore, the impact of deviation angle, damping, and eigenvalues on system stability is incorporated into the construction of the objective function. To optimize system parameters within the feasible region, the simulated annealing-particle swarm optimization algorithm (SA-PSO) is utilized. The effectiveness of the theoretical analysis and the proposed parameter optimization method is demonstrated through simulations.

Comparative Analysis of Intelligent Optimization Algorithms for Atmospheric Duct Inversion Using Automatic Identification System Signals

Using intelligent optimization algorithms to retrieve atmospheric duct parameters by monitoring automatic identification system (AIS) signals at sea is a new passive remote sensing technology for atmospheric ducts. To thoroughly compare and analyze the inversion results of different intelligent optimization algorithms and optimize the parameters of the algorithms, this study considered a simulated atmospheric duct environment for atmospheric duct inversion using the genetic, simulated annealing, and particle swarm optimization (PSO) algorithms. The results indicated that the PSO algorithm exhibited the best inversion performance. The inversion results of the simulated annealing particle swarm optimization (SAPSO) and PSO algorithms under different inversion parameters were further statistically analyzed, and the atmospheric duct parameters were obtained from measured AIS signals based on the SAPSO algorithm. The inversion results verified the effectiveness of the proposed algorithm, and they continuously improved with additional calculations in the inversion algorithm. However, the changing trend gradually slowed. Therefore, in practical applications, the inversion time consumption should be balanced with the inversion effect to optimize the inversion parameters.

Design of the particle swarm optimization simulated annealing MPPT control method based on motion shadow

Under the same environmental conditions, photovoltaic arrays at different operating voltages will significantly affect the size of solar power. To obtain as much power as possible, the maximum power point tracking (MPPT) control method has been invented. Previous studies focused on the tracking problem under local shadow conditions, but the dynamic shadow problem with rapid changes in local shadow was rarely discussed. To solve the tracking problem that the maximum power point has multi-peak and fast-changing characteristics under this condition, this paper proposes a simulated annealing MPPT control method based on particle swarm optimization, which compensates the disadvantages of the memoryless Markov chain in the optimization algorithm by adaptive asymmetric memory factor, thus improving the local search ability and convergence speed in the later stage. The control effect is verified by modeling and simulation in Simulink of MATLAB, and the adaptability of the heuristic control method is reflected by simulating the tracking mode under the moving shadow condition through the actual hardware deployment on the moving car.

Maximum power tracking of wave power generation system based on improved particle swarm optimization

In a wave power generation system, traditional particle swarm optimization (PSO) is prone to premature convergence and local optimal solution when it is used to control the maximum power tracking. Therefore, an improved PSO is proposed, and simulated annealing adaptive particle swarm optimization (SAAPSO) is constructed. The algorithm controls the inertia weight coefficient factor by hyperbolic tangent function and makes the nonlinear adaptive change. Using a linear change strategy to control social learning factors and self-learning factors, a simulated annealing operation is introduced, and a temperature is set according to the initial state of the population. The population is guided to compare the fitness value with the random number in the Metropolis criterion and temperature, and whether a new solution is generated is judged, to improve the problem that the algorithm falls into a locally optimal solution. The simulation results show that the algorithm can effectively prevent the wave power generation system from falling into the local maximum power point, and the maximum wave energy capture ability is significantly improved.

An Improved Adaptive Simulated Annealing Particle Swarm Optimization Algorithm for ARAIM Availability

Civil aviation transportation equipment is more convenient and faster than other transportation tools and is an essential part of intelligent transportation. It is significant to study the reliability of positioning information and enhance traffic safety. Advanced receiver autonomous integrity monitoring (ARAIM) can provide vertical guidance during the different navigation stages in civil aviation fields. The traditional multiple hypothesis solution separation (MHSS) algorithm distributes the probability of hazardous misleading information (PHMI) and probability of false alarm (PFA) uniformly over all visible satellites resulting in reduced global availability of ARAIM. Aiming at this problem, we proposed an adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm to redistribute integrity and continuity risks and establish a protection level optimization model. Based on the real BeiDou navigation satellite system/global positioning system (BDS/GPS) data, the experimental results show that the optimized algorithm can reduce the vertical protection level (VPL), and the ARAIM global availability of BDS/GPS is improved by 1.73%∼2.73%. The optimized algorithm can improve the availability of integrity monitoring at different stages of the navigation system and provide a basis for ensuring the reliability of the positioning results.

