共查询到20条相似文献,搜索用时 95 毫秒
1.
在现有文献研究的基础上,对传统实数遗传算法的进化策略又作了进一步研究,提出了一种改进的进化策略.进化策略克服了传统实数遗传算法中交叉得到的优秀个体有可能在变异过程中遭到破坏而不能生存的不足,并取消了交叉概率,使交叉产生的个体数增多,这样可增大产生更优秀个体的可能性,因而可使实数遗传算法的性能得到更好的改善.另外,给出了一种计算种群中个体适应度的计算公式和计算方法.该方法不但使得遗传算法具有较强的局部搜索能力,而且具有较强的广域搜索能力和较好的种群多样性,不易陷入局部最优解,从而可快速收敛到全局最优解.5个测试函数的计算结果表明,给出的实数遗传算法的改进进化策略比传统实数遗传算法进化策略的运算速度明显提高,迭代次数明显减少,从而验证了提出的实数遗传算法改进进化策略的有效性. 相似文献
2.
准确的预测黑龙江省农机总动力,可为黑龙江省的农业机械化发展趋势和农机产品市场分析提供理论指导,为制定黑龙江省农业机械化发展规划和预测近阶段农业机械化发展水平提供参考依据.利用黑龙江省1980-2007年农机总动力数据,运用标准BP神经网络和改进BP神经网络模型进行预测,预测结果表明,改进BP神经网络模型比标准BP神经网络模型在预测精度、运行时间、学习次数等方面更具优越性. 相似文献
3.
4.
基于遗传算法的同步优化算法 总被引:1,自引:0,他引:1
牛向阳 《应用数学与计算数学学报》2007,21(1):125-128
提出一种基于遗传算法的同步优化算法,该算法吸取了遗传算法和模拟退火算法的各自优点,将二进制编码和实数编码有机地结合起来,既能够快速收敛到全局最优解,又能够在优化神经网络结构的同时,得到较好的权值分布. 相似文献
5.
针对区域水资源利用的现状及特点,构建影响区域水资源可持续利用的主要评价指标体系.运用基于实数编码的加速遗传算法(RAGA)的投影寻踪分类(PPC)模型,把多维评价指标值转化为一维投影值,获得指标体系最佳投影方向和投影值,从而做出判断.以哈尔滨市为例,对哈尔滨市区域水资源可持续利用程度进行综合评价,将评价结果与多目标决策的灰色关联投影法的评价结果进行对比,验证了该模型在该方面研究的科学性和有效性. 相似文献
6.
油田注水系统拓扑布局优化的混合遗传算法 总被引:1,自引:0,他引:1
以投资最小为目标函数,建立了注水系统拓扑布局优化数学模型.根据模型特点,将优化问题分为两层,分别采用遗传算法和非线性优化方法进行求解.并对遗传算法的操作过程进行了改进,调整了适应函数,改进了交叉和变异操作,结合了模拟退火算法,在操作过程中使约束条件得到满足,减少了不可行解的产生,使遗传算法的优化性能得到了提高.优化算例说明了该方法的有效性. 相似文献
7.
8.
施工网络计划优化的极值种群遗传算法 总被引:3,自引:0,他引:3
针对普通遗传算法用于施工网络计划优化的缺点,通过种群划分与极值搜索,建立了网络计划优化的极值种群改进遗传算法模型,有效地避免了陷入局部极值点,应用证明,该算法与普通遗传算法相比,具有优化速度快、求解精度高,全局寻优能力强等优点,尤其适合于大型复杂工程网络的优化计算。 相似文献
9.
将概率因果模型的似然函数作为遗传算法的适应函数,从而将复杂系统的故障诊断转化为最优问题,建立了一种将概率因果模型和遗传算法相结合的故障诊断方法,并利用改进算法与概率因果故障诊断模型对船用核动力装置进行故障诊断实例分析,分析结果对船用核动力装置故障诊断具有重要的指导意义,而改进遗传算法是进行船用核动力装置故障诊断有效而实用的方法。 相似文献
10.
为了提高财务困境预测的正确率,改善模型预测的效果,将邻域粗糙集和遗传算法应用于对偶约束式最小二乘支持向量机,提出了一种基于邻域粗糙集属性约简的对偶约束式最小二乘支持向量机预测模型.同时,给出了这一改进模型的实现步骤.实证结果表明,通过邻域粗糙集指标预处理和遗传算法参数优化后,不但提高了模型预测的正确率,还降低了模型运行的时间,证实了该模型应用于财务困境预测是有效的. 相似文献
11.
The difficulty to solve multiple objective combinatorial optimization problems with traditional techniques has urged researchers to look for alternative, better performing approaches for them. Recently, several algorithms have been proposed which are based on the ant colony optimization metaheuristic. In this contribution, the existing algorithms of this kind are reviewed and a proposal of a taxonomy for them is presented. In addition, an empirical analysis is developed by analyzing their performance on several instances of the bi-criteria traveling salesman problem in comparison with two well-known multi-objective genetic algorithms. 相似文献
12.
