跨声速压气机转子多目标气动优化设计研究

采用数值优化方法对跨声速压气机转子NASA rotor37进行了多目标的数值三维气动优化设计,并对设计前后的几何形状、总体性能及流场的变化进行了详细的分析对比.结果表明,利用多目标遗传算法或者多目标模拟退火算法进行多目标的气动优化设计所得出的分布是基本一致的.各性能参数及流场结构的变化还是主要来源于前缘激波结构的变化.     

遗传算法的群体模式多样性与模式生长方程(Ⅱ)

在给定个体平均相似度的条件下,详细推导了单倍体和二倍体遗传算法在一致杂交算子作用下的模式生长方程,并给出了一致杂交算子时遗传算法的模式定理．     

火电站多目标负荷调度及其算法的研究

对传统意义下负荷调度模型进行修正,同时考虑最小化燃料费用和污染排放量,提出了火电站多目标负荷调度模型;并将强度Pareto进化算法(SPEA2)与并行遗传算法(PGA)相结合对其求解.结果表明:该算法求得的Pareto最优解分布均匀、收敛速度快、寻优能力强,决策者可根据不同的侧重点在Pareto解集中选择最终的满意解.应用该算法对某电厂进行多目标负荷调度,验证了其可行性和有效性.     

伪多目标模糊遗传算法在结构损伤识别中的应用

将模糊优选理论与多目标遗传算法相结合,提出了基于模糊优选理论的伪多目标遗传算法,解决了结构多损伤中传统遗传算法对载荷不敏感部位识别难、精度不高、运算效率低的不足,并将该方法应用于两端固支梁的损伤程度识别中.实例表明,该法对结构中单个单元损伤和多单元损伤程度识别准确、精度高、收敛速度快,对大型复杂结构的损伤程度识别具有广阔的应用前景.     

多目标非线性水资源优化配置模型的混合遗传算法

针对某市水资源优化配置模型的多目标、非线性特点,提出大系统总体优化的遗传算法求解方法.遗传算法构造中,对连续变量离散化采用浮点数级联编码;依据精确不可微罚函数法,构建个体适应度评价函数;将模拟退火算法与遗传算法相结合,探讨了基于模拟退火的混合遗传算法在水资源优化配置中的实际应用.实例表明,混合遗传算法是解决水资源优化配置多目标、非线性问题的有效途径.     

NSGA-Ⅲ算法在水资源多目标优化配置中的应用

针对红延河调水工程规划中多水源、多受水子区、多决策者间相互协调难的问题,建立了考虑经济、社会和环境效益的红延河调水工程的水资源多目标优化配置数学模型,采用基于参考点选择机制的遗传算法NSGA-Ⅲ得到受水区水资源多目标配置的Pareto解集.遗传算法NSGA-Ⅲ的Sp值总是小于NSGA-Ⅱ的Sp值,说明NSGA-Ⅲ更能提...     

基于自适应遗传算法的协调控制系统优化

针对火电单元机组的协调控制系统,提出了一种基于自适应遗传算法的多变量鲁棒PID控制器参数寻优方法.以控制器设定点跟踪性能为优化目标,鲁棒性能为动态约束,采用具有自适应交叉概率和变异概率的遗传算法寻优PID控制器参数.仿真结果表明,与传统遗传算法寻优所得PID控制器相比,基于自适应遗传算法的鲁棒PID控制器具有良好的负荷...     

基于改进多种群遗传算法的节能环保多目标优化模型

电力系统多目标经济负荷分配问题是个非线性、高维的复杂优化问题。提出基于交互式的改进多种群遗传算法,通过引入精英策略和移民策略的多种群遗传算法可以有效地克服标准遗传算法容易陷入局部最优解、易早熟的缺陷。针对文中提出的煤耗和排放2个目标函数,提出了基于目标满意度和总体协调度的交互式多目标处理方法,通过寻求向量空间内满足总体协调度的最短”欧氏距离”,来贴近决策者满意的理想值,解决了各目标函数之间最优解的相互冲突,达到协调好各个目标函数的目的,充分体现了决策者的意愿。试验算例表明,该算法能够有效地解决电力系统多目标经济负荷分配问题。     

管翅式换热器遗传算法优化

管翅式换热器作为一种高效的换热设备,提升其换热效率和降低投资成本显得至关重要.通过单目标遗传算法(GA)和多目标非支配排序遗传算法(NSGA-Ⅱ)对管翅式换热器进行优化设计,设置翅片高度、翅片间距、管长、横向管数和纵向管数5个自变量的合理设计范围,单目标优化选用换热器效率、压降熵产和最大收益3个目标函数,根据热力学第一...     

