Study on Multi-stream Heat Exchanger Network Synthesis with Parallel Genetic/Simulated Annealing Algorithm

The multi-stream heat exchanger network synthesis (HENS) problem can be formulated as a mixed integer nonlinear programming model according to Yee et al. Its nonconvexity nature leads to existence of more than one optimum and computational difficulty for traditional algorithms to find the global optimum. Compared with deterministic algorithms, evolutionary computation provides a promising approach to tackle this problem. In this paper, a mathematical model of multi-stream heat exchangers network synthesis problem is setup. Different from the assumption of isothermal mixing of stream splits and thus linearity constraints of Yee et al., non-isothermal mixing is supported. As a consequence, nonlinear constraints are resulted and nonconvexity of the objective function is added. To solve the mathematical model, an algorithm named GA/SA (parallel genetic/simulated annealing algorithm) is detailed for application to the multi-stream heat exchanger network synthesis problem. The performance of the proposed approach is demonstrated with three examples and the obtained solutions indicate the presented approach is effective for multi-stream HENS.

Using genetic/simulated annealing algorithm to solve disassembly sequence planning

Disassembly sequence planning （DSP） plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassembly sequence planning problems efficiently, a product disassembly hybrid graph model, which describes the connection, non-connection and precedence relationships between the product parts, is established based on the characteristic of disassembly. Farther, the optimization model is provided to optimize disassembly sequence. And the solution methodology based on the genetic/simulated annealing algorithm with binaxy-tree algorithm is given. Finally, an example is analyzed in detail, and the result shows that the model is correct and efficient.

Control parameter optimal tuning method based on annealing-genetic algorithm for complex electromechanical system

A new searching algorithm named the annealing-genetic algorithm(AGA) was proposed by skillfully merging GA with SAA. It draws on merits of both GA and SAA ,and offsets their shortcomings.The difference from GA is that AGA takes objective function as adaptability function directly,so it cuts down some unnecessary time expense because of float-point calculation of function conversion.The difference from SAA is that AGA need not execute a very long Markov chain iteration at each point of temperature, so it speeds up the convergence of solution and makes no assumption on the search space,so it is simple and easy to be implemented.It can be applied to a wide class of problems.The optimizing principle and the implementing steps of AGA were expounded. The example of the parameter optimization of a typical complex electromechanical system named temper mill shows that AGA is effective and superior to the conventional GA and SAA.The control system of temper mill optimized by AGA has the optimal performance in the adjustable ranges of its parameters.

Identification of vibration loads on hydro generator by using hybrid genetic algorithm

Vibration dynamic characteristics have been a major issue in the modeling and mechanical analysis of large hydro generators. An algorithm is developed for identifying vibration dynamic characteristics by means of hybrid genetic algorithm. From the measured dynamic responses of a hydro generator, an appropriate estimation algorithm is needed to identify the loading parameters, including the main frequencies and amplitudes of vibrating forces. In order to identify parameters in an efficient and robust manner, an optimization method is proposed that combines genetic algorithm with simulated annealing and elitist strategy. The hybrid genetic algorithm is then used to tackle an ill-posed problem of parameter identification, in which the effectiveness of the proposed optimization method is confirmed by its comparison with actual observation data.

Multicast Routing Based on Hybrid Genetic Algorithm

A new multicast routing algorithm based on the hybrid genetic algorithm (HGA) is proposed. The coding pattern based on the number of routing paths is used. A fitness function that is computed easily and makes algorithm quickly convergent is proposed. A new approach that defines the HGA's parameters is provided. The simulation shows that the approach can increase largely the convergent ratio, and the fitting values of the parameters of this algorithm are different from that of the original algorithms. The optimal mutation probability of HGA equals 0.50 in HGA in the experiment, but that equals 0.07 in SGA. It has been concluded that the population size has a significant influence on the HGA's convergent ratio when it's mutation probability is bigger. The algorithm with a small population size has a high average convergent rate. The population size has little influence on HGA with the lower mutation probability.

