Simulated Annealing Particle Swarm Optimization Algorithm Research Articles

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.

Sum-Rate Maximization Scheme for Multi-RIS-Assisted NOMA Uplink Systems

Sum-Rate Maximization Scheme for Multi-RIS-Assisted NOMA Uplink Systems

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.

Acceleration harmonic identification for an electro-hydraulic shaking table based on the Simulated Annealing-Particle Swarm Optimization algorithm

Nonlinear factors exist in electro-hydraulic shaking table and may distort the response signal when a sinusoidal acceleration is inputted. To obtain accurate harmonics, a harmonic identification strategy for the acceleration of the electro-hydraulic shaking table based on the SA-PSO (Simulated Annealing-Particle Swarm Optimization) algorithm was proposed. First, the criterion function of harmonic identification was built with the least square method. Then, the PSO algorithm was introduced into the internal circulations of the SA algorithm to speed up the local search speed to construct the harmonic identification strategy. To verify the performance of the identification strategy, the SA algorithm, PSO algorithm, and SA-PSO algorithm were successively used to identify the response signal. And the convergence rate, accuracy, and stability of the three algorithms were compared. Finally, the harmonic regeneration was carried out after the amplitude and phase of each harmonic were obtained. Experimental results show that the harmonic identification strategy has fast identification speed and high identification accuracy.

A New Solving Method Based on Simulated Annealing Particle Swarm Optimization for the Forward Kinematic Problem of the Stewart–Gough Platform

The forward kinematics of the Stewart–Gough platform is vital for high-precision positioning and sensing in six degrees of freedom. In this paper, the solving of forward kinematics is converted to searching for the best posture corresponding to the input outrigger length vectors by inverse kinematic calculation. The simulated annealing (SA) process is embedded into particle swarm optimization (PSO) to improve the convergence speed and overcome the local convergence. The proposed SA–PSO algorithm not only accelerates the convergence speed but also improves the reliability and success rate. After the optimization of algorithm parameters, the success rate of the SA–PSO for random outrigger length vector input is steadily above 99.9% while the results of PSO is between 93% and 100%. In the workspace simulation, the average iteration number of the proposed algorithm is reduced from 146.6 to 122.7, meanwhile, the success rate of SA–PSO is 99.992%, much higher than the 93.805% of PSO. Compared with the traditional Newton–Raphson method, the SA–PSO method has a higher success rate and is less time consuming. Finally, a physical experiment is conducted on a six-degree-of-freedom platform, revealing that the given algorithm is reliable and accurate.

Optimal Flood-Control Operation of Cascade Reservoirs Using an Improved Particle Swarm Optimization Algorithm

Optimal reservoir operation is an important measure for ensuring flood-control safety and reducing disaster losses. The standard particle swarm optimization (PSO) algorithm can find the optimal solution of the problem by updating its position and speed, but it is easy to fall into a local optimum. In order to prevent the problem of precocious convergence, a novel simulated annealing particle swarm optimization (SAPSO) algorithm was proposed in this study, in which the Boltzmann equation from the simulated annealing algorithm was incorporated into the iterative process of the PSO algorithm. Within the maximum flood peak reduction criterion, the SAPSO algorithm was used into two floods in the Tianzhuang–Bashan cascade reservoir system. The results shown that: (1) There are lower maximum outflows. The maximum outflows of Tianzhuang reservoir using SAPSO algorithm decreased by 9.3% and 8.6%, respectively, compared with the measured values, and those of Bashan reservoir decreased by 18.5% and 13.5%, respectively; (2) there are also lower maximum water levels. The maximum water levels of Tianzhuang reservoir were 0.39 m and 0.45 m lower than the measured values, respectively, and those of Bashan reservoir were 0.06 m and 0.46 m lower, respectively; and (3) from the convergence processes, the SAPSO algorithm reduced the convergence speed in the early stage of convergence and provided a superior objective function value than PSO algorithm. At the same time, by comparing with GA algorithm, the performance and applicability of SAPSO algorithm in flood operation are discussed further. Thus, the optimal operation model and SAPSO algorithm proposed in this study provide a new approach to realizing the optimal flood-control operation of cascade reservoir systems.

Optimal Design of Multimissile Formation Based on an Adaptive SA-PSO Algorithm

In an effort to maximize the combat effectiveness of multimissile groups, this paper proposes an adaptive simulated annealing–particle swarm optimization (SA-PSO) algorithm to enhance the design parameters of multimissile formations based on the concept of missile cooperative engagement. Firstly, considering actual battlefield circumstances, we establish an effectiveness evaluation index system for the cooperative engagement of missile formations based on the analytic hierarchy process (AHP). In doing so, we adopt a partial triangular fuzzy number method based on authoritative assessments by experts to ascertain the weight of each index. Then, considering given constraints on missile performance, by selecting the relative distances and angles of the leader and follower missiles as formation parameters, we design a fitness function corresponding to the established index system. Finally, we introduce an adaptive capability into the traditional particle swarm optimization (PSO) algorithm and propose an adaptive SA-PSO algorithm based on the simulated annealing (SA) algorithm to calculate the optimal formation parameters. A simulation example is presented for the scenario of optimizing the formation parameters of three missiles, and comparative experiments conducted with the traditional and adaptive PSO algorithms are reported. The simulation results indicate that the proposed adaptive SA-PSO algorithm converges faster than both the traditional and adaptive PSO algorithms and can quickly and effectively solve the multimissile formation optimization problem while ensuring that the optimized formation satisfies the given performance constraints.

