Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization

As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.

Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm

Cascade refrigeration system(CRS)can meet a wider range of refrigeration temperature requirements and is more energy efficient than single-refrigerant refrigeration system,making it more widely used in low-temperature industry processes.The synthesis of a CRS with simultaneous consideration of heat integration between refrigerant and process streams is challenging but promising for significant cost saving and reduction of carbon emission.This study presented a stochastic optimization method for the synthesis of CRS.An MINLP model was formulated based on the superstructure developed for the CRS,and an optimization framework was proposed,where simulated annealing algorithm was used to evolve the numbers of pressure/temperature levels for all sub-refrigeration systems,and particle swarm optimization algorithm was employed to optimize the continuous variables.The effectiveness of the proposed methodology was verified by a case study of CRS optimization in an ethylene plant with 21.89%the total annual cost saving.

Dependent task assignment algorithm based on particle swarm optimization and simulated annealing in ad-hoc mobile cloud

In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.

Model identification of hydraulic flight simulator based on improved particle swarm optimization and wavelet analysis

A new model identification method of hydraulic flight simulator adopting improved panicle swarm optimization （PSO） and wavelet analysis is proposed for achieving higher identification precision. Input-output data of hydraulic flight simulator were decomposed by wavelet muhiresolution to get the information of different frequency bands. The reconstructed input-output data were used to build the model of hydraulic flight simulator with improved particle swarm optimization with mutation （IPSOM） to avoid the premature convergence of traditional optimization techniques effectively. Simulation results show that the proposed method is more precise than traditional system identification methods in operating frequency bands because of the consideration of design index of control system for identification.

Hybrid Optimization Based PID Controller Design for Unstable System

PID controllers play an important function in determining tuning para-meters in any process sector to deliver optimal and resilient performance for non-linear,stable and unstable processes.The effectiveness of the presented hybrid metaheuristic algorithms for a class of time-delayed unstable systems is described in this study when applicable to the problems of PID controller and Smith PID controller.The Direct Multi Search(DMS)algorithm is utilised in this research to combine the local search ability of global heuristic algorithms to tune a PID controller for a time-delayed unstable process model.A Metaheuristics Algorithm such as,SA(Simulated Annealing),MBBO(Modified Biogeography Based Opti-mization),BBO(Biogeography Based Optimization),PBIL(Population Based Incremental Learning),ES(Evolution Strategy),StudGA(Stud Genetic Algo-rithms),PSO(Particle Swarm Optimization),StudGA(Stud Genetic Algorithms),ES(Evolution Strategy),PSO(Particle Swarm Optimization)and ACO(Ant Col-ony Optimization)are used to tune the PID controller and Smith predictor design.The effectiveness of the suggested algorithms DMS-SA,DMS-BBO,DMS-MBBO,DMS-PBIL,DMS-StudGA,DMS-ES,DMS-ACO,and DMS-PSO for a class of dead-time structures employing PID controller and Smith predictor design controllers is illustrated using unit step set point response.When compared to other optimizations,the suggested hybrid metaheuristics approach improves the time response analysis when extended to the problem of smith predictor and PID controller designed tuning.

Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem

Capacitated vehicle routing problem (CVRP) is an NP-hard problem. For large-scale problems, it is quite difficult to achieve an optimal solution with traditional optimization methods due to the high computational complexity. A new hybrid ap- proximation algorithm is developed in this work to solve the problem. In the hybrid algorithm, discrete particle swarm optimiza- tion (DPSO) combines global search and local search to search for the optimal results and simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum. The computational study showed that the proposed algorithm is a feasible and effective approach for capacitated vehicle routing problem, especially for large scale problems.

APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM

A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a collaborative population-based search, which isinspired by the social behavior of bird flocking. It combines local search (by self experience) andglobal search (by neighboring experience), possessing high search efficiency. Simulated annealing(SA) employs certain probability to avoid becoming trapped in a local optimum and the search processcan be controlled by the cooling schedule. By reasonably combining these two different searchalgorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, isdeveloped. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated byapplying it to some benchmark job-shop scheduling problems and comparing results with otheralgorithms in literature. Comparing results indicate that PSO-based algorithm is a viable andeffective approach for the job-shop scheduling problem.

