Modified Harmony Search Algorithm and Neural Networks for Concrete Mix Proportion Design

This study proposes a new methodology with harmony search (HS) algorithm and neural networks (NNs) for concrete mix proportioning. The basic procedure for the methodology consists of four steps: (1) constructing a database of mix designs; (2) establishing appropriate models for strength and workability; (3) optimizing mix proportion using the modified HS algorithm; and (4) refining the mixture using NNs. The proposed methodology could be a useful decision-making tool for concrete mix design.     

Hybrid Time-Cost Optimization of Nonserial Repetitive Construction Projects

Time-cost trade-off analysis represents a challenging task because the activity duration and cost have uncertainty associated with them, which should be considered when performing schedule optimization. This study proposes a hybrid technique that combines genetic algorithms (GAs) with dynamic programming to solve construction projects time-cost trade-off problems under uncertainty. The technique is formulated to apply to project schedules with repetitive nonserial subprojects that are common in the construction industry such as multiunit housing projects and retail network development projects. A generalized mathematical model is derived to account for factors affecting cost and duration relationships at both the activity and project levels. First, a genetic algorithm is utilized to find optimum and near optimum solutions from the complicated hyperplane formed by the coding system. Then, a dynamic programming procedure is utilized to search the vicinity of each of the near optima found by the GA, and converges on the global optima. The entire optimization process is conducted using a custom developed computer code. The validation and implementation of the proposed techniques is done over three axes. Mathematical correctness is validated through function optimization of test functions with known optima. Applicability to scheduling problems is validated through optimization of a 14 activity miniproject found in the literature for results comparison. Finally implementation to a case study is done over a gas station development program to produce optimum schedules and corresponding trade-off curves. Results show that genetic algorithms can be integrated with dynamic programming techniques to provide an effective means of solving for optimal project schedules in an enhanced realistic approach.     

Modeling and Optimization for Piercing Energy Consumption

 Energy consumption is an important quality index in the production of seamless tubes. The complex factors affecting energy consumption make it difficult to build its mechanism model, and optimization is also very difficult, if not impossible. The piercing process was divided into three parts based on the production process, and an energy consumption prediction model was proposed based on the step mean value staged multiway partial least square method. On the basis of the batch process prediction model, a genetic algorithm was adopted to calculate the optimum mean value of each process parameter and the minimum piercing energy consumption. Simulation proves that the optimization method based on the energy consumption prediction model can obtain the optimum process parameters effectively and also provide reliable evidences for practical production.     

Optimization of Water Distribution Networks Using Integer Linear Programming

In this study optimum design of municipal water distribution networks for a single loading condition is determined by the branch and bound integer linear programming technique. The hydraulic and optimization analyses are linked through an iterative procedure. This procedure enables us to design a water distribution system that satisfies all required constraints with a minimum total cost. The constraints include pipe sizes, which are limited to the commercially available sizes, reservoir levels, pipe flow velocities, and nodal pressures. Accuracy of the developed model has been assessed using a network with limited solution alternatives, the optimal solution of which can be determined without employing optimization techniques. The proposed model has also been applied to a network solved by others. Comparison of the results indicates that the accuracy and convergence of the proposed method is quite satisfactory.     

Simulated Annealing–Genetic Algorithm for Transit Network Optimization

This paper presents a mathematical stochastic methodology for transit route network optimization. The goal is to provide an effective computational tool for the optimization of a large-scale transit route network to minimize transfers with reasonable route directness while maximizing service coverage. The methodology includes representation of transit route network solution search spaces, representation of transit route and network constraints, and a stochastic search scheme based on an integrated simulated annealing and genetic algorithm solution search method. The methodology has been implemented as a computer program, tested using previously published results, and applied to a large-scale realistic network optimization problem.     

基于改进鸽群优化和马尔可夫链的多无人机协同搜索方法

针对多无人机在协同搜索过程中存在重复搜索、目标静止、搜索效率低的问题,提出基于改进鸽群优化和马尔可夫链的多无人机协同搜索方法.首先,建立类似传感器探测范围的蜂窝状环境模型,降低对搜索区域的重复搜索;其次,建立满足高斯分布的马尔可夫链动态目标运动模型;然后,将柯西扰动引入基本鸽群优化算法的地图和指南针算子,高斯扰动引入地标算子,同时利用模拟退火机制保留次优个体,进而有效缓减基本鸽群优化算法易陷入局部最优的问题.最后,通过仿真实验将本文算法与其他群体智能算法进行比较,结果表明新型算法的合理性和有效性.      