A novel procedure for identifying a hybrid QTL-allele system for hybrid-vigor improvement, with a case study in soybean(Glycine max)yield

“Breeding by design” for pure lines may be achieved by construction of an additive QTL-allele matrix in a germplasm panel or breeding population, but this option is not available for hybrids, where both additive and dominance QTL-allele matrices must be constructed. In this study, a hybrid-QTL identification approach, designated PLSRGA, using partial least squares regression(PLSR) for model fitting integrated with a genetic algorithm(GA) for variable selection based on a multi-locus, multi-allele model is described for additive and dominance QTL-allele detection in a diallel hybrid population(DHP). The PLSRGA was shown by simulation experiments to be superior to single-marker analysis and was then used for QTL-allele identification in a soybean DPH yield experiment with eight parents. Twenty-eight main-effect QTL with 138 alleles and nine QTL × environment QTL with 46 alleles were identified, with respective contributions of 61.8% and 23.5% of phenotypic variation. Main-effect additive and dominance QTL-allele matrices were established as a compact form of the DHP genetic structure. The mechanism of heterosis superior-to-parents(or superior-to-parents heterosis, SPH) was explored and might be explained by a complementary locus-set composed of OD+(showing positive over-dominance, most often), PD+(showing positive partial-to-complete dominance, less often) and HA+(showing positive homozygous additivity, occasionally) loci, depending on the parental materials. Any locus-type, whether OD+, PD + and HA+, could be the best genotype of a locus. All hybrids showed various numbers of better or best genotypes at many but not necessarily all loci, indicating further SPH improvement. Based on the additive/dominance QTL-allele matrices, the best hybrid genotype was predicted, and a hybrid improvement approach is suggested. PLSRGA is powerful for hybrid QTL-allele detection and cross-SPH improvement.

基于高维混合模型的离心泵叶轮子午面优化设计

为提高离心泵在设计工况下的运行效率和扬程,提出一种基于高维混合模型的离心泵叶轮优化设计方法.选取一台比转数为157的单级离心泵作为研究对象,通过CFturbo软件对优化变量进行参数化,然后结合数值模拟获得高维混合模型的训练集.在此基础上采用获取的训练集通过MATLAB机器学习得出效率、扬程与优化参数之间关于支持向量回归的高维模型,并采用遗传算法寻优.在设计工况下,所拟合的高维混合模型预测的效率和扬程值比原模型分别高1.5%和3.2 m,数值模拟验证优化方案的效率和扬程分别比原模型高0.9%和2.1 m.算例研究表明,将高维混合模型应用于离心泵叶轮的优化设计中可以实现快速寻优并提高离心泵水力性能.

多能源混合车队生鲜品配送车辆路径问题研究

针对同时考虑取送货和分时电价的电动车与燃油车混合车队车辆路径问题,以车辆固定成本、行驶成本、制冷成本、货损成本、时间窗成本、碳排放成本和充电成本之和最小为目标,构建生鲜品配送车辆路径优化模型,设计融合邻域搜索的遗传模拟退火混合算法并求解。结果表明:相比于取送分离,同时取送货模式能够显著提升配送效率,提高车辆装载率;通过技术升级适当增加电动车的电池容量,能够弱化车辆路径方案对充电设施的依赖程度,有效降低配送成本;通过与遗传算法、遗传变邻域混合算法运行结果对比,验证本文算法的有效性,为冷链物流企业在配送环节实现节能减排、降本增效提供借鉴和参考。

基于Plant Simulation的航空综合机加厂房布局仿真研究

科学的生产设置布局规划对航空制造业降低生产成本、提高产品质量尤为重要.以某民用航空发动机传动系统的齿轮和机匣综合加工厂房的规划设计为例,根据厂房设施布置的一般原则,以齿轮和机匣的年产量目标、产品加工工艺、单工艺加工面积需求为设计输入,并考虑各加工区的加工特点而带来的位置约束性,采用遗传算法与模拟退火算法相结合的混合遗传算法为优化工具,将特定的功能区固化在基因串特定的位置上来满足位置约束.计算得出优化方案后,以Plant Simulation为仿真平台,建立该综合机加厂房的仿真模型,从产量满足率、设备利用率、在制品库存量和生产线稳健性等多个指标进行了设施布局的仿真评价.结果表明,优化后的系统能够很好满足生产纲领,各关键设备负载比较均衡,同时维持低水平的在制品库存量,且生产线稳健性较好.因此,综合运用混合遗传算法与Plant Simulation仿真可以为生产设施布局问题给出一种有效、直观的解决方案,且由离散事件仿真获取的评价指标能深刻体现方案的优劣.

