Population-based optimization in structural engineering: a review

Structural engineering is focused on the safe and efficient design of infrastructure. Projects can range in size and complexity, many requiring massive amounts of materials and expensive construction and operational costs. Therefore, one of the primary objectives for structural engineers is a cost-effective design. Incorporating optimality criteria into the design procedure introduces additional complexities that result in problems that are nonlinear, nonconvex, and have a discontinuous solution space. Population-based optimization algorithms (known as metaheuristics) have been found to be very efficient approaches to these problems. Many researchers have developed and applied state-of-art metaheuristics to automate and optimize the design of real-world civil engineering problems. While there is a large body of published papers in this area, there are few comprehensive reviews that list, summarize, and categorize metaheuristic optimization in structural engineering. This paper provides an extensive survey of a wide range of metaheuristic techniques to structural engineering optimization problems. Also, information is provided on available structural engineering benchmark problems, the formulation of different objective functions, and the handling of various types of constraints. The performance of different optimization techniques is compared for many benchmark problems.

     

基于抽象凸下界估计的群体全局优化算法

针对确定性全局优化算法极高的计算复杂度以及随机性全局优化算法可靠性较低的问题,在群体进化算法框架下,结合抽象凸理论,提出一种基于抽象凸下界估计的群体全局优化算法。首先,对整个初始群体构建抽象凸下界估计松弛模型;然后,利用不断收紧的下界估计信息安全排除部分无效区域,并指导种群更新,同时借助支撑面的下降方向作局部增强;最后,根据进化信息更新支撑面。数值实验结果表明了所提出算法的有效性。     

A memetic algorithm for multiple-drug cancer chemotherapy schedule optimization.

This correspondence introduces a multidrug cancer chemotherapy model to simulate the possible response of the tumor cells under drug administration. We formulate the model as an optimal control problem. The algorithm in this correspondence optimizes the multidrug cancer chemotherapy schedule. The objective is to minimize the tumor size under a set of constraints. We combine the adaptive elitist genetic algorithm with a local search algorithm called iterative dynamic programming (IDP) to form a new memetic algorithm (MA-IDP) for solving the problem. MA-IDP has been shown to be very efficient in solving the multidrug scheduling optimization problem.     

Population-based continuous optimization, probabilistic modelling and mean shift

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.     

融合Powell搜索法的粒子群优化算法

为了进一步提高多模态函数寻优的效率,提出一种融合Powell搜索法的粒子群优化算法.将PSO算法的全局搜索能力与Powell法的强局部寻优能力有机地结合起来,在保证求解速度,尽可能找到全部极值点的同时提高了解的精确性.由于该算法只利用了函数值信息而不需要计算导数,是求解可微和不可微多模态函数优化问题的通用方法.仿真实验表明了新混合算法的有效性.     

混沌梯度组合优化算法

提出一种混沌梯度组合全局优化算法，并对该算法进行了收敛性分析．算法首先采用改进的变步长梯度法得到某个优化值，然后利用变尺度混沌搜索跳出局部极小，经过反复组合迭代，直至到达最优解．仿真结果表明，该算法能充分发挥梯度法寻优的快速性和混沌法寻优的全局搜索能力．     

LS-SVM的组合优化算法研究

根据LS-SVM不具稀疏解的特性,提出了LS-SVM的组合优化算法及其改进算法,并利用整数规划优化了LS-SVM的组合优化算法中划分的样本子集包含的样本个数p。最后,对算法复杂性做了分析,仿真验证了算法的收敛性和有效性。     

结合局部优化算法的改进粒子群算法研究

提出了结合局部优化算法的改进粒子群算法（Combination Particle Swarm Optimization,CPSO）,粒子群算法虽然通过群体规模来规避早熟,但缺乏局部快速搜索能力,因此将局部优化算法与改进粒子群算法相结合,并尝试不同的局部优化算法,例如牛顿法、最速下降法,通过典型函数优化实验表明,与其他改进粒子群算法相比,CPSO具有较强的寻优能力,鲁棒性和较快的收敛速度;实验也表明不同的局部优化算法在不同的特征函数上体现出不同的优势。     

企业投融资组合的模糊模型与优化

以投资组合产出率及目前流行的风险价值VAR为目标函数，研究了在这两个目标下企业投融资组合管理的模糊模型和优化问题，说明了决策变量是财务杠杆和债务结构．给出了金融市场不确定性环境的构造过程，运用进化规划进行优化计算，对不同模糊程度下的债务结构、财务杠杆及其股东权益资本产出率进行了仿真．     

