用混合遗传算法求解虚拟企业生产计划

针对虚拟企业生产计划的特点,以各成员企业承担的生产任务为对象,以快速响应市场为目标,建立了生产任务计划的数学模型,并基于该模型,提出一种基于遗传算法与模拟退火算法混合的求解算法,充分发挥了遗传算法良好的全局搜索能力和模拟退火算法有效避免陷入局部极小的优点．从而提高了算法的全局寻优能力．数值仿真计算表明了该算法的良好收敛性和有效性．     

求解TSP的混合遗传算法

介绍一种求解TSP的混合遗传算法,该算法结合了基于邻域的LK算法和采用Inver-Over算子的遗传算法,并在算法中增加一些控制策略,加快算法的收敛速度,又保证群体的多样性。实验表明该算法是有效的。     

改善收敛早熟的混合遗传算法

针对传统遗传算法收敛早熟问题,在传统包含模拟退火的混合遗传算法的基础上,设计加入了“包含浓度均衡措施的复制算法”,通过调整轮盘赌扇区面积,防止个体适应度的两极分化.从而避免了算法过早收敛于局部最优解;同时通过一个工程实例计算验证了算法的可行性.     

求解多组分化学相平衡的一种通用算法

基于Gibbs自由能最小原理,提出一种求解多组分化学相平衡的通用算法——混沌-蒙特卡罗混合算法。分别介绍了该算法的计算步骤和关键性能参数。多个计算实例表明,本文提出的方法可以不断拓展解的空间,避免假收敛,能以完全概率、高速并行搜索到全局最优解。     

一种基于混合遗传和粒子群的智能优化算法

粒子群算法(particle swarm optimization, PSO)原理简单、搜索速度快,但前期容易“早熟”.遗传算法(genetic algorithm, GA)具有很强的全局搜索能力,但收敛精度不高.综合考虑二者优缺点,把遗传算子引入PSO算法中,并采用交叉搜索的方法,调整惯性权重以及变异方式使粒子得到进化,当粒子种群进化到一定层度后,对部分粒子进行变异处理,这样不仅避免算法陷入局部最优解,而且获得较高收敛精度和执行能力,可解决工程中非线性、多极值的问题.据测试函数以及与其他寻优算法的对比分析表明,此混合策略在求解精度、搜索效率和处理不同复杂度问题等方面都有很好的优越性,具有满足工程需要的能力.     

电磁综合问题的混合遗传算法求解

综合问题是电磁领域中经常遇到的问题,本文给出了一种混合遗传算法,可以很好的处理综合问题.该方法采用带有最优个体保存策略的遗传算法,并结合柔性约束技术和自适应搜索技术.柔性约束可以极大地压缩搜索空间,自适应搜索可以使搜索区的跟踪位移始终保持最优状态.通过线天线阵和滤波器的综合问题,展现了该方法的效能,进而证实该方法是实现多自变量综合问题的强有力手段.     

求解JSP问题的改进型混合遗传算法

在蜂群进化遗传算法框架的基础上,提出一种求解JobShop问题的新的混合遗传算法。本算法设计保证了算法的收敛性,改善了算法的局部搜索能力,延缓或避免了早熟收敛的发生。OR Library经典算例的仿真结果,证明了算法的有效性。     

求解多背包问题的混合遗传算法

针对多背包问题最优解的求解,设计了一种新的价值密度;在此基础上结合传统的贪心算法,提出了一种求解多背包问题的混合遗传算法。该算法采用整数编码,并采用轮盘赌选择方法,对背包资源利用不足的可行解进行修正处理,对不可行解进行修复处理。并在大量的数值实验的基础上,将该方法与传统方法及简单遗传算法进行比较,实验结果表明,该混合遗传算法提高了问题求解的速度和精度,有一定的优越性。     

求解TSP问题的混合遗传算法

TSP问题是一类经典的NP问题,目前有很多方法对其求解,而用混合遗传算法对其求解取得了很好的成效。常见的混合遗传算法有遗传算法与最速下降法相结合（GACSDM）、遗传算法与模拟退火法相结合（SAGA）。设计了贪婪的复合变异算子（GCM）,并引入隔代爬山法算子（Climb）增加遗传算法的局部搜索能力。实验结果表明该算法是有效的。     

