遗传算法的改进策略及其在桥梁抗震优化设计中的应用效果

本文论述了采用遗传算法进行结构优化设计时遇到的诸如计算量大、早熟收敛和边界探索不足等棘手问题，提出了三个解决对策，编制了计算程序，其在桥梁抗震代化设计中的应用效果表明，改进后的遗传算法不但提高了计算速度，而且在尽可能短的时间内找到最好的优化解。     

Entwicklung eines hybriden Genetischen Algorithmus zur Tourenplanung

Subject of this paper is the development of a Genetic Algorithm (GA) for the Vehicle Routing Problem (VRP). At first a simple GA for the one-depot “standard” VRP with travel distance and capacity restrictions is presented. Then the GA is enhanced by hybridization. The performance of the enhanced GA is evaluated. Finally, the GA is modified to handle a cost function and some real world restrictions.     

模糊关系方程的解的模糊遗传寻优算法

在一般遗传算法ＧＡ的基础上，基于模糊集理论中的模糊关系方程的解的寻优问题提出了模糊遗传算法ＦＧＡ，它能有效地找出模糊关系方程的解的寻优问题的近似最优解。还给出了一个重要的定理：模糊模式定理。     

改进遗传单纯形混合算法在光纤对接中的应用

本文首先给出了纤端光场分布的3D模型并针对单模光纤与激光器的对接分析了错位、角度偏差等对接效率影响因素。通过与传统的爬山法进行比较,提出将遗传单纯形混合算法应用到光纤对接中。单纯形法是一种局部搜索方法,这种寻优方法收敛速度快,它与遗传算法组合起来可以改善单独使用遗传算法收敛速度慢的缺陷。同时,针对光纤对接要求初始种群小所造成的个体多样性差的缺点,改进了单纯形的反射、压缩系数,有效地避免了遗传操作的过早收敛,并通过仿真结果证明了混合算法可以在10代之内找到光纤对接的最佳耦合点。     

遗传算法与惩罚函数法在机械优化设计中的应用

提出了应用于机械优化设计的"遗传算法+惩罚函数法"的通用算法.它非常适合求解复杂的非线性约束优化问题.本通用算法既克服了传统优化方法的缺点,得到了一个较为理想的全域最优解;同时也改善了遗传算法的局限性.     

基于遗传算法的订单拣货实时排序问题研究

根据配送中心订单拣货问题的特点,以订单客户的满意度为目标,建立了数学模型,并设计了相应的遗传算法来求解该问题。在算例中,将遗传算法与先到先服务和最早工期优先规则进行了比较,结果表明,遗传算法可以明显优化目标函数值,有助于提高配送中心的拣货效率,提高客户满意度,具有良好的实用性。     

一种封闭空间自适应有源噪声控制系统优化方法

将遗传算法（GA）引人封闭空间自适应有原噪声控制（AANC）系统优化设计,以解决长期以来缺乏有效的确定封闭空间次级声源和误差传感器最优位置方法的问题。以小阻尼矩形空间为例,进行了计算机仿真。结果证明：此方法对封闭空间AANC系统优化是有效的。     

基于混合算法的单堆垛机多巷道拣选作业调度研究

针对单堆垛机在多巷道自动化仓库中的拣选路径规划问题,建立了求解含周转箱约束的堆垛机拣选作业最短路径数学模型,提出用遗传模拟退火混合算法进行求解。通过Matlab分别对不同算法进行实例仿真,结果表明：该混合算法克服了遗传算法早熟,以及模拟退火算法收敛性慢的缺点,求出的解更优,收敛速度更快,稳定性更好。该混合算法具有可行性和高效性。     

Optimization of the inspection intervals of a safety system in a nuclear power plant by Multi-Objective Differential Evolution (MODE)

In this paper, we consider the problem of the optimization of the inspection intervals of the High Pressure Injection System (HPIS) of a Pressurized Water Reactor (PWR). For its solution, we investigate the use of Differential Evolution (DE) and compare it to another popular Evolutionary Algorithm (EA), the Genetic Algorithm (GA). In the comparison, we look in particular at the computation time and at the characteristics of the Pareto frontier. The problem is first treated as a single-objective optimization (SO) and then as a multi-objective optimization (MO). For this latter, a Multi-Objective Differential Evolution (MODE) code has been purposely developed, in Matlab.     

