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.

Hybrid Strategy of Particle Swarm Optimization and Simulated Annealing for Optimizing Orthomorphisms

Orthomorphism on F n2 is a kind of elementary permutation with good cryptographic properties. This paper proposes a hybrid strategy of Particle Swarm Optimization (PSO) and Simulated Annealing (SA) for finding orthomorphisms with good cryptographic properties. By experiment based on this strategy, we get some orthomorphisms on F n2(n=5, 6, 7, 9, 10) with good cryptographic properties in the open document for the first time, and the optimal orthomorphism on F 82 found in this paper also does better than the one proposed by Feng Dengguo et al. in stream cipher Loiss in difference uniformity, algebraic degree, algebraic immunity and corresponding permutation polynomial degree. The PSOSA hybrid strategy for optimizing orthomorphism in this paper makes design of orthomorphisms with good cryptographic properties automated, efficient and convenient, which proposes a new approach to design orthomorphisms.

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.

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.

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.

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 improved 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.

Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.

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.

基于PCA-SAPSO-BP神经网络的瓦斯涌出量预测研究

为提高煤矿井下瓦斯涌出量预测效率和准确性,针对瓦斯涌出量影响因素的多重相关性、复杂性问题,提出使用主成分分析法对影响因素进行降维处理,针对BP神经网络收敛速度慢且易陷入局部最优解的问题,引入退火粒子群算法优化BP神经网络的权值和阈值;利用Matlab软件编写并构建了PCA-SAPSO-BP神经网络耦合算法对瓦斯涌出量进行预测;选取开滦钱家营煤矿瓦斯涌出量及其影响因子数据作为样本,使用BP神经网络模型、PSO-BP模型和SAPSO-BP模型对样本进行预测。结果表明:PCA-SAPSO-BP神经网络模型的预测平均相对误差为1.06%,PCA-PSO-BP模型为2.20%,PCA-BP模型为3.00%,SAPSO-BP模型为1.61%,PSO-BP模型为2.81%,BP模型为3.98%;预测模型的归一化均方误差为0.0025,希尔不等系数为0.0055,平均绝对误差为0.07 m^(3)/min,判定系数为0.9975,证明PCA-SAPSO-BP神经网络模型提高了BP模型瓦斯涌出量的预测精度。

基于CSAPSO-BP神经网络的光纤陀螺温度补偿研究

光纤陀螺是惯导系统的重要组成器件,环境温度变化会造成光纤陀螺的零偏发生漂移,从而降低测量精度。运用传统的BP神经网络进行预测易陷入局部极小值,导致补偿失败。该文采用混沌模拟退火粒子群BP神经网络的光纤陀螺零偏温度补偿模型,优化了网络参数。通过在-40~60℃的升降温实验对模型进行验证,实验结果表明,该温度补偿模型的零偏稳定性比补偿前约有70%的精度提升,与以往BP模型相比,其预测性能和补偿效果更好。

基于SAPSO算法的集成型三端口变换器回流功率优化

占空比加双重移相控制下的交错并联Buck-Boost和双有源全桥集成的三端口变换器功率密度高、工作方式灵活、应用广泛,但是由于控制方式较为复杂,回流功率高也成为其发展的瓶颈。针对该集成型三端口变换器工作过程中存在的功率回流问题,分析了回流功率产生原理,建立了此变换器传输功率与回流功率数学模型,得到了回流功率与占空比和移相占空比的关系方程;基于此方程,采用模拟退火粒子群算法对占空比与移相角进行优化。通过运行工况为输入电压100 V、输出电压48 V、开关频率20 kHz的仿真与实验结果表明:所提方法可以实现减小回流功率的目标回流功率减少近13%,与模拟退火算法相比,该方法使集成型三端口变换器的回流功率明显降低。

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.

基于AHP和SAPSO的伸缩装置技术状况评定权重系数计算

在伸缩装置技术状况评估过程中,权重系数的研究一直是非常重要的环节。本文提出了一种基于层次分析法(AHP)和改进的模拟退火粒子群算法(SAPSO)的公路桥梁伸缩装置技术状况评定权重系数计算方法。首先,把伸缩装置技术状况评估中AHP过程的判断矩阵一致性检验和判断矩阵权重系数计算归结为非线性优化问题;然后,在SAPSO算法的惯性权重中引入双曲正切函数(HTF)来改善算法的局部寻优性能,采用改进后的HTF-SAPSO算法来计算AHP中的判断矩阵权重系数值;随后,基于AHP构建了公路桥梁伸缩装置技术状况评定的层次结构;最后,以板式橡胶伸缩缝为例进行了权重系数的计算,并和均方根法计算结果进行了比较。

基于SAPSO-BP的CO_(2)相变致裂效果预测及敏感度分析

液态CO_(2)相变致裂效果受很多因素影响,针对BP神经网络收敛速度慢且易陷入局部最优解的问题,为提高模型的预测精度和泛化能力,采用SAPSO算法优化BP神经网络的权值和阈值,并采用MATLAB软件编写构建了SAPSO-BP算法,基于仿真得到的35组数据,使用SAPSO-BP,PSO-BP,BP模型以及多元线性回归模型对致裂效果进行预测,其平均相对误差分别为2.73%、7.1%、14.6%、13.9%。平均绝对误差分别为0.068、0.169、0.239、0.314 m。表明:SAPSO-BP算法预测精度最高,提高了BP模型的预测精度,其精度满足工程实际需要。并采用Sobol指数法探究了相关影响因素对有效致裂半径的敏感度,表明:敏感度由高到低依次为地应力、弹性模量、瓦斯压力、泄放压力、致裂器间距、钻孔直径、抗拉强度,可为CO_(2)相变致裂的工程设计提供理论支持。

基于SACPS算法的住宅小区电动汽车集群有序充电

针对传统电动汽车有序充电存在的充电影响因素考虑不全、优化目标过于单一、充电体验不友好等问题,以住宅小区电动汽车集群充电为研究对象,构建集群有序充电模型,提出模拟退火的混沌粒子群(simulated annealing chaotic particle swarm,简称SACPS)算法,且使用该文算法对集群有序充电模型进行优化,最后对优化结果进行仿真实验.仿真实验结果表明:相对于其他2种算法,该文算法能使电动汽车集群有序充电模型取得更低的最佳适应度;与集群无序充电相比,SACPS算法的集群有序充电的负荷峰值、负荷峰谷比、充电费用分别降低了42.62%,96.81%,15.61%;SACPS算法的集群有序充电在一定程度上实现了与其他负荷的错峰用电.因此,SACPS算法具有优越性.

基于ASAPSO算法的特钢配料成本优化模型研究

在特种钢材冶炼生产中,配料不仅影响生产成本,其准确性也关系冶炼特钢的品质和成功率。针对配料在保证冶炼成功的前提下尽量减少成本问题,提出了基于自适应模拟退火粒子群(ASAPSO)算法的特钢配料成本优化模型。首先以物料守恒为基础,考虑中频炉冶炼工艺约束并结合冶金反应工程理论,建立以最低配料成本为目标的最优化模型。然后采用ASAPSO算法对模型求解,并对比粒子群算法、遗传算法和差分进化算法的求解结果,实验结果表明:ASAPSO算法的总体性能优于粒子群算法,其解的平均值和标准差优于其他三个算法,求解结果更稳定,可为企业实际配料生产提供一定的参考意义。

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。

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。

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。