基于乌鸦搜索算法的行星滚柱丝杠参数优化

针对行星滚柱丝杠结构参数匹配问题,提出一种基于乌鸦搜索算法的参数优化模型.考虑行星滚柱丝杠螺纹啮合点位置的影响,建立丝杠、滚柱以及螺母空间螺旋曲面方程,得到螺纹啮合点处位置与螺纹牙厚之间的关系.根据螺纹副和齿轮副啮合关系,确定内齿圈和滚柱端部轮齿设计参数.利用空间螺旋曲面方程,获得螺纹啮合点处的法向量,并推导行星滚柱丝杠各零件间的受力关系.以行星滚柱丝杠结构参数作为设计变量,以螺母外径、丝杠中径、滚柱长度等参数最小为优化目标,考虑行星滚柱丝杠结构约束和主要承力部件强度约束,利用乌鸦搜索算法作为优化算法,建立行星滚柱丝杠参数优化模型,从而实现优化变量最佳匹配.最后,针对三种负载,利用该优化模型得到三组行星滚柱丝杠结构参数,并将其优化结果与国外产品手册进行对比,从而验证了本文优化模型有效性.     

应急救援物资车辆运输路线多目标优化

运用运筹学中图论及多目标优化的理论和方法建立应急救援物资车辆最佳运输路线的选择模型,并基于启发式算法求解该模型.从静态网络应急物资车辆运输路线的双目标优化问题入手,设计适合本文模型的算法,并将之推广至含有三个及三个以上优化目标的路线选择问题.引入时间扩展图的概念,将动态网络中的最佳运输路线问题转化为静态网络中的路径选择问题.算法实质是通过构造辅助决策函数实现Dijstra算法的调用,并在辅助函数构成的搜索空间上寻找最优解,是一种快速的、近似的算法.利用随机路网和真实路网测试本文算法,测试结果与本文的理论分析一致,证明本文算法在应急救援物资车辆运输路线的多目标优化问题中可行且有较好的应用效果.      

Bayesian Probabilistic Inference for Nonparametric Damage Detection of Structures

This paper presents a Bayesian hypothesis testing-based probabilistic assessment method for nonparametric damage detection of building structures, considering the uncertainties in both experimental results and model prediction. A dynamic fuzzy wavelet neural network method is employed as a nonparametric system identification model to predict the structural responses for damage evaluation. A Bayes factor evaluation metric is derived based on Bayes’ theorem and Gaussian distribution assumption of the difference between the experimental data and model prediction. The metric provides quantitative measure for assessing the accuracy of system identification and the state of global health of structures. The probability density function of the Bayes factor is constructed using the statistics of the difference of response quantities and Monte Carlo simulation technique to address the uncertainties in both experimental data and model prediction. The methodology is investigated with five damage scenarios of a four-story benchmark building. Numerical results demonstrate that the proposed methodology provides an effective approach for quantifying the damage confidence in the structural condition assessment.     

Damage Detection and Damage Detectability— Analysis and Experiments

A technique to identify structural damage in real time using limited instrumentation is presented. Contrast maximization is used to find the excitation forces that create maximum differences in the response of the damaged structure and the analytical response of the undamaged structure. The optimal excitations for the damage structure are then matched against a database of optimal excitations to locate the damage. To increase the reliability of the approach under modeling and measurement errors, the contrast maximization approach is combined with an approach based on changes in frequency signature. The detectability of any particular damage with the proposed technique depends on the ratio of the magnitude of damage and the magnitude of errors in the measurements, as well as on how much the damage influences the measurements. A damage detectability prediction measure, that incorporates these effects, is developed. The technique is first tested numerically on a 36 degree-of-freedom space truss. To simulate experimental conditions, an extensive study is carried out in the presence of noise. A similar truss is then built and the finite-element method (FEM) model of the structure is corrected using experimental data. The technique is applied to locate the damage in several members. The experimental results indicate that this technique can robustly identify the damaged member with limited measurements and real-time computation. The effectiveness of the damage detection measure is also demonstrated.     

