基于神经网络-混合进化算法的桥式起重机的防摇摆控制

使用径向基函数(Radial Basis Function,RBF)神经网络为桥式起重机设计一种防摇摆控制器,并采用遗传算法(Genetic Algorithm,GA)与粒子群优化算法(Particle Swarm Optimization,PSO)相结合的混合进化算法(Hybrid Evolutionary Algorithm,HEA)作为神经网络的学习算法.RBF神经网络用于生成台车运动的光滑轨迹,而混合进化算法以台车遵循所生成轨迹到达目标位置时起重机系统的机械能为优化目标,对神经网络的参数进行优化调整,从而达到抑制负载残余摆动的目的.最后通过仿真验证了所提出的混合进化算法相对于遗传算法和粒子群优化算法的优越性以及所设计的防摇摆控制器的正确性和有效性.     

带服务优先级车辆路径问题的扫描禁忌搜索算法

提出了一种带服务优先级车辆路径问题的模型(Vehicle Routing Problem with Precedence Constraints,VRPPC),和一种扫描—禁忌搜索算法(sweep-Taboo Search Algorithm,S-TSA).然后,运用S-TSA对郑煤物资供销有限公司的带有服务优先级的危险物资配送进行优化求解,并与扫描遗传算法(sweep-Genetic Algorithm,SGA),禁忌搜索算法(Taboo Search Algorithm,TSA),人工鱼群算法(Artificial Fish Algorithm,AFA)进行比较研究,研究结果显示:扫描禁忌搜索算法能在满足服务优先级的前提下,使配送费用最少.     

基于遗传算法和模拟退火的插值马氏模型用于真核生物启动子识别

启动子识别是生物信息学领域极具挑战的问题.本文在IMC(Interpolated Markov Chin)的框架下考虑碱基的插入与缺失,采用SA(Simulated Annealing)训练转移概率,以增加模型的鲁棒性,利用GA(Genetic Algorithm)优化IMC插值系数,以克服梯度算法易于陷入局部极值点的缺陷,最后将该模型用于启动子识别,识别率在测试集达到86%.     

基于NSGA-Ⅱ的应急储备库多目标选址决策模型及算法研究

应急物资储备库选址问题是在近年世界灾害多发的现实背景下产生的,根据具体选址问题特点建立了多目标选址决策模型。该模型综合考虑了两种灾害风险下储备库的成本费用、覆盖效率以及对重点地区的备用覆盖,以使模型更加符合实际目标及约束情况。算法设计上,首次采用带精英策略的非支配排序遗传算法(Fast and elitist Non-dominated Sorting Genetic AlgorithmⅡ,NSGA-Ⅱ)解决储备库多目标选址问题,得到了Pareto非劣解分布并同不带精英策略的常规NSGA算法下的仿真结果进行对比分析。验证了模型的可行性以及NSGA-Ⅱ在解决储备库多目标选址问题的有效性。     

基于NSGA-Ⅱ的应急储备库多目标

应急物资储备库选址问题是在近年世界灾害多发的现实背景下产生的,根据具体选址问题特点建立了多目标选址决策模型。该模型综合考虑了两种灾害风险下储备库的成本费用、覆盖效率以及对重点地区的备用覆盖,以使模型更加符合实际目标及约束情况。算法设计上,首次采用带精英策略的非支配排序遗传算法(Fast and elitist Non-dominated Sorting Genetic Algorithm Ⅱ,NSGA-Ⅱ)解决储备库多目标选址问题,得到了Pareto非劣解分布并同不带精英策略的常规NSGA算法下的仿真结果进行对比分析。验证了模型的可行性以及NSGA-Ⅱ在解决储备库多目标选址问题的有效性。     

最经济智能控制系统研究开发及应用

在最经济控制、低成本自动化和智能控制已有成果的基础上,提出并研究了最经济智能控制系统(Most,Economical Intelligent Control System—MEICS)的概念、设计方法和实现技术.探讨了MEICS的体系结构,分析了其经济性;提出了广义自适应遗传算法(Generalized Self-adaptive Genetic Algorithm—GSAGA),即首先产生均匀分布的初始种群,保留种群中的优秀个体直接进入下一代,再根据种群模式的状况决定是否引入"高品质移民",最后自适应地进行交换和变异运算;提出了基于网络的控制系统的信息结构能通性概念,建立了信息结构模型和信息结构能通性判据;分析了信息结构的可靠性和经济性,提出了评价可靠性的指标;采用GSAGA分别对MEICS的系统逻辑信息通道结构和控制系统参数进行优化设计,实现了MEICS的优化设计;介绍了MEICS的应用例子.     

