工程项目资源均衡优化的混合遗传算法研究

"工期固定—资源均衡"优化是指在工期一定的条件下,合理调整网络计划的某些工序,以实现资源均衡利用的一种管理方法.本文基于工程项目资源均衡优化方法中常用的遗传算法和最小矩法,提出了一种混合遗传算法.该算法首先使用遗传算法得到一个较好的初始点,然后采用最小矩法进行局部优化,克服了遗传算法局部寻优能力不足的缺陷,增强了算法的优化效果.最后通过算例分析验证了该混合算法的可行性和有效性,因而是一种较好的优化算法.     

利用模拟退火遗传算法实现图像阈值分割

本文提出了一种利用模拟退火算法和遗传算法相结合的图像阈值分割算法,试验结果表明该算法增强了算法的全局收敛性,加快了算法的收敛速度,提高了图像阈值分割的效率.     

混沌遗传算法对BP神经网络的改进研究

BP神经网络算法是目前应用最广泛的一种神经网络算法,但有收敛速度慢和易陷入局部极小值等缺陷.本文利用混沌遗传算法(CGA)具有混沌运动遍历性、遗传算法反演性的特性来改进BP神经网络算法.该算法的基本思想是用混沌遗传算法对BP神经网络算法的初始权值和初始阈值进行优化.把混沌变量加入遗传算法中,提高遗传算法的全局搜索能力和收敛速度;用混沌遗传算法优化后得到的最优解作为BP神经网络算法的初始权值和阈值.通过实验观察,改进后的结果与普通的BP神经网络算法的结果相比,具有更高的准确率.     

混合遗传算法收敛性分析

针对遗传算法爬山能力弱但合局搜索能力强的特点 ,本文将遗传算法嵌入到基入传统优化的拟下降算法中 ,并对算法的拟下降步骤做了一定的改进 ,使得整个算法具有全局收敛性 .本文采用马尔可夫的观点进一步证明了算法的全局收敛性 ,并用极难优化的测试函数给出了数值算例 ,证明了本文算法为一种可行的全局优化算法 .     

零售业连锁网点选址与布局演化模型

蚁群算法是近年来出现的一种新型仿生优化算法,是求解复杂优化问题有效方法.本文建立了基于蚁群算法的零售业连锁网点选址与布局演化模型,并利用Matlab进行仿真研究.通过对模拟结果的分析,验证了零售业连锁网点的选址与布局规律.     

作业车间调度问题的改进混合遗传算法

作业车间调度是一类求解困难的组合优化问题,本文在考虑遗传算法早熟收敛问题和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法相结合,提出了一种基于遗传和禁忌搜索的混合算法,并用实例对该算法进行了仿真研究.结果表明,该算法有很好的收敛精度,是可行的,与传统的算法相比较,有明显的优越性.     

蚁群算法下断层追踪在井震联合构造解释中的应用——以喇南中东一区萨二油层组为例

蚁群算法是最近十余年刚刚兴起的一种高效的仿生优化算法,算法采用分布式并行计算和正反馈自我催化机制,具有易与和其他算法结合等特点.将其应用于断层追踪能够客观、全面的诠释断层的空间展布情况,有效的提高断层解释精度.以喇南中东一区萨二油层组为例,应用基于蚁群算法下断层追踪方法准确落实目的层段断层展布规律,能够很好地解决原认识在开发过程中所遇到的矛盾,对于深入油层开发、完善注采系统、挖潜剩余油具有一定的指导意义.     

车辆路径问题的改进遗传算法

提出一种基于遗传算法的求解车辆路径问题的新算法,避免传统遗传算法处理不可行约束条件中惩罚项系数选取不当所出现的问题.同时,通过现实例子分析该算法的优劣性,实验结果表明该算法是一种有效的算法.     

蚁群遗传混合算法

将蚁群遗传混合算法分别求解离散空间的和连续空间优化问题.求解旅行商问题的混合算法是以遗传算法为整个算法的框架,利用了蚁群算法中的信息素特性的进行交叉操作;根据旅行商问题的特点,给出了4种变异策略;针对遗传算法存在的过早收敛问题,加入2-0pt方法对问题求解进行了局部优化.与模拟退火算法、标准遗传算法和标准蚁群算法进行比较,4种混合算法效果都比较好,策略D的混合算法效果最好.求解连续空间优化问题是以蚁群算法为整个算法的框架,加入遗传算法的交叉操作和变异操作,用测试函数验证了混合蚁群算法的正确性.     

求多峰函数全部全局最优解的改进遗传算法

本文研究了多峰优化问题,利用梯度算子和筛选策略,得到了一种可求解多峰函数全部最优解的改进型遗传算法.数值模拟结果表明,该算法在处理复杂多峰函数优化问题时,局部搜索能力和克服过早收敛能力方面相对于传统遗传算法均有很大提高.     