基于Levant微分器的磁浮球控制算法研究

针对永磁电磁混合悬浮系统控制中因信号突变和噪声干扰引起的控制效果不理想等问题,提出一种改进型Levant微分器和PID相结合的控制方法(improve Levant-PID,ILevant-PID)。融合PID控制适应性强和Levant微分器对输入噪声的鲁棒特性优势,解决系统输出抖振问题,利用模拟退火-粒子群优化解决ILevant-PID控制器参数多、关联性强等制约问题。仿真实验表明:与传统PID控制方法相比,ILevant-PID控制方法在阶跃输入下,启动更为平缓,调节时间减小了41.19%,超调量减小了40.36%。ILevant-PID控制器在阶跃输入时有无噪声条件下的稳态误差分别为±0.37 mm及±0.23 mm,相对于PID降低了87%以上。在跟踪方波输入时,ILevant-PID可实现PID无法对8 mm给定信号无超调跟踪,较好地改善PEMS(permanent magnet electro magnetic hybrid suspension)系统控制性能。

混合云环境面向安全科学工作流数据布局策略

为解决混合云环境下科学工作流数据布局问题,在考虑数据的安全需求的前提下,以优化跨数据中心传输时延为目标,提出了一种混合云环境下面向安全的科学工作流布局策略。分析数据集的安全需求以及数据中心所能提供的安全服务,提出安全等级分级规则;设计并提出基于遗传算法和模拟退火算法的自适应粒子群优化算法(adaptive particle swarm optimization algorithm based on SA and GA,SAGA-PSO),避免算法陷入局部极值,有效提高种群多样性;与其它经典布局算法对比,基于SAGA-PSO的数据布局策略在满足数据安全需求的同时能够大大降低传输时延。

考虑混合工艺的自动化码头多设备资源协同调度优化模型和算法设计

Considering the uncertainty of the speed of horizontal transportation equipment,a cooperative scheduling model of multiple equipment resources in the automated container terminal was constructed to minimize the completion time,thus improving the loading and unloading efficiencies of automated container terminals.The proposed model integrated the two loading and unloading processes of“double-trolley quay crane+AGV+ARMG”and“single-trolley quay crane+container truck+ARMG”and then designed the simulated annealing particle swarm algorithm to solve the model.By comparing the results of the particle swarm algorithm and genetic algorithm,the algorithm designed in this paper could effectively improve the global and local space search capability of finding the optimal solution.Furthermore,the results showed that the proposed method of collaborative scheduling of multiple equipment resources in automated terminals considering hybrid processes effectively improved the loading and unloading efficiencies of automated container terminals.The findings of this study provide a reference for the improvement of loading and unloading processes as well as coordinated scheduling in automated terminals.

基于Simulink/嵌入Matlab函数模块的复杂实时混合试验

实时混合试验是在拟动力试验技术的基础上发展起来的新型混合试验技术。域间并行多步长分离积分算法因能解决该技术中多步长协调问题、能提高积分计算效率而在实时混合试验中具有较好的应用前景。探讨了采用Simulink/嵌入Matlab函数模块执行复杂实时混合试验的可行性,归纳了该方法的优点,完成了基于一种并行多步长分离积分算法的实时混合试验。研究表明,该方法便捷可靠,为复杂实时混合试验提供了有效建模工具。

基于混合算法计算炸药JWL状态方程参数的研究

JWL状态方程参数是炸药爆炸膨胀数值模拟仿真的基础,圆筒试验标定法复杂且成本很高,为了准确快速地获得炸药JWL状态方程参数,提出一种基于灰狼粒子群混合算法计算确定炸药JWL状态方程参数的方法。该方法仅把炸药的爆速和初始密度作为已知条件即可计算确定JWL状态方程参数。基于此方法对TNT、PETN 1.77、HMX、PBX-9010、PBX-9501和OCTOL炸药的JWL状态方程参数进行计算,将计算所得参数进行圆筒试验数值仿真模拟,同时与标准参数仿真模拟结果进行对比,仿真结果曲线R 2分别为0.995681、0.999012、0.99999、0.999533、0.999069和0.998904,均大于0.99,说明计算方法可行,可为炸药JWL状态方程参数的获取研究工作提供一定参考。