Adaptive SLAM Methodology Based on Simulated Annealing Particle Swarm Optimization for AUV Navigation

Simultaneous localization and mapping (SLAM) is crucial and challenging for autonomous underwater vehicle (AUV) autonomous navigation in complex and uncertain ocean environments. However, inaccurate time-varying observation noise parameters may lead to filtering divergence and poor mapping accuracy. In addition, particles are easily trapped in local extrema during the resampling, which may lead to inaccurate state estimation. In this paper, we propose an innovative simulated annealing particle swarm optimization-adaptive unscented FastSLAM (SAPSO-AUFastSLAM) algorithm. To cope with the unknown observation noise, the maximum a posteriori probability estimation algorithm is introduced into SLAM to recursively correct the measurement noise. Firstly, the Sage–Husa (SH) based unscented particle filter (UPF) algorithm is proposed to estimate time-varying measurement noise adaptively in AUV path estimation for improving filtering accuracy. Secondly, the SH-based unscented Kalman filter (UKF) algorithm is proposed to enhance mapping accuracy in feature estimation. Thirdly, SAPSO-based resampling is proposed to optimize posterior particles. The random judgment mechanism is used to update feasible solutions iteratively, which makes particles disengage local extreme values and achieve optimal global effects. The effectiveness and accuracy of the proposed algorithm are evaluated through simulation and sea trial data. The average AUV navigation accuracy of the presented SAPSO-AUFastSLAM method is improved by 18.0% compared to FastSLAM, 6.5% compared to UFastSLAM, and 5.9% compared to PSO-UFastSLAM.

Multi-objective based demand response strategy optimization considering differential demand on reliability of power system

The reliability of power system will vary with different demand response strategies, and it is significant to make a decision on demand response for satisfying differential reliability demand of power system. First, a peak-valley period partition model with an objective of maximizing silhouette coefficient was established based on the fuzzy clustering and step-wise iteration technology. Second, this paper proposed a multi-objective time of use electricity price optimization model considering the interests of both supply and demand sides, as well as reliability demand, and a multi-objective simulated annealing particle swarm optimization algorithm was developed to obtain solution. Third, the Pareto front curve of the dual-objective function was fitted by using a third-order Hermite interpolation algorithm, and the time of use prices corresponding to the reliability demand was obtained by using the back propagation neural network algorithm. Finally, the proposed model and algorithm were verified by the RBTS system, and the results indicate good rationality and effectiveness of the proposed method.

Optimization design of stepped rotor type bearingless switched reluctance motor

In order to solve the problems of small starting torque and large torque ripple existing in traditional 12/14 hybrid stator pole bearingless switched reluctance motor (BSRM), a stepped rotor type BSRM is proposed. The proposed motor not only inherits the advantages of traditional 12/14 hybrid stator pole BSRM, but also can start at any rotor position and has relatively small torque ripple. To obtain better electromagnetic characteristics, simulated annealing particle swarm optimization (SAPSO) algorithm is proposed to optimize the structure of the stepped rotor type BSRM. Then, the torque, inductance and suspension force characteristics of the stepped rotor type BSRM before and after optimization are analyzed and compared. Finally, the effectiveness of the proposed optimization algorithm is verified by analysis results.

Bi-objective optimization framework for prefabricated construction service combination selection using genetic simulated annealing algorithm

PurposeThe purpose of this paper is to develop a bi-objective optimization framework to select prefabricated construction service composition. An improved algorithm-genetic simulated annealing algorithm (GSA) is employed to demonstrate the application of the framework.Design/methodology/approachThe weighted aggregate multi-dimensional collaborative relationship is used to quantitatively evaluate the synergistic effect. The quality of service is measured using the same method. The research proposed a service combination selection framework of prefabricated construction that comprehensively considers the quality of service and synergistic effect. The framework is demonstrated by using a GSA that can accept poor solutions with a certain probability. Furthermore, GSA is compared with the genetic algorithm (GA), simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) to validate the performance.FindingsThe results indicated that GSA has the largest optimal fitness value and synergistic effect compared with other algorithms, and the convergence time and convergence iteration of the improved algorithm are generally at a low level.Originality/valueThe contribution of this study is that the proposed framework enables project managers to clarify the interactions of the prefabricated construction process and provides guidance for project collaborative management. In addition, GSA helps to improve the probability of successful collaboration between potential partners, therefore enhancing client satisfaction.

Optimal Allocation of Distributed Generators in Active Distribution Network considering TOU Price

As the distributed generators (DGs) are connected to active distribution network (ADN), it strengthens the communication interest between the customs and DGs and can facilitate energy integration. This article proposes hybrid pricing for DG configuration by taking advantage of fixed pricing and dynamic pricing. The time sequence scenario of wind-photovoltaic-load power can be got by k-means, which can balance the calculation burden with multiple scenarios. The planning model is built as the optimal objective for minimal investment in operation and maintenance of DGs, reduction of network loss, and decrease of voltage deviation. An improved simulated annealing particle swarm optimization algorithm is also proposed by refining the initialized population based on the niche fitness, introducing inertia weight with chaotic disturbance and accelerating local search with learning factor of dynamic parameter. The effectiveness and rationality of the proposed methodology are verified by simulation in the IEEE 69-bus system.

Inverse kinematics analysis of rock drilling manipulator based on improved SA-PSO

In the inverse solution of the rock drilling manipulator, the particle swarm optimization algorithm has been widely used, but due to the complex structure of the manipulator, its disadvantage is that the convergence speed is slow and it is easy to fall into the local optimal solution. Therefore, the adaptive annealing coefficient is introduced into the simulated annealing particle swarm optimization algorithm and improved; the overall search and convergence are improved. Through the simulation experiment of the PSO algorithm, it is proved that this method is better than the conventional PSO method in terms of convergence speed and precision, and can better solve the problem of slow convergence speed and falling into local optimum.