Optimal distribution of reliability for a large network based on connectivity

It is a non-polynomial complexity problem to calculate connectivity of the complex network. When the system reliability cannot be expressed as a function of element reliability, we have to apply some heuristic methods for optimization based on connectivity of the network. The calculation structure of connectivity of complex network is analyzed in the paper. The coefficient matrixes of Taylor second order expansion of the system connectivity is generated based on the calculation structure of connectivity of complex network. An optimal schedule is achieved based on genetic algorithms （GA）. Fitness of seeds is calculated using the Taylor expansion function of system connectivity. Precise connectivity of the optimal schedule and the Taylor expansion function of system connectivity can be achieved by the approved Minty method or the recursive decomposition algorithm. When error between approximate connectivity and the precise value exceeds the assigned value, the optimization process is continued using GA, and the Taylor function of system connectivity needs to be renewed. The optimization process is called iterative GA. Iterative GA can be used in the large network for optimal reliability attribution. One temporary optimal result will be generated every time in the iteration process. These temporary optimal results approach the real optimal results. They can be regarded as a group of approximate optimal results useful in the real project.     

Optimization of heat pump using fuzzy logic and genetic algorithm

Heat pumps offer economical alternatives of recovering heat from different sources for use in various industrial, commercial and residential applications. In this study, single-stage air-source vapor compression heat pump system has been optimized using genetic algorithm (GA) and fuzzy logic (FL). The necessary thermodynamic properties for optimization were calculated by FL. Thermodynamic properties obtained with FL were compared with actual results. Then, the optimum working conditions of heat pump system were determined by the GA.     

面向工程全局优化的混沌优化算法研究进展Research advances of chaos optimization algorithms for engineering global optimization

近年来,基于混沌的初值敏感性、伪随机性、遍历性以及自相似分形等非线性动力学特性所发展的混沌优化方法,是一种有潜力的工程全局优化新工具,已广泛应用于科学与工程技术的各学科领域。根据混沌优化方法的发展历程,以算法基本思想和工程应用研究状况为重点,评述了混沌神经网络优化方法、第一类混合混沌优化算法（基于混沌搜索）、第二类混合混沌优化算法（混沌序列代替随机序列）以及混沌分形优化四种主要混沌优化算法。混沌映射最早被引入神经网络,发展了混沌神经网络优化方法,可解决复杂的组合优化等全局优化问题。遗传算法及粒子群等启发式随机算法虽具全局搜索能力,但易出现早熟并陷入局部最优。然后,出现了混沌搜索的概念,研究者将其嵌入启发式算法建立了第一类混合混沌优化算法,可有效克服原启发式算法早熟收敛的缺点。随后,利用混沌映射产生的混沌序列代替启发式算法中的随机参数形成了第二类混合混沌优化算法。混合混沌优化算法有益于实现快速全局收敛和提高计算精度。最后,利用混沌分形特性,从分形理论出发提出一类新颖的混沌分形优化算法,可搜索到优化问题的所有全局最优解。此外,对混沌优化算法研究的几个发展方向进行了展望,诸如加强混沌优化算法的参数设计、处理大规模优化、多目标优化问题以及使用代理模型等。     

Adoption of a genetic algorithm (GA) for tomographic reconstruction of line-of-sight optical images

 A new tomographic reconstruction scheme is proposed that uses a genetic algorithm (GA), a robust and combinatorial function optimization based on the mechanics of the genetic principle. The paper first discusses the implicitly parallel and scaled random nature of the GA optimization using an illustrative example. An introduction of the elementary distribution function (EDF) to constitute the cross-sectional field shows a successful adoption of GA for optical tomography. The GA-based tomography was examined for interferometric projections of computer-synthesized phantom density fields. The GA-based tomography shows accurate and stable image reconstruction, particularly for limited projections. Received: 6 November 1995 / Accepted: 6 May 1996     

AN EFFICIENT GENETIC ALGORITHM FOR LARGE SCALE BUILT-UP STRUCTURAL OPTIMIZATION

In this paper,a new zigzag method for plate structures and a geneticalgorithm (GA) of dynamic source seed spaces are developed and a combination ofthem is used to deal with large scale built-up structural optimization.The new GAcombined with the zigzag method can work efficiently when coping with large scalestructural optimization included displacement and stress constraints.Examples showthat this GA is robust and can be used for many complex structural optimizationproblems.     