Simulated Annealing Particle Swarm Optimization for High-Efficiency Power Amplifier Design

In this article, a method for design automation high-efficiency power amplifiers (PAs) based on an optimization-oriented is proposed. The advantages of using particle swarm optimization to design PAs in electronic design automatic tools are analyzed, and then, simulated annealing is combined to form simulated annealing particle swarm optimization (SA-PSO). In this article, the perfectly inelastic collision used in conventional PSO is replaced by the perfectly elastic collision to improve the searching ability through reducing kinetic energy consumption. A stepped fitness function is defined to avoid the performance pit at a single-frequency point, and the concept “missing and returning” is introduced to solve the nonconvergence problem in simulation. Based on the improved SA-PSO algorithm, a broadband 10 W and a triband 6-W PA are designed. The broadband 10-W PA achieves an average drain efficiency (DE) of 62.5% and an output power of 41.2 dBm in the 0.8-3-GHz frequency band, respectively; the triband 6-W PA achieves an average of 57.4%/47.9%/47.3% DE and 37.7-/38.3-/37.4 dBm output power in 3.35-3.55-/5.75-5.95-/7.65-7.95-GHz frequency bands, respectively, while the gains of most stopbands reach less than 0 dB. With digital predistortion technology, the adjacent channel power ratio (ACPR) can reach less than -45 dBc.

Optimal Capacity Allocation of Energy Storage System considering Uncertainty of Load and Wind Generation

Energy storage systems (ESSs) are promising solutions for the mitigation of power fluctuations and the management of load demands in distribution networks (DNs). However, the uncertainty of load demands and wind generations (WGs) may have a significant impact on the capacity allocation of ESSs. To solve the problem, a novel optimal ESS capacity allocation scheme for ESSs is proposed to reduce the influence of uncertainty of both WG and load demands. First, an optimal capacity allocation model is established to minimize the ESS investment costs and the network power loss under constraints of DN and ESS operating points and power balance. Then, the proposed method reduces the uncertainty of load through a comprehensive demand response system based on time-of-use (TOU) and incentives. To predict the output of WGs, we combined particle swarm optimization (PSO) and backpropagation neural network to create a prediction model of the wind power. An improved simulated annealing PSO algorithm (ISAPSO) is used to solve the optimization problem. Numerical studies are carried out in a modified IEEE 33-node distribution system. Simulation results demonstrate that the proposed model can provide the optimal capacity allocation and investment cost of ESSs with minimal power losses.

Cooperative Adaptive Cruise Control Strategy Optimization for Electric Vehicles Based on SA-PSO With Model Predictive Control

The Cooperative Adaptive Cruise Control (CACC) is considered to be an effective method to improve traffic flow. However, the comfortability and economy need to be paid more attention to besides stability and safety. In this paper, a CACC strategy is proposed for an electric vehicle platoon to improve the economy, following performance, safety, and comfortability characteristics, based on the model predictive control (MPC) with simulated annealing-particle swarm optimization (SA-PSO) algorithm. Firstly, the braking force distribution strategy of electric vehicles is designed to improve the efficiency of regenerative braking. Secondly, based on the variable vehicle spacing with fixed time headway, a vehicle platoon following strategy is established to meet the following performance and safety. Thirdly, an MPC controller is used to control the states of the vehicles in the platoon to satisfy the performance. A multi-objective function of the MPC controller is established, including the economy, following performance, comfortability, and safety of the vehicles in the platoon. The SA-PSO algorithm effectively solves the problem of the discrete variables in the objective function. Simulations are conducted to validate the sufficient conditions of the economy, following performance, comfortability, and safety. Simulation results demonstrate that the economic efficiency of the CACC strategy with the economic index is 16.5% higher than that of the existing ACC strategy. Meanwhile, the other characteristics can also meet the control requirement.

Variable Weight Coefficient Optimization of Gearshift Actuator With Direct-Driving Automated Transmission

Direct-Driving Automated Mechanical Transmission (DAMT) gearshift actuator is a kind of electromagnetic linear actuator. In practical application, the actual gearshift force in each steady states is significantly different from the electromagnetic force model. For the optimization of gear shifting actuator with undetermined volume constraint, this paper presents a variable weight coefficient optimization method of electromagnetic linear actuator which equates the output forces under different steady states in the fitness function. These are intended to adjust the relationships among the optimized objects by using the fitness function to improve the overall optimization effect. In order to obtain the fitness value of each combination parameter accurately, finite element analysis (FEA) is adopted in this paper, to improve the efficiency of the algorithm, and a more efficient simulated annealing -particle swarm optimization algorithm (SA-PSO) is used by the proposed optimization method. Finally, experiment is carried out based on prototype of gearshift actuator to verify the optimization results. The results show that the maximum electromagnetic force and terminal electromagnetic force are both increased by more than 20%, and the volatility of gearshift force is reduced 26.4%, which ensures the structure compactness and fast response. The proposed method decreases the volatility by 27.5% compared with the constant weight coefficients and improves the gearshift force in each stage.