Control point policy optimization using genetic algorithms

This paper describes the application of an integrated Genetic Algorithm (GA)/Discrete Event Simulation model for selecting optimum values for Critical Point Policy (CPP) hedging time and buffer size parameters. The CPP is shown to perform well, when compared with the Critical Ratio priority rule, in terms of improving service levels, particularly when subject to conditions where buffer sizes and Takt times are required to be small. The technique developed involves buffer sizes being chosen by a GA according to a constraint on the total storage space available within the system. A method is described for reducing the number of variables that the GA needs to deal with, hence, improving the efficiency of the GA optimization process. The development and application work reported also provides further understanding into how and when the CPP should be applied.     

IMPROVEMENTS OF SIMPLE GENETIC ALGORITHM IN STRUCTURAL DESIGN

The efficiency of Simple Genetic Algorithm (SGA) can be improved by some strategies. They are elitest strategy, multi-point crossover, identification of passive design variables, gradual increase of penalty parameter, and bit-wise local search. Topology optimization using GA is also discussed in this paper and examples are given. Five numerical examples show the efficiency and the optimum solutions of GA are greatly improved by these strategies. © 1997 John Wiley & sons, Ltd.     

A high-resolution neutron spectra unfolding method using the Genetic Algorithm technique

The Bonner sphere spectrometers (BSS) are commonly used to determine the neutron spectra within various nuclear facilities. Sophisticated mathematical tools are used to unfold the neutron energy distribution from the output data of the BSS. This paper highlights a novel high-resolution neutron spectra-unfolding method using the Genetic Algorithm (GA) technique. The GA imitates the biological evolution process prevailing in the nature to solve complex optimisation problems. The GA method was utilised to evaluate the neutron energy distribution, average energy, fluence and equivalent dose rates at important work places of a DIDO class research reactor and a high-energy superconducting heavy ion cyclotron. The spectrometer was calibrated with a ²⁴¹Am/Be (α,n) neutron standard source. The results of the GA method agreed satisfactorily with the results obtained by using the well-known BUNKI neutron spectra unfolding code.     

副热带高压与东亚季风指数的非线性数学模型的遗传算法参数优化

针对东亚夏季风环流演变与副热带高压活动极为复杂,动力模型难以准确建立的情况,提出用遗传算法从实际观测资料中反演重构副高指数与夏季风环流因子动力模型的方法,反演重构了东亚夏季风环流因子与副高形态指数的动力预报模型并进行了模型的预报试验。结果表明,遗传算法全局搜索和并行计算优势可客观准确和方便快捷地反演重构东亚夏季风环流因子与副高指数的动力模型,所建模型能对副高指数和夏季风环流的演变进行较为准确的预测,进而为东亚夏季风环流与副高等复杂天气指数的动力模型建立和预测提供了新的方法途径。     

基于遗传算法策略的含时滞结构振动主动控制研究

该文提出一种新的含时滞结构振动控制方法-基于遗传算法优化反馈控制增益的控制方法,该方法以时滞微分动力学方程为依据,以遗传算法为优化工具,然后根据特定的优化控制目标实现控制增益的求解.遗传算法优化控制增益的方法具有以下优点:其一,由于优化所得的控制增益充分考虑了时滞对结构振动控制的影响,因而能够有效地解决由于时滞的存在使...     