基于LSTM-GA混合模型的患者预约排队策略优化

在医疗运作管理领域,合理的资源分配能够帮助更多的患者尽早就医,降低患者病情恶化和死亡的风险。本文设计了预约排队策略对患者占有资源的顺序进行分配,建立了基于长短时记忆(Long Short Term-Memory, LSTM)神经网络和遗传算法(Genetic Algorithm, GA)的混合模型以优化排队策略。首先利用大数据和深度学习分析患者到达和医院服务情况,建立LSTM神经网络学习数据特征并预测未来数据,相比于排队论常用的随机分布方法取得了更好的效果.其次设计了基于排队系统仿真的排队策略优化算法,利用改进GA得到最优排队策略。实证研究表明,文本的方法可以明显降低患者的等待时间,最高可达59%。最后对排队策略进行敏感性分析,结果表明排队策略有效作用于仿真的各个时段。     

GA优化T-S云推理网络火灾识别模型设计

针对传统火灾报警系统存在着准确度不高、误报、漏报及泛化能力不强的问题,设计了一种基于GA(Genetic Algorithm)优化T-S云推理网络火灾探测模型,对模型进行训练和测试。并将T-S云推理网络与模糊神经网络对火灾信号的识别结果进行对比,给出MATLAB的仿真结果。通过仿真结果表明,该火灾探测模型识别精度更高,偏离样本数据测试结果与期望输出之间误差更小,模型提高了对火灾信号的识别精度和泛化能力。     

求解旅行商问题的搜寻者遗传算法

针对简单遗传算法易陷入局部最优及收敛速度慢的不足,提出一种改进遗传算法-基于启发式策略的搜寻者遗传算法.首先将搜寻者优化算法中的模糊思想和近邻策略相结合改进变异算子,增强种群多样性,避免陷入局部最优;然后针对路径优化问题基于启发式策略设计反转算子,使得路径中不存在交叉边,加快收敛速度;最后将改进遗传算法用于求解旅行商问题.结果表明,改进遗传算法的求解精度和求解效率明显优于基本遗传算法.     

随机化均匀设计遗传算法

众所周知,遗传算法的运行机理及特点是具有定向制导的随机搜索技术,其定向制导的原则是:导向以高适应度模式为祖先的"家族"方向.以此结论为基础.利用随机化均匀设计的理论和方法,对遗传算法中的交叉操作进行了重新设计,给出了一个新的GA算法,称之为随机化均匀设计遗传算法.最后将随机化均匀设计遗传算法应用于求解函数优化问题,并与简单遗传算法和佳点集遗传算法进行比较.通过模拟比较,可以看出新的算法不但提高了算法的速度和精度,而且避免了其它方法常有的早期收敛现象,     

基于模糊遗传算法的自融资有效投资组合研究

在马克维茨投资组合的均值一方差模型框架下,给出限制投资数量的自融资投资组合优化模型.把预期收益率不等式约束转化为模糊约束,采用一种通过惩罚因子,对适应度函数进行修正的模糊遗传算法来求解模型.在理论上,这种算法能够将最优基因较完整地遗传到下一代,有效地避免了早熟现象,可以得到更好的适应度函数值.在实际应用中,对一具体自融资有效投资组合实例进行计算,结果表明:本文所提出的模糊遗传算法是可行的、有效的,具有更好的优化结果.     

A hybrid genetic algorithm for the resource-constrained project scheduling problem

In this paper we propose a Hybrid Genetic Algorithm (HGA) for the Resource-Constrained Project Scheduling Problem (RCPSP). HGA introduces several changes in the GA paradigm: a crossover operator specific for the RCPSP; a local improvement operator that is applied to all generated schedules; a new way to select the parents to be combined; and a two-phase strategy by which the second phase re-starts the evolution from a neighbour’s population of the best schedule found in the first phase. The computational results show that HGA is a fast and high quality algorithm that outperforms all state-of-the-art algorithms for the RCPSP known by the authors of this paper for the instance sets j60 and j120. And that it is competitive with other state-of-the-art heuristics for the instance set j30.     