Siting and sizing of distributed generators based on improved simulated annealing particle swarm optimization.

Distributed power grids generally contain multiple diverse types of distributed generators (DGs). Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) algorithms have some deficiencies in site selection and capacity determination of DGs, such as slow convergence speed and easily falling into local trap. In this paper, an improved SA-PSO (ISA-PSO) algorithm is proposed by introducing crossover and mutation operators of genetic algorithm (GA) into SA-PSO, so that the capabilities of the algorithm are well embodied in global searching and local exploration. In addition, diverse types of DGs are made equivalent to four types of nodes in flow calculation by the backward or forward sweep method, and reactive power sharing principles and allocation theory are applied to determine initial reactive power value and execute subsequent correction, thus providing the algorithm a better start to speed up the convergence. Finally, a mathematical model of the minimum economic cost is established for the siting and sizing of DGs under the location and capacity uncertainties of each single DG. Its objective function considers investment and operation cost of DGs, grid loss cost, annual purchase electricity cost, and environmental pollution cost, and the constraints include power flow, bus voltage, conductor current, and DG capacity. Through applications in an IEEE33-node distributed system, it is found that the proposed method can achieve desirable economic efficiency and safer voltage level relative to traditional PSO and SA-PSO algorithms, and is a more effective planning method for the siting and sizing of DGs in distributed power grids.

Trajectory Planning of 7-DOF Space Manipulator for Minimizing Base Disturbance

In the free-floating mode, there is intense dynamic coupling existing between the space manipulator and the base, and the base attitude may change while performing a motion with its manipulator. Therefore, it is necessary to reduce the interference that resulted from the manipulator movement. For planning trajectories of the space manipulator with 7 degrees of freedom (7-DOF), simulated annealing particle swarm optimization (SAPSO) algorithm is presented in the paper. Firstly, kinematics equations are setup. Secondly, the joint functions are parameterized by sinusoidal functions, and the objective function is defined according to the motion constraints of manipulator and accuracy requirements of the base attitude. Finally, SAPSO algorithm is used to search the optimal trajectory. The simulation results verify the proposed method.

Surface fitting and position-pose measurements based on an improved SA-PSO algorithm

Scattered point pattern matching is an important issue in computer vision and pattern recognition,which is widely used for target recognition,medical and remote sensing image registration,and position and pose measurements. This paper describes an algorithm for use in an explosive ordnance disposal robot for when the target object is partial exposed.A simulated annealing-particle swarm optimization(SA-PSO)algorithm based on particle density distribution changes for scattered point pattern matching is developed.The algorithm is more accurate and faster than previous algorithms.The fitness function is constructed from a series of scattered points collected from the local surface of a rotated object.Accurate surface fitting and pose parameters are found using coordinate transforms.The factors that affect the accuracy and applicability are discussed in detail.Tests show that this method is accurate and efficient,with less points needed and less sensitivity to point errors compared with the traditional least squares method.

Research on AC Asynchronous Motor Vector Control Speed Control System Based on Labview

During the process of using traditional particle swarm optimization (PSO) to identify electric parameters, the algorithm can be easily caught in locally optimal solution, thus leading to relatively large identification result error. Therefore, the article proposes a simulated annealing particle swarm optimization (SA-PSO) algorithm to integrate the advantages, namely the strong global optimization capability of simulated annealing (SA) algorithm and the fast convergence speed of PSO algorithm, in order to improve the traditional PSO algorithm, and uses simulated annealing principle to determine inertia weight of PSO algorithm. Meanwhile, DFIG with the unit capacity of 1.5MW has been taken as the research object for simulation analysis. The test system employs LabVIEW software for experiment design, and the result shows: compared with the identification result of traditional PSO algorithm, SA-PSO algorithm can rapidly and accurately identify DFIG electric parameters and the identification result has higher precision.

Structural Non-Probabilistic Reliability Analysis Based on SAPSO-DE Hybrid Algorithm

This paper presents a structural non-probabilistic reliability analysis approach based on a hybrid algorithm of the simulated annealing-particle swarm optimization algorithm and the differential evolution (SAPSO-DE) algorithm. In the structural non-probabilistic reliability analysis, the problem with uncertain parameters can be formulated as an optimization problem using convex model. However, the limit state function is usually implicit for the uncertain parameters. By employing the SAPSO-DE hybrid algorithm based on the evolution of the cognitive and social experiences, the problem of the structural non-probabilistic reliability analysis is solved. A numerical example is given to illustrate the high precision and good feasibility of the present method. The results shows that this proposed approach is effective, and has the predominant capability of global optimization and convergence precision.