Optimal Layout of Sewer Systems: A Deterministic versus a Stochastic Model

The optimization of a new or partially existing urban drainage system may be modeled as a subproblems sequence of layout and optimal design within the discrete search space. The design optimization, incorporating the optimal selection of the pumping stations, intermediate manholes, pipe sections, and installation depths, for a general system fixed layout in plan, is a high level sequential decision problem which may be efficiently solved deterministically through a multilevel dynamic programming model. The optimal general layout may be selected in a deterministic way by means of a simple economical comparison of all plan solutions having optimized designs, for small to medium sized systems (if the specific restrictions of the applications are appropriately exploited) in practicable computer time. For larger dimension networks, where it is clearly impossible to achieve plan optimization with full enumeration (which is a NP complete), stochastic search models can be used. For the subproblem layout, an effective enumeration model is presented; the results of a stochastic model proposed previously, using simulated annealing for an application example, are compared and discussed in detail.     

基于支持向量机的边坡稳定性预测

为准确快速地对边坡进行稳定性分析,提出基于支持向量机(Support vector machine SVM)的稀土矿开采边坡的SVM预测模型,结合42个稀土矿开采边坡实际案例,并分别采用网格寻优算法、遗传算法(Genetic algorithm GA)、粒子群算法(Particle swarm optimization...     

基于工序加工能力的并行公差优化设计新方法

文献[1]提出了一种基于工序加工能力的并行工序公差优化设计方法,并用优化算法求解优化模型,本文提出了一种将遗传算法与复合形算法相结合而构成的遗传复合形算法。在遗传算法初始化之后,用复合形法先进行局部寻优,选出较好的个体,再用遗传算法在全局寻求最优解,将其应用于并行公差模型,与资料[1]中结果做比较,发现较以前的算法有较大改进。     

Integrated Genetic Algorithm for Optimization of Space Structures

Gradient‐based mathematical‐optimization algorithms usually seek a solution in the neighborhood of the starting point. If more than one local optimum exists, the solution will depend on the choice of the starting point, and the global optimum cannot be found. This paper presents the optimization of space structures by integrating a genetic algorithm with the penalty‐function method. Genetic algorithms are inspired by the basic mechanism of natural evolution, and are efficient for global‐searches. The technique employs the Darwinian survival‐of‐the‐fittest theory to yield the best or better characters among the old population, and performs a random information exchange to create superior offspring. Different types of crossover operations are used in this paper, and their relative merit is investigated. The integrated genetic algorithm has been implemented in C language and is applied to optimization of three space truss structures. In each case, an optimum solution was obtained after a limited number of iterations.     

一种改进的人工蜂群算法——粒子蜂群算法

针对经典人工蜂群算法收敛速率较慢,后期易陷入局部最优解的不足,本文将粒子群算法中"全局最优"的思想引入到人工蜂群算法的改进过程,从而形成了一种新的人工蜂群改进算法——粒子蜂群算法.首先,提出了趋优度的概念,用来衡量引领蜂在有限次迭代过程中向全局最优解靠近或远离的程度,趋优度值可以评价个体的"发展潜力",趋优度值越低的个体,越需要增大变异的程度,以便找到质量更优的解.其次,专门设计了一种新的蜜蜂群体——粒子蜂,在引领蜂变异阶段根据趋优度的大小将引领蜂变异为侦查蜂和粒子蜂,粒子蜂的出现在很大程度上增加了种群的多样性,拓展了算法的搜索范围.然后,通过粒子蜂群算法种群序列是一个有限齐次马尔科夫链和种群进化单调性的分析,验证了本文所提算法的种群序列依概率1收敛于全局最优解集.最后,将本文所提算法应用于多个常见测试函数,并与经典蜂群算法、近年其他文献改进蜂群算法进行了仿真对比研究,仿真结果表明本文所提算法确实加大了种群的分散度、扩宽了搜索范围,从而具有更快的收敛速度和更高的寻优精度.      