桁架拓扑优化的多点逼近遗传算法

提出一种基于多点逼近函数和遗传算法的桁架拓扑优化方法。该方法建立了包含连续尺寸和离散拓扑两类变量的优化模型，并通过构造多点逼近函数建立了结构优化问题的第一级序列显式近似，然后采用分层优化方法：在外层对拓扑变量采用遗传算法进行优化；在内层对尺寸变量通过可由对偶法求解的第二级序列近似问题进行优化。几个经典的桁架拓扑优化算例表明该方法能以较少的结构分析次数获得比较理想的概率意义上的最优解。     

结构优化的GA算法

本文提出了一种用于结构优化的具有动态种源空间的ＧＡ算法，这种ＧＡ算法可以与其它结构优化方法如齿行法结合起来处理结构优化问题。这种ＧＡ具有很好的鲁棒性和较高的效率，可作为一个黑箱处理相当广泛的结构优化问题。     

GA based meta-modeling of BPN architecture for constrained approximate optimization

Artificial neural networks (ANN) have been extensively used as global approximation tools in the context of approximate optimization. ANN traditionally minimizes the absolute difference between target outputs and approximate outputs, thereby resulting in approximate optimal solutions being sometimes actually infeasible when it is used as a meta-model for inequality constraint functions. The paper explores the development of the modified back-propagation neural network (BPN) based meta-model that ensures the constraint feasibility of approximate optimal solution. The BPN architecture is optimized via genetic algorithm (GA) to determine integer/continuous decision parameters such as the number of hidden layers, the number of neurons in a hidden layer, and interconnection weights between layers in the network. The verification of the proposed approach is examined by adopting a number of standard structural problems and an optical disk drive (ODD) suspension problem. Finally, GA based approximate optimization of suspension with optical flying head (OFH) is conducted to enhance the shock resistance capability in addition to dynamic characteristics.     

基于拟满应力设计和遗传算法的网架截面优化方法

通过在遗传算法中嵌入拟满应力算子,提出了一种以网架结构杆件截面作为离散变量的优化设计方法,即基于拟满应力设计和遗传算法的网架截面优化方法.分析结果表明,该法能够提高遗传算法的搜索效率和获得全局最优解的可靠性,对于同时有应力和位移约束的网架等空间结构截面优化问题,这种混合算法有较高的效率.     

基于遗传算法和参数化建模的非线性结构优化

提出了一种对存在接触关系的非线性结构(装配体)进行优化设计的新方法。该方法将遗传算法与结构几何及有限元参数化建模方法相结合，在通用CAE软件的二次开发编程环境中实现对带接触的结构装配体进行结构尺寸和形状优化设计。文中利用该方法对某浮动式闭气结构的重要结构参数和关键构件形状实施了优化设计，使其闭气性能得到大幅度提高，体现了本文方法在解决这类优化问题中的优势。本文的方法有利于拓宽结构优化技术在机械设计领域的应用范围。     

Unsupervised neural network model optimized with evolutionary computations for solving variants of nonlinear MHD Jeffery-Hamel problem

A heuristic technique is developed for a nonlinear magnetohydrodynamics(MHD) Jeffery-Hamel problem with the help of the feed-forward artificial neural network(ANN) optimized with the genetic algorithm(GA) and the sequential quadratic programming(SQP) method. The two-dimensional(2D) MHD Jeffery-Hamel problem is transformed into a higher order boundary value problem(BVP) of ordinary differential equations(ODEs). The mathematical model of the transformed BVP is formulated with the ANN in an unsupervised manner. The training of the weights of the ANN is carried out with the evolutionary calculation based on the GA hybridized with the SQP method for the rapid local convergence. The proposed scheme is evaluated on the variants of the Jeffery-Hamel flow by varying the Reynold number, the Hartmann number, and the angles of the walls. A large number of simulations are performed with an extensive analysis to validate the accuracy, convergence, and effectiveness of the scheme. The comparison of the standard numerical solution and the analytic solution establishes the correctness of the proposed designed methodologies.     