A new support vector machine optimized by improved particle swarm optimization and its application

A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.

Structural optimization of Au–Pd bimetallic nanoparticles with improved particle swarm optimization method

Due to the dependence of the chemical and physical properties of the bimetallic nanoparticles（NPs） on their structures,a fundamental understanding of their structural characteristics is crucial for their syntheses and wide applications. In this article, a systematical atomic-level investigation of Au–Pd bimetallic NPs is conducted by using the improved particle swarm optimization（IPSO） with quantum correction Sutton–Chen potentials（Q-SC） at different Au/Pd ratios and different sizes. In the IPSO, the simulated annealing is introduced into the classical particle swarm optimization（PSO） to improve the effectiveness and reliability. In addition, the influences of initial structure, particle size and composition on structural stability and structural features are also studied. The simulation results reveal that the initial structures have little effects on the stable structures, but influence the converging rate greatly, and the convergence rate of the mixing initial structure is clearly faster than those of the core-shell and phase structures. We find that the Au–Pd NPs prefer the structures with Au-rich in the outer layers while Pd-rich in the inner ones. Especially, when the Au/Pd ratio is 6：4, the structure of the nanoparticle（NP） presents a standardized Pd（core） Au（shell） structure.

Scenario-oriented hybrid particle swarm optimization algorithm for robust economic dispatch of power system with wind power

An economic dispatch problem for power system with wind power is discussed.Using discrete scenario to describe uncertain wind powers,a threshold is given to identify bad scenario set.The bad-scenario-set robust economic dispatch model is established to minimize the total penalties on bad scenarios.A specialized hybrid particle swarm optimization(PSO)algorithm is developed through hybridizing simulated annealing(SA)operators.The SA operators are performed according to a scenario-oriented adaptive search rule in a neighborhood which is constructed based on the unit commitment constraints.Finally,an experiment is conducted.The computational results show that the developed algorithm outperforms the existing algorithms.

Simulation-optimization model of reservoir operation based on target storage curves

This paper proposes a new storage allocation rule based on target storage curves. Joint operating rules are also proposed to solve the operation problems of a multi-reservoir system with joint demands and water transfer-supply projects. The joint operating rules include a water diversion rule to determine the amount of diverted water in a period, a hedging rule based on an aggregated reservoir to determine the total release from the system, and a storage allocation rule to specify the release from each reservoir. A simulation-optimization model was established to optimize the key points of the water diversion curves, the hedging rule curves, and the target storage curves using the improved particle swarm optimization （IPSO） algorithm. The multi-reservoir water supply system located in Liaoning Province, China, including a water transfer-supply project, was employed as a case study to verify the effectiveness of the proposed join operating rules and target storage curves. The results indicate that the proposed operating rules are suitable for the complex system. The storage allocation rule based on target storage curves shows an improved performance with regard to system storage distribution.

Optimization on the Impeller of a Low-specific-speed Centrifugal Pump for Hydraulic Performance Improvement

In order to widen the high-efficiency operating range of a low-specific-speed centrifugal pump, an optimization process for considering efficiencies under 1.0Qd and 1.4Qd is proposed. Three parameters, namely, the blade outlet width b2, blade outlet angle β2, and blade wrap angle φ, are selected as design variables. Impellers are generated using the optimal Latin hypercube sampling method. The pump efficiencies are calculated using the software CFX 14.5 at two operating points selected as objectives. Surrogate models are also constructed to analyze the relationship between the objectives and the design variables. Finally, the particle swarm optimization algorithm is applied to calculate the surrogate model to determine the best combination of the impeller parameters. The results show that the performance curve predicted by numerical simulation has a good agreement with the experimental results. Compared with the efficiencies of the original impeller, the hydraulic efficiencies of the optimized impeller are increased by 4.18% and 0.62% under 1.0Qd and 1.4Qd, respectively. The comparison of inner flow between the original pump and optimized one illustrates the improvement of performance. The optimization process can provide a useful reference on performance improvement of other pumps, even on reduction of pressure fluctuations.