带并行腔和重入约束的双臂集束型设备调度方法

为有效解决半导体制造业中带并行腔和重入约束的双臂集束型设备调度问题,提出一种以优化搜索为核心的调度方法.首先,引入优化FIFO (first in first out)搜索规则,并以系统总完工时间最小化作为目标,建立带并行腔和重入约束的四加工腔双臂集束型设备调度数学模型.在此基础之上,结合虚拟缓冲模块的概念,提出一种基于机械臂交换策略的优化搜索算法.最后,对所提出的算法进行仿真实验,实验结果表明该算法是可行且有效的.      

A novel immune genetic algorithm based on quasi secondary response

Combining the advantages of a genetic algorithm and an artificial immune system,a novel genetic algorithm named immune genetic algorithm based on quasi secondary response(IGA-QSR) is proposed. IGA-QSR employs a database to simulate the standard secondary response and the quasi secondary response. Elitist strategy,automatic extinction,clonal propagation,diversity guarantee,and selection based on comprehensive fitness are also used in the process of IGA-QSR.Theoretical analysis,numerical examples of three benchmark mathematical optimization problems and a traveling salesman problem all demonstrate that IGA-QSR is more effective not only on convergence speed but also on convergence probability than a simple genetic algorithm with the elitist strategy (SGA-ES). Besides,IGA-QSR allows the designers to stop and restart the optimization process freely without losing the best results that have already been obtained.These properties make IGA-QSR be a feasible,effective and robust search algorithm for complex engineering problems.     

Superior Exploration–Exploitation Balance in Shuffled Complex Evolution

Numerous applications within water resources require a robust and efficient optimization algorithm. Given that these applications involve multimodal nonconvex and discontinuous search spaces, evolutionary algorithms (EAs)—which are known to possess global optimization properties—have been widely used for this purpose. For an evolutionary algorithm to be successful, two important facets of the search—exploration and exploitation of the search space—need to be addressed. In this study, we address the issue of achieving a superior exploration–exploitation tradeoff in an extensively used EA, the shuffled complex evolution (SCE-UA). A scheme to improve the exploration capability of the SCE-UA in finding the global optimum is suggested. The scheme proposed a systematically located initial population instead of a randomly generated one used in SCE-UA. On a suite of commonly used test functions, the robustness and efficiency of the modified SCE-UA algorithm was compared with the original SCE-UA. It is observed that when the points in the initial population are strategically placed, it leads to better exploration of the search space, and hence, yields a superior balance between exploration and exploitation. This in turn results in a significant improvement in the robustness of the SCE-UA algorithm.     

基于混合策略的转炉终点锰含量预测研究

摘要：转炉终点钢水锰含量预测,对原料添加和冶炼成本节约具有重要作用。针对凭经验预估的终点锰含量值与实际值较大偏差导致的生产成本升高的问题,建立了一种基于混合策略的改进型鲸鱼优化算法(IWOA)与最小二乘向量机(LSSVM)的转炉终点锰含量预测模型,引入柯西变异提高鲸鱼优化算法(WOA)跳出局部最优的能力;借助惯性权重增强鲸鱼算法局部搜索能力和收敛精度;提出差分变异以增加鲸鱼算法在探索末期的物种多样性和降低陷入局部最优概率。实验结果表明,IWOA LSSVM锰含量预测模型不仅在全局和局部寻优以及收敛速度有较大的提升,在误差性能指标方面优势明显,且预测误差于±0.01%间的命中率为93.3%。     

Vibration-based Damage Detection of Structures by Genetic Algorithm

Vibration-based methods are being rapidly applied to detect structural damage. The usual approaches incorporate sensitivity analysis and the optimization algorithm to minimize the discrepancies between the measured vibration data and the analytical data. However, conventional optimization methods are gradient based and usually lead to a local minimum only. Genetic algorithms explore the region of the whole solution space and can obtain the global optimum. In this paper, a genetic algorithm with real number encoding is applied to identify the structural damage by minimizing the objective function, which directly compares the changes in the measurements before and after damage. Three different criteria are considered, namely, the frequency changes, the mode shape changes, and a combination of the two. A laboratory tested cantilever beam and a frame are used to demonstrate the proposed technique. Numerical results show that the damaged elements can be detected by genetic algorithm, even when the analytical model is not accurate.