GA-BPSO混合优化中红外光谱特征波段筛选的润滑油添加剂种类识别技术

针对各种设备润滑油中微量多品种添加剂种类识别问题,提出二进制粒子群算法结合遗传算法(GA-BPSO)混合优化中红外光谱特征波段筛选方法. 首先建立K近邻算法(KNN)和随机森林算法(RF)的润滑油添加剂种类识别基础分类模型;然后通过GA-BPSO混合优化算法在光谱全波段范围内筛选特征波段区域,消除干扰及无效信息,压缩庞大光谱数据集,降低搜索空间维度;再以模型识别准确率作为评价标准,用优选出的特征波段在基础分类模型上构建高性能增强分类模型. 选取硫化异丁烯(T321)、烷基二苯胺(T534)和硫化磷酸胺盐(T307) 三种润滑油添加剂作为测试对象,以不同配比混合在基础油中,采集配制样品的中红外光谱数据,并划分为训练集与测试集,分别导入基础分类模型与增强分类模型进行训练及测试. 结果显示,GA-BPSO优化筛选特征波段,使KNN的有效波段长度削减至原来的16.4%,识别准确率从70%提高到89.58%;RF的有效波段长度削减至原来的15.8%,识别准确率从85%提升至97.5%. 对比研究发现,GA-BPSO混合特征波段优选方法明显优于GA和BPSO单独使用时的筛选结果,在极大地减轻运行负担的同时,有效提高了模型多种类同步识别的准确率和稳定性.      

On the comparative optimal analysis and synthesis of four-bar function generating mechanism using different heuristic methods

In this research, optimal synthesis of function generation of four-bar linkages is investigated using different heuristic optimization methods. The novelty of this study is that we considered the five precision point’s distribution and both stroke angles of the follower and crank links as the optimization variables. Therefore, a non-linear optimization was done with seven variables. Different optimization techniques, including heuristic and gradient based methods were used for optimization and were completely compared. Finally, general solutions were found and distribution of precision points for some desired functions was rationalized. GA-Hybrid seems to be the best method in finding global optimums in cases that the problem is sorely nonlinear, and also it is fast. Even it seems that distribution of precision points is independent of stroke angles of the follower and crank links. Some precision points are out of the design domain, and they are sorted in a way that they can describe the behavior of desired function better.     

基于遗传算法的抗震钢框架多目标优化设计

考虑抗震钢框架优化问题具有多目标的特点,在遗传算法的基础上对抗震钢框架多目 标优化设计进行了探讨. 在无约束Pareto排序遗传算法的基础上,提出了一个简单、实用 而又可以避免采用罚函数的全新排序方法,在此基础上形成了一种求解有约束多目标优化 问题的Pareto遗传算法(CMOPGA), 并给出了具体的算法流程图. 以钢框架重量最轻和结构 总动应变能最小为目标,基于相关的设计规范,给出了抗震钢框架多目标优化问题的一种合 理提法. 采用CMOPGA对一个两跨六层抗震钢框架实例进行了多目标优化设计,并提出了一 个在Pareto最优解集的基础上选取妥协解的相对最小距离妥协原则. 算例结果表明,采用 CMOPGA求解抗震钢框架多目标优化问题是可行和有效的.     