基于DPLL的混合遗传算法求解SAT问题

基于"聚类排序选择"优化遗传算法求解SAT问题时,引入交叉算子和变异算子,并根据适应度函数及问题本身特性,调节阈值δ,生成新的种群聚类。这种遗传算法有效地抑制了算法的延迟收敛,从而保证了为可满足性公式能够快速找到一个可满足性指派。同时,在遗传算法中引入了DPLL算法,对部分变元进行消解,提高了算法的求解效率。相关的实验数据表明,本算法的性能明显优于同类算法。     

易挥发弱电解质体系化学及相平衡计算的遗传算法

基于Gibbs自由能最小化原理,提出用遗传算法计算易挥发弱电解质体系化学及相平衡问题。首先建立了易挥发弱电解质体系Gibbs自由能的计算模型,将含化学反应平衡和相平衡计算问题转化为有约束的最优化问题．并应用遗传算法求解;其次在算法实施时提出两点改进,即通过对优化变量采取动态边界的可行域编码方法来处理问题约束和引入反应平衡常数来提高低含量组分的计算精度;最后对两个算例进行了计算。计算表明,本文计算结果与文献值相吻合,并且简单易实施,是这类问题计算的有效方法。     

Solving a timetabling problem using hybrid genetic algorithms

All over the world, human resources are used on all kinds of different scheduling problems, many of which are time-consuming and tedious. Scheduling tools are thus very welcome. This paper presents a research project, where Genetic Algorithms (GAs) are used as the basis for solving a timetabling problem concerning medical doctors attached to an emergency service. All the doctors express personal preferences, thereby making the scheduling rather difficult. In its natural form, the timetabling problem for the emergency service is stated as a number of constraints to be fulfilled. For this reason, it was decided to compare the strength of a Co-evolutionary Constraint Satisfaction (CCS) technique with that of two other GA approaches. Distributed GAs and a simple special-purpose hill climber were introduced, to improve the performance of the three algorithms. Finally, the performance of the GAs was compared with that of some standard, nonGA approaches. The distributed hybrid GAs were by far the most successful, and one of these hybrid algorithms is currently used for solving the timetabling problem at the emergency service. © 1997 John Wiley & Sons, Ltd.     

A classification method using a hybrid genetic algorithm combined with an adaptive procedure for the pool of ellipsoids

This paper presents a hybrid classification method that utilizes genetic algorithms (GAs) and adaptive operations of ellipsoidal regions for multidimensional pattern classification problems with continuous features. The classification method fits a finite number of the ellipsoidal regions to data pattern by using hybrid GAs, the combination of local improvement procedures and GAs. The local improvement method adaptively expands, rotates, shrinks, and/or moves the ellipsoids while each ellipsoid is separately handled with a fitness value assigned during the GA operations. A set of significant features for the ellipsoids are automatically determined in the hybrid GA procedure by introducing “don’t care” bits to encode the chromosomes. The performance of the method is evaluated on well-known data sets and a real field classification problem originated from a deflection yoke production line. The evaluation results show that the proposed method can exert superior performance to other classification methods such as k nearest neighbor, decision trees, or neural networks. Ki K. Lee received the B.S. degree from Han Yang University, Seoul, Korea in 1994, and the M.S. and Ph.D. degrees in industrial engineering from Korea Advanced Institute Science and Technology (KAIST), Daejeon, Korea in 1996 and 2005, respectively. From 2001 to 2004, he was a senior research engineer in telecommunication systems laboratory of LG Electronics Inc. Since 2005, he has been an assistant professor in the School of Management at Inje University, Kimhae, Korea. His research interests include intelligent decision support systems, soft computing, and pattern recognition. Wan C. Yoon received the B.S. degree from Seoul National University, Korea in 1977, the M.S. degree from KAIST, Korea in 1979, and the Ph.D. degree in industrial and systems engineering from Georgia Institute of Technology in 1987. He is professor of the Department of Industrial Engineering at KAIST, Korea. His research interests include application of artificial intelligence, human decision-making and aiding, information systems, and joint intelligent systems. Dong H. Baek received the B.S. degree from Han Yang University, Seoul, Korea in 1992, and the M.S. and Ph.D. degrees in industrial engineering from Korea Advanced Institute Science and Technology (KAIST), Daejeon, Korea in 1994 and 1999, respectively. He is an assistant professor in management information systems at department of business administration, Hanyang University, Korea. His research interests include management information systems, system engineering, and machine learning.     