Extending economic design of p charts to handle user specified constraints using a genetic algorithm

For certain processes, the quality of the output can be monitored using attribute inspection statistical process control. Various approaches have been used to determine optimal or near-optimal parameters for such a plan. Since most of these approaches use an unconstrained model, some solutions can result in theoretical and pragmatic problems. In this paper we used Duncan's loss function as the objective function for the development of a Genetic Algorithm (GA). In addition to formulating a GA to find a solution to the model, user constraints concerning the frequency of inspection, the number of defects allowed and the production rate are used to limit the search space for the GA. The GA is selected over other search techniques such as traditional calculus-based, enumeration, or undirected random search methods because the GA is more robust than other search techniques.     

基于遗传算法的指向性声源自适应有源消声系统优化

详细讨论了自由声场自适应有源噪声控制（ＡＡＮＣ）中，初级源为无限大刚性障板上的圆面活塞辐射器，采用一个和二个点源做次级声源时，使ＡＡＮＣ系统获得最大降噪效果的次级声源和误差传感器（以下统称为执行元件）的布放问题，并利用遗传算法（ＧＡ）确定次级源和远场误差传感器的最优位置。通过理论分析和计算机仿真结果的比较，证明了采用遗传算法可以准确地得到传统的梯度搜索优化算法难以得到的位置优化解。     

基于遗传算法的神经网络被动声呐目标分类研究

被动声呐目标识别系统中目标分类器的设计和训练是一项重要内容，本文设计了目标分类器的神经网络结构，提出了一种用改进的遗传算法训练神经网络分类器的新方法，最后，对海上实录的Ａ，Ｂ，Ｃ三类目标噪声进行了分类识别，实验结果表明基于遗传算法的神经网络分类器比传统的基于ＢＰ算法的神经网络分类源泛化性能有明显提高。     

Forecasting E-Commerce Adoption Based on Bidirectional Recurrent Neural Networks

E-commerce refers to a system that allows individuals to purchase and sell things online. The primary goal of e-commerce is to offer customers the convenience of not going to a physical store to make a purchase. They will purchase the item online and have it delivered to their home within a few days. The goal of this research was to develop machine learning algorithms that might predict e-commerce platform sales. A case study has been designed in this paper based on a proposed continuous Stochastic Fractal Search (SFS) based on a Guided Whale Optimization Algorithm (WOA) to optimize the parameter weights of the Bidirectional Recurrent Neural Networks (BRNN). Furthermore, a time series dataset is tested in the experiments of e-commerce demand forecasting. Finally, the results were compared to many versions of the state-of-the-art optimization techniques such as the Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Genetic Algorithm (GA). A statistical analysis has proven that the proposed algorithm can work significantly better by statistical analysis test at the P-value less than 0.05 with a one-way analysis of variance (ANOVA) test applied to confirm the performance of the proposed ensemble model. The proposed Algorithm achieved a root mean square error of RMSE (0.0000359), Mean (0.00003593) and Standard Deviation (0.000002162).     

A new spanning tree-based genetic algorithm for the design of multi-stage supply chain networks with nonlinear transportation costs

The design of configuration and the transportation planning are crucial issues to the effectiveness of multi-stage supply chain networks. The decision makers are interested in the determination the optimal locations of the hubs and the optimal transportation routes to minimize the total costs incurred in the whole system. One may formulate this problem as a 0-1 mixed integer non-linear program though commercial packages are not able to efficiently solve this problem due to its complexity. This study proposes a new spanning tree-based Genetic Algorithm (GA) using determinant encoding for solving this problem. Also, we employ an efficient heuristic that fixes illegal spanning trees existing in the chromosomes obtained from the evolutionary process of the proposed GA. Our numerical experiments demonstrate that the proposed GA outperforms the other previously published GA in the solution quality and convergence rate.     

一类资源负荷均衡问题的优化调度算法

针对一类n个独立任务在m个不完全同等的处理机上处理，使处理机的最大负荷为最小的非抢先调度问题，提出了一种启发式算法－－最小平衡算法，并分析了它的时间复杂性，在此基础上，又将最小平衡算法和遗传算法结合起来，提出了基于遗传的最小平衡算法，并用实例证实了该算法的有效性。