基于CMDLAC指标和遗传算法的结构损伤定位研究

提出了基于综合多损伤定位保证准则(CMDLAC)指标和遗传算法(GA)的结构损伤定位方法。基于结构损伤标量模型,结合灵敏度分析提出了基于频率和振型变化的CMDLAC指标,将损伤识别问题转化为约束优化问题,最后以CMDLAC指标为优化目标函数应用遗传算法求解实现了结构的损伤定位。在考虑测试自由度不完备和测量噪声等因素影响的情况下,对桁架结构损伤的数值模拟分析表明了方法的有效性,验证了CMDLAC指标定位损伤和抵抗噪声的能力,同时指出其具有较强的实际可操作性。     

A COMBINAT0RIAL ALGORITHM FOR THE DISCRETE OPTIMIZATION OF STRUCTURES

I.Intr0ducti0nAmajorithofthestructuraloptimumproblemsarediscreteonesinengineeringpractice.Becauseofthebasicreason,namelythediscontinuityofdesignvariables,thediscreteoptimizationhastwoprominentdifficulties:(l)Themodelsofdiscreteoptimizationsarenonconvexpro…     

改进单向搜索遗传算法的工程结构优化设计

本文基于规范规定的约束条件和各项技术标准要求，建立了离散变量结构优化模型。针对遗传算法在迭代过程中经常出现未成熟收敛、振荡、随机性太大和迭代过程缓慢等缺点，提出一种新的遗传算子单亲遗传算子，用于对遗传算法的改进。并提出一种离散变量结构优化设计的单向搜索算法与遗传算法结合在一起解决问题。优化设计结果表明，这种改进单向搜索遗传算法的收敛特性得到了很好的改善，即发挥了单向搜索算法局部搜索能力强的特点，又发挥了遗传算法全局性好的特点。该方法是一种有效的工程结构优化设计方法。     

桁架结构截面优化设计的改进模拟退火算法

将模拟退火算法应用于桁架结构截面尺寸优化设计,提出若干方法改进了算法的鲁棒性、计算效率和求解精度。通过一批经典问题,同时与传统结构优化算法和遗传算法进行了比较。数值结果表明,本文的改进模拟退火算法具有很高的优化求解精度,计算效率有显著提高且优于遗传算法,有望在结构优化设计问题中发挥其特点。     

Chaotic ant swarm for the traveling salesman problem

Chaotic ant swarm (CAS) is an optimization algorithm based on swarm intelligence theory, which has been applied to find the global optimum solution in continuous search space. However, it is not able to solve the combinational optimization problem directly, e.g., the traveling salesman problem (TSP). To tackle this problem, we propose a new method to solve the traveling salesman problem based on chaotic ant swarm, CAS-TSP for short. The CAS-TSP is developed by introducing a mapping from continuous space to discrete space, reverse operator and crossover operator into the CAS. Computer simulations demonstrate that the CAS-TSP is capable of generating optimal solution to instances of the TSPLIB in almost all test problems of sizes up to 150. Also a comparative computational study shows that this CAS-TSP algorithm is an efficient tool for solving TSP and this heuristic is competitive also with other heuristics.     

A combinatorial algorithm for the discrete optimization of structures

The definition of local optimum solution of the discrete optimization is first given, and then a comprehensive combinatorial algorithm is proposed in this paper. Two-level optimum method is used in the algorithm. In the first level optimization, an approximate local optimum solution is found by using the heuristic algorithm, relative difference quotient algorithm, with high computational efficiency and high performance demonstrated by the performance test of random samples. In the second level, a mathematical model of (-1, 0, 1) programming is established first, and then it is changed into (0, 1) programming model. The local optimum solution X^* will be from the (0, 1) programming by using the delimitative and combinatorial algorithm or the relative difference quotient algorithm. By this algorithm, the local optimum solution can be obtained certainly, and a method is provided to judge whether or not the approximate optimum solution obtained by heuristic algorithm is an optimum solution. The above comprehensive combinatorial algorithm has higher computational efficiency.Project supported by Natural Science Foundation of Shandong Province