A hybrid genetic algorithm for the phylogeny problem using path-relinking as a progressive crossover strategy

A phylogenetic tree relates taxonomic units using their similarities over a set of characteristics. Given a set of taxonomic units and their characteristics, the phylogeny problem under the parsimony criterion consists in finding a phylogenetic tree with a minimum number of evolutionary steps. We developed a hybrid genetic algorithm for the problem of building a phylogenetic tree minimizing parsimony. The algorithm combines local search with a crossover strategy based on path-relinking, an intensification technique originally used in the context of other metaheuristics such as scatter search and GRASP. Computational experiments on benchmark and randomly generated instances show that the proposed algorithm is very robust and outperforms other heuristics in terms of solution quality and running times.     

相平衡和平衡级分离专用计算软件VB设计

本软件系统包括4个子系统：（1）平衡数据关联子系统－含Van Laar,Margules,NRTL,UNIQUAC和Wilson模型以及Antione常数回归方程;（2）精馏子系统－含间歇精馏,普通精馏和萃取精馏;（3）液液萃取子系统－包括无水和含水溶剂体系;（4）数据库子系统－建成bpd.mdb数据文件,具有查询和管理各种烷烃,芳烃和溶剂等基础物性的功能。软件系统采用VB设计,界面友好,操作简便,可用于汽液和液液平衡数据模型参数的关联以及多元多级汽液和液液平衡级的分离计算。     

含化学反应体系多相平衡计算方法的研究进展

首先给出了含化学反应体系多相平衡计算问题的数学方程,然后对国内外含化学反应体系多相平衡计算方法的现状和进展做了综述,包括经典的非化学计量系数法和化学计量系数法以及最近提出的稳定性分析法、全局最优法和遗传算法等,并具体介绍了化学计量系数法中两种常用算法-S-C算法和KZ算法以及遗传算法的算法原理和计算步骤。     

一种快速收敛的混合遗传算法

利用遗传算法早熟的特点 ,构造出一种快速收敛的混合算法来求解优化问题 ,并分析了它的收敛性。它是使用遗传算法来生成搜索方向 ,从而保证了算法的收敛性。该算法利用遗传算法的全局搜索能力 ,并采用 Nelder- Mead单纯形法来加强算法的局部搜索能力 ,加快了算法的收敛速率。模拟实验表明 ,该方法具有高效性和鲁棒性     

A vehicle routing problem solved by using a hybrid genetic algorithm

The main purpose of this study is to find out the best solution of the vehicle routing problem simultaneously considering heterogeneous vehicles, double trips, and multiple depots by using a hybrid genetic algorithm. This study suggested a mathematical programming model with a new numerical formula which presents the amount of delivery and sub-tour elimination. This model gives an optimal solution by using OPL-STUDIO(ILOG CPLEX). This study also suggests a hybrid genetic algorithm (HGA) which considers the improvement of generation for an initial solution, three different heuristic processes, and a float mutation rate for escaping from the local solution in order to find the best solution. The suggested HGA is also compared with the results of a general genetic algorithm and existing problems suggested by Eilon and Fisher. We found better solutions rather than the existing genetic algorithms.     

基于混合递阶遗传算法的径向基神经网络学习算法及其应用

在研究径向基神经网络学习算法的基础上, 提出了一种新型的径向基神经网络学习算法———混合递阶遗传算法. 该算法将递阶遗传算法和最小二乘法的优点结合在一起, 能够同时确定径向基神经网络的结构和参数, 并具有较高的学习效率. 采用基于混合递阶遗传算法的径向基神经网络对混沌时间序列学习和预测, 取得了较好的效果.