Resource allocation optimization of equipment development task based on MOPSO algorithm

Resource allocation for an equipment development task is a complex process owing to the inherent characteristics,such as large amounts of input resources,numerous sub-tasks,complex network structures,and high degrees of uncertainty.This paper presents an investigation into the influence of resource allocation on the duration and cost of sub-tasks.Mathematical models are constructed for the relationships of the resource allocation quantity with the duration and cost of the sub-tasks.By considering the uncertainties,such as fluctuations in the sub-task duration and cost,rework iterations,and random overlaps,the tasks are simulated for various resource allocation schemes.The shortest duration and the minimum cost of the development task are first formulated as the objective function.Based on a multi-objective particle swarm optimization(MOPSO)algorithm,a multi-objective evolutionary algorithm is constructed to optimize the resource allocation scheme for the development task.Finally,an uninhabited aerial vehicle(UAV)is considered as an example of a development task to test the algorithm,and the optimization results of this method are compared with those based on non-dominated sorting genetic algorithm-II(NSGA-II),non-dominated sorting differential evolution(NSDE)and strength pareto evolutionary algorithm-II(SPEA-II).The proposed method is verified for its scientific approach and effectiveness.The case study shows that the optimization of the resource allocation can greatly aid in shortening the duration of the development task and reducing its cost effectively.

Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Parameter Calibration in Hydrological Simulation

Parameter calibration is an important part of hydrological simulation and affects the final simulation results.In this paper,we introduce heuristic optimization algorithms,genetic algorithm(GA)to cope with the complexity of the parameter calibration problem,and use particle swarm optimization algorithm(PsO)as a comparison.For large-scale hydrological simulations,we use a multilevel parallel parameter calibration framework to make full use of processor resources,and accelerate the process of solving high-dimensional parameter calibration.Further,we test and apply the experiments on domestic supercomputers.The results of parameter calibration with GA and PSO can basically reach the ideal value of 0.65 and above,with PSO achieving a speedup of 58.52 on TianHe-2 supercomputer.The experimental results indicate that using a parallel implementation on multicore CPUs makes high-dimensional parameter calibration in large-scale hydrological simulation possible.Moreover,our comparison of the two algorithms shows that the GA obtains better calibration results,and the PSO has a more pronounced acceleration effect.

基于神经网络优化模型的中药复方安慰剂配色模拟研究

目的构建粒子群反向传播(Particle swarm optimization-back propagation,PSO-BP)神经网络对中药复方颗粒剂安慰剂制备着色剂的用量进行预测,为中药复方颗粒剂安慰剂颜色的模拟提供一种新思路。方法运用BP神经网络建立样品颜色参数L、a^(*)、b^(*)与色素质量分数的模型,利用粒子群算法的全局搜索能力优化BP神经网络权重和偏置,防止模型出现局部最小值,再采用线性降低权系数法和引入变异算子提高粒子群算法的全局寻优能力;以颜色综合评价指标(ΔE)为客观评价标准,验证试验结果。结果训练结果表明,改进的PSO-BP神经网络拟合精度最高达到98.31%;预测结果表明,改进的PSO-BP神经网络的预测误差最小,平均绝对百分比误差(MAPE)、均方根误差(RMSE)和平均色差(ΔE)分别为0.4115、2.1646、2.56;制备3种颗粒的验证样品进行验证,验证样品与模型药物的ΔE分别为1.73、2.63、4.11,肉眼直观评价其中两组与模型药物色差较小。结论基于改进粒子群优化算法的BP神经网络可模拟中药复方颗粒剂安慰剂制备着色剂用量预测,可作为安慰剂配色研究的推荐优化模型。