采用二叉树编码的遗传算法实现数据拟合

针对采用数值分析方法进行数据拟合求解复杂度高、运算最大而精度较低的缺陷 ,本文给出一种基于二叉树编码的遗传算法来进行数据拟合 ,取得了较好的效果     

基于遗传算法重建多个散射体的组合Newton法

本文研究由单个入射声波或电磁波及其远场数据反演多个柔性散射体边界的逆散射问题.通过建立边界到边界总场的非线性算子及其n6chet导数,本文首先给出了基于单层位势的组合Newton法.将组合Newton法转化为泛响优化问题,从而获得了该方法重建单个散射体的收敛性分析.然后,基于遗传算法和正则化参数选取的模型函数方法,给出...     

A Hybrid Genetic Algorithm for the Single Machine Scheduling Problem

A hybrid genetic algorithm (HGA) is proposed for the single machine, single stage, scheduling problem in a sequence dependent setup time environment within a fixed planning horizon (SSSDP). It incorporates the elitist ranking method, genetic operators, and a hill-climbing technique in each searching area. To improve the performance and efficiency, hill climbing is performed by uniting the Wagner-Whitin Algorithm with the problem-specific knowledge. The objective of the HGA is to minimize the sum of setup cost, inventory cost, and backlog cost. The HGA is able to obtain a superior solution, if it is not optimal, in a reasonable time. The computational results of this algorithm on real life SSSDP problems are promising. In our test cases, the HGA performed up to 50% better than the Just-In-Time heuristics and 30% better than the complete batching heuristics.     

A Combined Genetic-neural Algorithm for Mobility Management

This work presented a new approach to solve the location management problem by using the location areas approach. A combination of Genetic Algorithm and Hopfield Neural Network is used to find the optimal configuration of location areas in a mobile network. Toward this end, the location areas configuration of the network is modeled so that the general condition of all the chromosomes of each population improves rapidly by the help of a Hopfield Neural Network. The Hopfield Neural Network is included in the Genetic Algorithm optimization process, to expedite its convergence, since the generic Genetic Algorithm is not fast enough. Simulation results are very promising and they lead to network configurations that are unexpected.      

SASEGASA: A New Generic Parallel Evolutionary Algorithm for Achieving Highest Quality Results

This paper presents a new generic Evolutionary Algorithm (EA) for retarding the unwanted effects of premature convergence. This is accomplished by a combination of interacting generic methods. These generalizations of a Genetic Algorithm (GA) are inspired by population genetics and take advantage of the interactions between genetic drift and migration. In this regard a new selection scheme is introduced, which is designed to directedly control genetic drift within the population by advantageous self-adaptive selection pressure steering. Additionally this new selection model enables a quite intuitive heuristics to detect premature convergence. Based upon this newly postulated basic principle the new selection mechanism is combined with the already proposed Segregative Genetic Algorithm (SEGA), an advanced Genetic Algorithm (GA) that introduces parallelism mainly to improve global solution quality. As a whole, a new generic evolutionary algorithm (SASEGASA) is introduced. The performance of the algorithm is evaluated on a set of characteristic benchmark problems. Computational results show that the new method is capable of producing highest quality solutions without any problem-specific additions.     

遗传信赖域方法

本文将具有并行计算性能的遗传算法与具有全局收敛的信赖域方法相结合以形成混合搜索方法,为解决复杂多峰极值优化问题提供一种有效算法,证明了算法的收敛性。     

Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates

This paper studies the scheduling problem in hybrid flow shop (HFS) environment. The sequence dependent family setup time (SDFST) is concerned with minimization of makespan and total tardiness. Production environments in real world include innumerable cases of uncertainty and stochasticity of events and a suitable scheduling model should consider them. Hence, in this paper, due date is assumed to be uncertain and its data follow a normal distribution. Since the proposed problem is NP-hard, two metaheuristic algorithms are presented based on genetic algorithm, namely: Non-dominated Sorting Genetic Algorithm (NSGAII) and Multi Objective Genetic Algorithm (MOGA). The quantitative and qualitative results of these two algorithms have been compared in different dimensions with multi phase genetic algorithm (MPGA) used in literature review. Experimental results indicate that the NSGAII performs very well when compared against MOGA and MPGA in a considerably shorter time.     

蚂蚁算法和小生境遗传算法的融合

蚂蚁算法是一种新型的模拟进化算法,也是一种随机型智能搜索算法.较为系统的总结了算法的基本理论,分析了其基本算法解决TSP问题的模型,针对蚂蚁算法易出现停滞的缺点,把小生境遗传算法和蚂蚁算法融合,仿真比较实验结果表明优于基本蚂蚁算法.