基于自组织映射遗传算法的水库多目标优化调度研究

为了弥补遗传算法易陷入局部解的缺陷,使该算法产生的非劣解更好更均匀地靠近非劣解前沿,并且使多目标调度结果更贴近真实情况,将自组织映射和遗传算法相结合,应用到以发电和供水为目标的水库多目标调度中,并分别与NSGA2算法、主要目标法进行比较分析,验证该算法的合理性与优越性。结果表明,自组织映射遗传算法能在满足约束条件的情况下,使发电供水两目标调度的非劣解更好地向非劣解前沿靠近,给出的实时供水方案更符合实际情况。     

A fast EIT image reconstruction method for the two-phase flow visualization

A preliminary investigation is introduced to demonstrate the feasible potentials of the application of the EIT (Electrical Impedance Tomography) to visualize the bubble distribution in two-phase flow field. We expect the required experimental apparatus for the EIT bubble distribution measurement to be rather simple thus much cheaper than the other bubble motion monitoring devices like LDV (Laser Doppler Velocïmetry), PIV (Particle Image Velocimetry) and optical probes. At the present stage, however, the EIT visualization of the bubble distribution takes too long time to be implemented. In this paper, an adaptive mesh grouping method based on fuzzy-GA (Genetic Algorithm) is introduced to reduce the image reconstruction time significantly. Sample reconstructed images by the proposed method are presented with discussion for several ‘artificial’ bubble distributions.     

PEM fuel cell supported distribution static compensator for power quality enhancement in three-phase four-wire distribution system

One of the major problems in electrical power system is the lack of quality of power due to the rapid growth of nonlinear load and unbalanced load utilization in three-phase four-wire distribution system. In this paper, PEM (Proton Exchange Membrane) fuel cell supported four-leg Distribution Static Compensator (DSTATCOM) is modelled to mitigate harmonics, neutral current and load balancing under nonlinear load and unbalanced load conditions in three-phase four-wire distribution system. The instantaneous reactive power (IRP) theory control algorithm is proposed for four-leg DSTATCOM. The Real coded Genetic Algorithm (RGA) optimized Proportional Integral (PI) controller and Adaptive Neuro Fuzzy Inference System (ANFIS) controller are used for regulating the DC link voltage of DSTATCOM. This paper also investigates the performance of ANFIS based DSTATCOM with conventional method. The proposed system is modelled and its performance is analyzed in MATLAB/SIMULINK.     

Allocation and sizing of battery energy storage system for primary frequency control based on bio-inspired methods: A case study

This paper presents a new procedure for optimal allocation and optimal sizing of a battery energy storage system (BESS) for primary frequency support in an isolated power system. For the BESS allocation, a transmission bus system with larger frequency decline is recognized and the BESS sizing is performed by a constrained optimization strategy based on a new modified metaheuristic, called Developed Harris Hawks Optimization (DHHO). The simulation results of the suggested DHHO are compared with Bat Optimization Algorithm (BOA) and Genetic Algorithm (GA) from the literature to show the method efficiency. The final results showed higher precision with lower required iterations for the suggested DHHO method. Also, the proposed DHHO gives lower investment costs for BESS with lower power and energy requirement toward the other compared methods.     