一种基于用户通勤行为的家庭能量管理优化策略

随着电动汽车快速发展,V2G技术可大幅降低家庭能量管理系统中用户的用能成本,但V2G会影响用户出行。针对此问题提出了一种基于用户通勤行为的家庭能量管理系统优化策略,通过极大似然估计和蒙特卡罗模拟构建用户出行模型,其次将杂交粒子群与混沌算法、免疫算法相融合,利用多重混沌免疫杂交粒子群算法(MCIHPSO)对目标函数进行求解,最后,通过仿真及实验验证了本文所提控制策略显著降低V2G功能对用户出勤的影响。

基于可用度评价的混联装配线缓冲区优化配置方法

针对具有多设备并联、多工位串联、多条子生产线串并联特征的混联装配线,提出在缓冲区总容量约束下,以最大化系统可用度为目标的缓冲区优化配置策略。通过分析缓冲区状态对各工位设备的影响,将带有缓冲区的装配线等效处理;利用通用发生函数和串并联复合算子求解系统的可用度发生函数,建立缓冲区容量与系统可用度的数学模型;最后采用遗传模拟算法求解缓冲区的优化配置方案,以某医疗公司输液针装配线为例,评估与分析算法的优化结果,验证了该方案的可行性。

基于粒子-蚁群混合算法的截割头形状优化设计

针对EBH-150型横轴式掘进机截割头在截割过程中遇到的载荷波动问题,采用粒子-蚁群混合算法对现有抛物线形截割头进行优化设计。利用Matlab数值模拟软件,对截齿的排列参数进行多目标优化,以期减少载荷波动并提升掘进机的工作稳定性。优化结果显示,截割头的截线间距经过调整后,从外向内逐渐减小,使得单个截齿受力更为均匀。横向载荷波动降低了约62%,其他方向的载荷波动也显著降低,均超过50%。这些改进有效提高了掘进机横摆进刀的稳定性,并有助于延长截割头的使用寿命。尽管优化后的截割头在某些方向上的载荷均值有所增加,但载荷峰值降低,避免了单个截齿的过载现象。总体而言,优化设计取得了理想的效果,但仍需通过实际应用进行验证。本研究为掘进机截割头的优化设计提供了一种有效的算法支持,对于提高掘进机的工作效率和安全性能具有重要意义。

基于混合遗传算法和仿真的后桥总成线投产顺序优化研究

多品种、小批量混流生产模式可以使企业适应个性化、定制化的需求,满足消费者个性化需求。合理的投产顺序能够提高人和机器的利用率,进而提高企业的生产效率。以某公司后桥装配线中的后桥总成线为例,建立最小循环周期最短模型,运用混合遗传算法求解出最优投产顺序。在此基础上,借助Flexsim仿真软件,建立后桥总成混流装配模型,对后桥总成过程进行仿真。仿真结果验证了合理的投产顺序能够降低工作站的空闲率和提高产线的产量,进而提高企业生产效率。

Solving algorithm for TA optimization model based on ACO-SA

An ant colony optimization （ACO）-simulated annealing （SA）-based algorithm is developed for the target assignment problem （TAP） in the air defense （AD） command and control （C2） system of surface to air missile （SAM） tactical unit. The accomplishment process of target assignment （TA） task is analyzed. A firing advantage degree （FAD） concept of fire unit （FU） intercepting targets is put forward and its evaluation model is established by using a linear weighted synthetic method. A TA optimization model is presented and its solving algorithms are designed respectively based on ACO and SA. A hybrid optimization strategy is presented and developed synthesizing the merits of ACO and SA. The simulation examples show that the model and algorithms can meet the solving requirement of TAP in AD combat.

Enhanced Heap-Based Optimizer Algorithm for Solving Team Formation Problem

Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF problem.TF has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms.This paper proposes two improved swarm-based algorithms for solving team formation problem.The first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)algorithm.It also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima trapping.The second algorithm is the Chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space.During HBO’s optimization process,a logistic chaotic map is used.The performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills.Furthermore,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.