A feedback combination of global optimization and local optimization for robust template-based visual tracking

This paper proposes a robust template-based visual tracking algorithm. The proposed algorithm combines global optimization and local optimization. The global optimization is performed in translational matching, and the local optimization is implemented by gradient descent in homography-based matching. Translational matching is robust to large translation of the reference image, although it is not robust to rotation, or scaling. In contrast, homography-based matching is robust to rotation, and scaling, although it is not robust to large translation. The proposed algorithm is a feedback combination of the two matching algorithms. Translational matching modifies the initial value for gradient descent in homography-based matching. Homography-based matching updates the reference image for translational matching. The proposed feedback combination inherits advantages from both translational matching and homography-based matching. Robot experiments demonstrate the robustness of the proposed feedback combination to composite transformations of translation, rotation, and scaling.     

复杂三角网格模型刀具组合优化加工研究

在满足复杂三角网格模型数控加工的精度要求下,为提高加工效率,提出了刀具组合优化方法。该方法以刀具轨迹长度最短为目标,并综合考虑模型微分几何信息对加工效率和精度的影响。首先根据面片法矢和曲率将模型分割为多个特征子区域,不同类型的特征子区域采用不同的刀具轨迹生成策略;建立各类型子区域的精确轨迹长度估算模型;选取合适的标准刀具组合并估算轨迹长度,拟合三次B样条曲线,求取曲线一阶导矢得到最小值,选择对应的标准刀具尺寸作为最优刀具尺寸。算法在VC++环境下进行了编程实现和算例验证,结果表明该方法是可行有效的。     

基于自动编码器组合的深度学习优化方法

为了提高自动编码器算法的学习精度,更进一步降低分类任务的分类错误率,提出一种组合稀疏自动编码器(SAE)和边缘降噪自动编码器(mDAE)从而形成稀疏边缘降噪自动编码器(SmDAE)的方法,将稀疏自动编码器和边缘降噪自动编码器的限制条件加载到一个自动编码器(AE)之上,使得这个自动编码器同时具有稀疏自动编码器的稀疏性约束条件和边缘降噪自动编码器的边缘降噪约束条件,提高自动编码器算法的学习能力。实验表明,稀疏边缘降噪自动编码器在多个分类任务上的学习精度都高于稀疏自动编码器和边缘降噪自动编码器的分类效果;与卷积神经网络(CNN)的对比实验也表明融入了边缘降噪限制条件,而且更加鲁棒的SmDAE模型的分类精度比CNN还要好。     

Expert systems for cancer chemotherapy

In the present paper we describe BREASTCAN and NEWCHEM, two expert systems for the characterization of optimal adjuvant cancer therapies. The purpose of BREASTCAN is to support physicians in the postoperative breast cancer therapy, on the basis of currently used therapy protocols. It was developed in Prolog and positively validated, referring to the chemotherapies used by oncologists for some patients in the National Cancer Institute in Milan. NEWCHEM is a system oriented to the development of new cancer therapies, based on pharmaco-cell kinetic modeling and the newest molecular knowledge about neoplastic process. The system is being built and at first it will be validated by experiments on mice. Our aim with NEWCHEM is to extend our knowledge base and our rules to incorporate also all the modt advanced knowledge at the molecular and cellular level, both theoretical and experimental, to make readily accessible to the health-community a system was the present state of the art allows.     

韦伯型多点设施优化选址的组合算法研究

韦伯型设施选址问题是组合优化领域中的一类重要问题,其核心内容是如何在离散的需求空间域内,寻找到最优决策关注点,即设施点。对于单点设施最优规划问题,由于不存在设置点之间的作用,仅考虑设施点与需求点之间的引力作用问题即可。对于多点设施的最优规划问题,不仅存在着设施点与需求点之间的引力作用问题,而且从资源优化配置的角度,还存在着设施点之间的斥力问题。因此,需要从系统整体优化的角度进行选择规划。目前解决韦伯型设施多点的优化选址问题,一般是通过寻找局部最优解的逐次递阶法来确定最优设施点。但由于该方法没有考虑到设施点间的斥力问题,容易导致设施点间的粘连。针对此问题,提出了一种PGSA-GA组合算法,通过建立模拟植物生长算法得到全局最优解的单点坐标,将其与需求点结合构建遗传算法优化的多目标规划多点设施选址模型求出Pareto最优解,并依此推广到多次选址方案。     