基于SA-PSO算法优化CNN的电能质量扰动分类模型

针对传统电能质量扰动分类模型中扰动特征复杂、识别步骤繁琐的问题,提出了一种通过模拟退火(SA)算法与粒子群优化(PSO)算法相结合来优化卷积神经网络(CNN)的电能质量扰动分类模型。将CNN卷积层中的二维卷积核替换成一维卷积核;采用SA算法对PSO算法进行改进,规避PSO算法陷入局部最优的困境;采用改进后的PSO算法对CNN进行参数寻优;利用优化CNN提取和筛选合适的特征,根据这些特征利用分类器得到最终分类结果。通过算例分析得出,使用基于SA-PSO算法优化的CNN的电能质量扰动分类模型能精确地识别出电能质量扰动信号。

电动汽车双层优化模型的充放电调度策略

传统的分时电价策略虽然一定程度上可以改善电动汽车无序充电所产生的电网日负荷峰谷差加大、负荷率降低等状况,但易产生新的负荷高峰,并且当前多目标优化等策略削峰填谷效果欠佳或用户参与度不高。针对上述问题,提出一种基于双层优化模型的调度策略以充分考虑电网和用户两侧需求。第1层模型以优化电网日负荷方差最小为目标函数;第2层优化模型建立以车主充电成本最小以及保证用户出行需求的目标函数,然后用改进的粒子群-模拟退火算法对双层优化模型进行循环迭代求解,并将第2层优化后的结果反馈给第1层,以此循环优化,输出最终结果。对比优化前后的负荷曲线,结果表明:与当前优化策略相比,所提出的基于双层优化模型的V2G调度策略能有效降低新的负荷高峰及负荷峰谷差,减少参与V2G的用户成本,实现两侧双赢。

采用改进BP-PID控制的机器人避障仿真研究

针对移动机器人避障过程中行驶路径长、寻路速度慢等问题,提出了一种改进反向传播-比例-积分-微分(BP-PID)控制器,并对移动机器人避障效果进行仿真验证。利用移动机器人在二维坐标系的避障简图,得出了移动机器人运动方程式。引用比例-积分-微分(PID)控制器和3层BP神经网络结构,利用BP神经网络的学习能力调整PID控制器参数。引用粒子群算法进行改进,通过改进粒子群算法在线优化BP-PID控制器,确保移动机器人BP-PID控制器收敛于全局最优值,从而使移动机器人避障效果更好。在不同环境中,采用Matlab软件对移动机器人避障效果进行仿真,比较改进前和改进后的移动机器人避障效果。结果显示:在不同环境中,改进前和改进后的BP-PID控制器均能使移动机器人安全地躲避障碍物;但是采用改进的粒子群算法优化BP-PID控制器,可以使移动机器人运动路径更短,迭代次数更少,搜索时间更短。采用改进BP-PID控制器,能够提高移动机器人避障过程中寻路速度,缩短行驶路径,效果更好。

基于改进粒子群算法优化PID控制的主动悬架性能研究

针对二自由度主动悬架比例-积分-微分(PID)控制器参数整定问题,引入粒子群算法,借助粒子群算法的全局搜索能力解决PID控制器参数整定问题,考虑到传统粒子群算法收敛速度较慢,设计了一种改进粒子群算法,根据悬架性能评价指标建立目标函数,分别模拟了随机路面激励输入和减速带式梯形冲击路面激励输入,并验证了基于改进粒子群算法优化的PID控制器的有效性。仿真结果表明:改进粒子群算法后目标函数的收敛速度明显提高;基于改进粒子群算法优化PID控制的主动悬架在不同激励输入条件下均具有较好的行驶平顺性;验证了改进粒子群算法的有效性并解决了PID控制器参数整定问题。

基于离散粒子群算法的管道保温结构优化研究

针对目前管道保温结构优化算法不稳定、结果优化程度不高的问题,建立以经济效益为目标函数,以满足国家散热损失标准等条件为约束函数的离散型数学模型。以BPSO算法为基础改变其位置更新规则,防止种群进化失效;采用自适应权重增加粒子的全局和局部搜索能力;充分利用模拟退火算法的思想避免出现早熟现象。应用改进的算法分别对普通蒸汽管道和核电站的蒸汽管道进行系统仿真实验。结果表明,该算法能够在满足国家散热损失标准等条件下取得最优解,可以为管道保温结构提供合理的优化方案。