Za'er S. Abo-Hammour Othman M.K. Alsmadi Adnan M. Al-Smadi Maha I. Zaqout Mohammad S. Saraireh 《Mathematical and Computer Modelling of Dynamical Systems: Methods, Tools and Applications in Engineering and Related Sciences》2013,19(2):201-221
A new method for simultaneously determining the order and the parameters of autoregressive moving average (ARMA) models is presented in this article. Given an ARMA (p, q) model in the absence of any information for the order, the correct order of the model (p, q) as well as the correct parameters will be simultaneously determined using genetic algorithms (GAs). These algorithms simply search the order and the parameter spaces to detect their correct values using the GA operators. The proposed method works on the principle of maximizing the GA fitness value relying on the deviation between the actual plant output, with or without an additive noise, and the estimated plant output. Simulation results show in detail the efficiency of the proposed approach. In addition to that, a practical model identification and parameter estimation is conducted in this article with results obtained as desired. The new method is compared with other well-known methods for ARMA model order and parameter estimation. 相似文献
13.
Genetic algorithms have attracted a good deal of interest in the heuristic search community. Yet there are several different
types of genetic algorithms with varying performance and search characteristics. In this article we look at three genetic
algorithms: an elitist simple genetic algorithm, the CHC algorithm and Genitor. One problem in comparing algorithms is that
most test problems in the genetic algorithm literature can be solved using simple local search methods. In this article, the
three algorithms are compared using new test problems that are not readily solved using simple local search methods. We then
compare a local search method to genetic algorithms for geometric matching and examine a hybrid algorithm that combines local
and genetic search. The geometric matching problem matches a model (e.g., a line drawing) to a subset of lines contained in
a field of line fragments. Local search is currently the best known method for solving general geometric matching problems. 相似文献
14.
《European Journal of Operational Research》1999,112(1):54-80
Production planning problems frequently involve the assignment of jobs or operations to machines. The simplest model of this problem is the well known assignment problem (AP). However, due to simplifying assumptions this model does not provide implementable solutions for many actual production planning problems. Extensions of the simple assignment model known as the generalized assignment problem (GAP) and the multi-resource generalized assignment problem (MRGAP) have been developed to overcome this difficulty. This paper presents an extension of the (MRGAP) to allow splitting individual batches across multiple machines, while considering the effect of setup times and setup costs. The extension is important for many actual production planning problems, including ones in the injection molding industry and in the metal cutting industry. We formulate models which are logical extensions of previous models which ignored batch splitting for the problem we address. We then give different formulations and suggest adaptations of a genetic algorithm (GA) and simulated annealing (SA). A systematic evaluation of these algorithms, as well as a Lagrangian relaxation (LR) approach, is presented. 相似文献
15.
Hoi-Ming Chi Okan K. Ersoy Herbert Moskowitz Jim Ward 《European Journal of Operational Research》2007
Using a supply chain network, we demonstrate the feasibility, viability, and robustness of applying machine learning and genetic algorithms to respectively model, understand, and optimize such data intensive environments. Deployment of these algorithms, which learn from and optimize data, can obviate the need to perform more complex, expensive, and time consuming design of experiments (DOE), which usually disrupt system operations. We apply and compare the behavior and performance of the proposed machine learning algorithms to that obtained via DOE in a simulated Vendor Managed Replenishment system, developed for an actual firm. The results show that the models resulting from the proposed algorithms had strong explanatory and predictive power, comparable to that of DOE. The optimal system settings and profit were also similar to that obtained from DOE. The virtues of using machine learning and evolutionary algorithms to model and optimize data rich environments thus seem promising because they are automatic, involving little human intervention and expertise. We believe and are exploring how they can be made adaptive to improve parameter estimates with increasing data, as well as seamlessly detecting system (and therefore model) changes, thus being capable of recursively updating and reoptimizing a modified or new model. 相似文献
16.
Multidimensional Optimization with a Fuzzy Genetic Algorithm 总被引:2,自引:0,他引:2
We present a new heuristic method to approximate the set of Pareto-optimal solutions in multicriteria optimization problems. We use genetic algorithms with an adaptive selection mechanism. The direction of the selection pressure is adapted to the actual state of the population and forces it to explore a broad range of so far undominated solutions. The adaptation is done by a fuzzy rule-based control of the selection procedure and the fitness function. As an application we present a timetable optimization problem where we used this method to derive cost-benefit curves for the investment into railway nets. These results show that our fuzzy adaptive approach avoids most of the empirical shortcomings of other multiobjective genetic algorithms. 相似文献
17.
Walter Krämer 《PAMM》2007,7(1):2140009-2140010
18.
19.
In this paper, we present a hybrid genetic algorithm for the well-known nurse scheduling problem (NSP). The NSP involves the
construction of roster schedules for nursing staff in order to maximize the quality of the roster schedule subject to various
hard constraints. In the literature, several genetic algorithms have been proposed to solve the NSP under various assumptions.
The contribution of this paper is twofold. First, we extensively compare the various crossover operators and test them on
a standard dataset in a solitary approach. Second, we propose several options to hybridize the various crossover operators. 相似文献
20.
This work considers a decision problem about orders of owners and routes of smallholdings for a harvester in an agricultural cooperative in which each owner has a proposal about the instant time in which he would like that the machine starts the activity in his land and the different smallholdings of each owner should be processed as a block. A binary linear programming model is introduced in order to reducing costs. Solving the model for actual size instances is computationally burdensome. Hence, we introduce and implement two heuristic algorithms to reduce the computational time. The heuristics are applied to the real case of the cooperative “Os Irmandiños” with a large number of owners and smallholdings. The numerical results show that the heuristics can solve large instances effectively with reasonable computational effort. 相似文献