Analysis of periodic injections in cancer chemotherapy

A recently developed method for explicitly evaluating the time-averaged performance measure of an impulse-forced dynamical system is used to evaluate the effect of periodic injections as a cancer chemotherapy regimen. Explicit formulae are derived for two time-averaged performance measures, thus avoiding the need for tedious repeated numerical integrations. The method is shown to give more accurate predictions than previous periodic optimization methods in the case of one performance measure, and is successfully applied to the other performance measure for which previous optimization methods are inapplicable.     

Design optimization of resource combination for collaborative logistics network under uncertainty

Collaborative logistics networks (CLNs) are considered to be an effective organizational form for business cooperation that provides high stability and low cost. One common key issue regarding CLN resource combination is the network design optimization problem under discrete uncertainty (DU-CLNDOP). Operational environment changes and information uncertainty in network designs, due to partner selection, resource constrains and network robustness, must be effectively controlled from the system perspective. Therefore, a general two-stage quantitative framework that enables decision makers to select the optimal network design scheme for CLNs under uncertainty is proposed in this paper. Phase 1 calculates the simulation result of each hypothetical scenario of CLN resource combination using the expected value model with robust constraints. Phase 2 selects the optimal network design scheme for DU-CLNDOP using the orthogonal experiment design method. The validity of the model and method are verified via an illustrative example.     

An evolutionary approach to cancer chemotherapy scheduling

In this paper, we investigate the employment of evolutionary algorithms as a search mechanism in a decision support system for designing chemotherapy schedules. Chemotherapy involves using powerful anti-cancer drugs to help eliminate cancerous cells and cure the condition. It is given in cycles of treatment alternating with rest periods to allow the body to recover from toxic side-effects. The number and duration of these cycles would depend on many factors, and the oncologist would schedule a treatment for each patient’s condition. The design of a chemotherapy schedule can be formulated as an optimal control problem; using an underlying mathematical model of tumour growth (that considers interactions with the immune system and multiple applications of a cycle-phase-specific drug), the objective is to find effective drug schedules that help eradicate the tumour while maintaining the patient health’s above an acceptable level. A detailed study on the effects of different objective functions, in the quality and diversity of the solutions, was performed. A term that keeps at a minimum the tumour levels throughout the course of treatment was found to produce more regular treatments, at the expense of imposing a higher strain on the patient’s health, and reducing the diversity of the solutions. Moreover, when the number of cycles was incorporated in the problem encoding, and a parsimony pressure added to the objective function, shorter treatments were obtained than those initially found by trial and error.

板带材缺陷检测中的多特征优化组合方法研究

针对冷轧板带材常见表面缺陷图像识别的特点,提出了板带材表面缺陷多特征优化组合方法,该方法以直方图统计特征、小波变换特征、灰度共生矩阵特征、不变矩特征等4类特征共26维特征向量为基础,依据类间类内距离差的类别可分离性判据对特征进行优化,选出最优特征向量组合。对6类典型板带材表面缺陷进行实验,实验结果表明,采用多特征优化组合方法选择出的特征向量具有较好的分离效果,显著地提高了表面缺陷的识别率。     

基于输出协同调制的四端输出矩阵变换器开关组合优化方法

含补偿单元的四端输出三相-两相矩阵变换器(3-2MC)能有效提升带两相独立负载的矩阵变换器输入性能,在现有的调制方法下,四端输出3-2MC的电压增益较三端拓扑低,且该电压增益是与多个变量有关的表达式.针对上述缺陷,提出一种输出协同调制的开关组合优化方法,该方法在单位调制周期时间内对同极性输出侧和补偿侧进行协同调制,以提高有效矢量利用率.通过理论推导和分析可得,与现有调制方法相比,采用所提出方法能将四端输出3-2MC电压增益提高到常规三端输出3-2MC的水平,同时使得四端输出3-2MC的电压增益是一个只与输出不对称度有关的函数表达式.仿真结果表明,基于输出侧协同调制的开关组合优化方法能够实现输入与输出功率的解耦,有效拓宽四端输出3-2MC的线性调节区间,同时使系统具有良好的输入与输出性能.