Bacterial Foraging Algorithm based solar PV parameter estimation

The abundance and non-polluting nature of solar energy has aroused the interest of many researchers. This worldwide attention of photovoltaic panels has led to the need of generating accurate model for solar photovoltaic (PV) module before proceeding to the installation part. However, accurate modeling of solar PV characteristics is difficult; since the manufacturer’s datasheet provides only four values such as V_mp, I_mp, V_oc, and I_sc. Further, for accurate modeling precise estimation of model parameters at different environmental conditions are very essential. On the other hand, optimization technique is a very powerful tool to obtain solutions to complex non-linear problems. Hence, in this paper, Bacterial Foraging Algorithm is proposed to model the solar PV characteristics accurately. A new equation has been evolved to determine the values of V_oc, V_mp accurately; since these values decides the closeness of the simulated characteristics. Model parameters are extracted for three different types of solar PV panels. A systematic evaluation and performance comparison of Bacterial Foraging Algorithm with other optimization techniques such as Genetic Algorithm and Artificial Immune System has been done and the best computational technique is derived based on performance criteria such as accuracy, consistency, speed of convergence and absolute error. Extensive computations are carried out for the proposed method, as well as for Genetic Algorithm and Artificial Immune System to substantiate the findings.     

Optimal design of plate-fin heat exchangers by a hybrid evolutionary algorithm

This study explores the first application of a Genetic Algorithm hybrid with Particle Swarm Optimization (GAHPSO) for design optimization of a plate-fin heat exchanger. A total number of seven design parameters are considered as the optimization variables and the constraints are handled by penalty function method. The effectiveness and accuracy of the proposed algorithm is demonstrated through an illustrative example. Comparing the results with the corresponding results using GA and PSO reveals that the GAHPSO can converge to optimum solution with higher accuracy.     

Neuro-genetic optimization of laminar slot jets impinging on a moving surface

In this work, heat transfer from a moving surface due to series of impinging slot jets under laminar conditions has been optimized. For this study numerical investigations were carried out initially using Ansys Fluent 14 and these results were used to train an artificial neural network (ANN). This trained network was integrated into Micro-Genetic Algorithm to get the optimum parameters for better heat transfer from the surface, an optimization procedure proposed by Madadi and Balaji. Pitch of the jets (P), height of the jets (H) and the non-dimensional surface velocity (V_s) were chosen as dependent variables for optimum heat transfer. 99 simulations were performed by changing above parameters for each Reynolds number, Re of 100 and 200 were used for case study. Imposition of surface velocity strongly affects the heat transfer magnitude and distribution following a change in flow structure. The performance of Micro-Genetic Algorithm (μGA) was also compared with standard Genetic Algorithm (GA); it shows that μGA reaches optimum in less than half the time of standard GA. The optimum results show that the pitch of the jets, height of the jets and surface velocity should be as low as possible.     

基于遗传算法和Matlab的一种可靠度计算方法

利用由Matlab语言编制的遗传算法优化工具箱计算工程结构的可靠度，给出了设计验算点，避免了非线性功能函数的泰勒级数展开及遗传算法程序的编制。     

船舶柴油机转速智能控制系统仿真

以MTU16V396柴油机为对象,建立了柴油机控制系统的简化传递函数和执行器模型。采用PID控制,并利用遗传算法优化控制器参数,对柴油机动态调速过程进行仿真分析,和基于Z-N(Ziegler-Nichols)法的参数整定方法做了比较,结果表明遗传算法的性能远优于传统的参数整定法。     

Maximization of generated power from wind energy conversion system using a new evolutionary algorithm

In this paper, a grid-connected Doubly Fed Induction Generator controlled by a Sliding Mode Controller (SMC) is used to maximize the Wind Energy Conversion System (WECS) output power. A SMC is implemented using a PID controller that is tuned using a new algorithm based on hybrid Differential Evolution with a Linearized Biogeography-Based Optimization (LBBO-DE). Biogeography-Based Optimization (BBO) is an evolutionary optimization algorithm based on a mathematical model of organism distribution. BBO permits a recombination of the solutions features by migration. A new migration model based on the sigmoid function is proposed. An analysis of the LBBO-DE is conducted using six different models, including the sigmoid model. Their performance were tested with 23 benchmark functions. The comparison reveals that the sigmoid model has the best performance. Therefore, the LBBO-DE with a sigmoid model is used to optimize the controller parameters to maximize the WECS output power. The LBBO-DE with the sigmoid model is compared with the Tyreus-Luyben tuning method, Genetic Algorithm (GA) and Linearized BBO (LBBO). The results showed that the LBBO-DE has the best performance. The proposed algorithm is verified using an experimental setup for the maximization of the generated power from the WECS and reducing power loss.