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.

Vehicle recognition and tracking based on simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm

Target recognition and tracking is an important research filed in the surveillance industry.Traditional target recognition and tracking is to track moving objects, however, for the detected moving objects the specific content can not be determined.In this paper, a multi-target vehicle recognition and tracking algorithm based on YOLO v5 network architecture is proposed.The specific content of moving objects are identified by the network architecture, furthermore, the simulated annealing chaotic mechanism is embedded in particle swarm optimization-Gauss particle filter algorithm.The proposed simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm(SA-CPSO-GPF) is used to track moving objects.The experiment shows that the algorithm has a good tracking effect for the vehicle in the monitoring range.The root mean square error(RMSE), running time and accuracy of the proposed method are superior to traditional methods.The proposed algorithm has very good application value.

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.

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.

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

Orthomorphism on F2^n is a kind of elementary pemmtation with good cryptographic properties. This paper proposes a hybrid strategy of Particle Swarm Optimization （PSO） and Sirrmlated Annealing （SA） for finding orthomorphisrm with good cryptographic properties. By experiment based on this strategy, we get some orthorrorphisrm on F2^n = 5, 6, 7, 9, 10） with good cryptographic properties in the open document for the first time, and the optirml orthorrrphism on F 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 irrarnity and corresponding pernmtation polynomial degree. The PSOSA hybrid strategy for optimizing orthomerphism in this paper makes design of orthorrorphisrm with good cryptographic properties automated, efficient and convenient, which proposes a new approach to design orthornorphisrm.

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.

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.

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.

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。

高速铁路多列车客票预分与通售策略协同优化

在客票预分方案中设置通售票能使分配票额有效适应售票过程中动态变化的购票需求,但也容易出现通售票过早肢解,从而导致后续长途OD对旅客购票失败、通售票无法充分利用。为避免此情况出现以进一步提高通售票的利用率,考虑通售票销售策略与铁路客票预分方案的协同优化问题,实现由OD固定票、通售票构成的客票预分方案与通售票发售策略(以下简称“通售策略”)的协同优化。首先,基于旅客随机购票需求描述、旅客购票列车选择分析,构建铁路客票预分方案与通售策略协同优化模型,以客票总收益最大化为目标,考虑列车席位能力、列车服务、客票量上下限以及通售发售策略等约束;其次,在生成多套购票需求样本的基础上,结合旅客出行购票过程的仿真,设计模型求解的粒子群算法。算例结果表明对比不考虑通售策略的客票预分方案,本文优化方法能使列车平均客座率提高4.96%、列车客票总收益提高2.74%,充分验证了引入通售策略能有效实现客票的灵活与充分利用。

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。

基于多目标粒子群优化算法的某轻型商用车操纵稳定性优化研究

针对某轻型商用车稳态回转时侧倾度偏大的问题对其悬架进行优化改进。基于ADAMS/car搭建整车多体动力学模型,通过前悬架反向平行轮跳试验、后悬架理论计算验证了悬架仿真模型的准确性。进行整车稳态回转工况和转向盘中间位置转向工况仿真分析,结果表明,车身侧倾度偏高。为实现操纵稳定性优化分析的流程自动化,提出了基于modeFRONTIER的联合仿真方法。以悬架设计参数为优化变量,以汽车的侧倾度与横摆角速度响应滞后时间为优化目标,采用拉丁超立方试验设计方法拟合得到混合代理模型,并结合多目标粒子群优化算法对悬架系统进行多目标优化,获得了悬架系统优化方案。优化结果显示,在不影响平顺性的前提下,汽车车身侧倾度降低了13.93%,横摆角速度响应滞后时间降低了2.75%,整车操纵稳定性得到了提升。

考虑电-氢-热多能互补的微网多目标优化配置

氢储能具有储能容量大、储存时间长、清洁无污染、可实现多种能源网络互联互补和协同优化等诸多优点,有望成为推动分布式能源发展和提升终端能源利用效率的重要支撑技术。为了提高独立型微网供电可靠性及可再生能源利用率,文中分析了典型电、氢、热装置的运行特性,提出考虑电-氢-热多能互补的独立微网多目标优化配置模型,并基于模拟退火的粒子群(simulated annealing particle swarm optimization,SAPSO)算法对目标问题进行求解,获得不同配置方案下的技术经济指标。最后,通过东北某地独立微网优化配置算例,基于MATLAB平台验证了所提多能互补配置方案较传统电储能配置方案负荷失电率降低了3.18%,可再生能源利用率提高了8.37%。所提配置方案可有效促进可再生能源消纳,保证独立微网的供电可靠性。