Optimal Design of Reliable Computer Networks: A Comparison of Metaheuristics

In many computer communications network design problems, such as those faced by hospitals, universities, research centers, and water distribution systems, the topology is fixed because of geographical and physical constraints or the existence of an existing system. When the topology is known, a reasonable approach to design is to select components among discrete alternatives for links and nodes to maximize reliability subject to cost. This problem is NP-hard with the added complication of a very computationally intensive objective function. This paper compares the performance of three classic metaheuristic procedures for solving large and realistic versions of the problem: hillclimbing, simulated annealing and genetic algorithms. Three alterations that use local search to seed the search or improve solutions during each iteration are also compared. It is shown that employing local search during evolution of the genetic algorithm, a memetic algorithm, yields the best network designs and does so at a reasonable computational cost. Hillclimbing performs well as a quick search for good designs, but cannot identify the most superior designs even when computational effort is equal to the metaheuristics.     

Bayesian variable selection in generalized linear models using a combination of stochastic optimization methods

In this paper the usage of a stochastic optimization algorithm as a model search tool is proposed for the Bayesian variable selection problem in generalized linear models. Combining aspects of three well known stochastic optimization algorithms, namely, simulated annealing, genetic algorithm and tabu search, a powerful model search algorithm is produced. After choosing suitable priors, the posterior model probability is used as a criterion function for the algorithm; in cases when it is not analytically tractable Laplace approximation is used. The proposed algorithm is illustrated on normal linear and logistic regression models, for simulated and real-life examples, and it is shown that, with a very low computational cost, it achieves improved performance when compared with popular MCMC algorithms, such as the MCMC model composition, as well as with “vanilla” versions of simulated annealing, genetic algorithm and tabu search.     

求解整数规划问题的混合遗传算法及收敛性

1引言 科学和工程领域中的许多优化问题最终可以归结为求解一个带有约束条件的整数规划问题.其形式为: {maxx∈In f(x) s.t.gi(x)=0,j=1,…,me; gi(x)≥0,i=me+1,…m, x∈nΠi=1 Ai, 式中I表示整数集,x=(x1,…,xn)T,Ai(i∈{1,…,n})为有限整数集. 遗传算法作为一种优化技术,是一种近似算法,一般不能保证一定能得到优化问题的精确解.     

A comparison of local search methods for flow shop scheduling

Local search techniques are widely used to obtain approximate solutions to a variety of combinatorial optimization problems. Two important categories of local search methods are neighbourhood search and genetic algorithms. Commonly used neighbourhood search methods include descent, threshold accepting, simulated annealing and tabu search. In this paper, we present a computational study that compares these four neighbourhood search methods, a genetic algorithm, and a hybrid method in which descent is incorporated into the genetic algorithm. The performance of these six local search methods is evaluated on the problem of scheduling jobs in a permutation flow shop to minimize the total weighted completion time. Based on the results of extensive computational tests, simulated annealing is found to generate better quality solutions than the other neighborhood search methods. However, the results also indicate that the hybrid genetic descent algorithm is superior to simulated annealing.     

A modified DNA genetic algorithm for parameter estimation of the 2-Chlorophenol oxidation in supercritical water

Based on the mechanism of biological DNA genetic information and evolution, a modified DNA genetic algorithm (MDNA-GA) is proposed to estimate the kinetic parameters of the 2-Chlorophenol oxidation in supercritical water. In this approach, DNA encoding method, choose crossover operator and frame-shift mutation operator inspired by the biological DNA are developed for improving the global searching ability. Besides, an adaptive mutation probability which can be adjusted automatically according to the diversity of population is also adopted. A local search method is used to explore the search space to accelerate the convergence towards global optimum. The performance of MDNA-GA in typical benchmark functions and kinetic parameter estimation is studied and compared with RNA-GA. The experimental results demonstrate that the proposed algorithm can overcome premature convergence and yield the global optimum with high efficiency.     

基于模矢搜索和遗传算法的混合约束优化算法

近年,免梯度方法又开始引起大家的注意，由于不需要计算函数的梯度．特别适合用来求解那些无法得到梯度信息或需要花很大计算量才能得到梯度信息的问题．本文构造了一个基于模矢搜索和遗传算法的混合优化算法．在模矢搜索方法的搜索步，用一个类似于遗传算法的方法产生一个有限点集．算法是全局收敛的.     

A genetic algorithm with local search strategy for improved detection of community structure

On the basis of modularity optimization, a genetic algorithm is proposed to detect community structure in networks by defining a local search operator. The local search operator emphasizes two features: one is that the connected nodes in a network should be located in the same community, while the other is “local selection” inspired by the mechanisms of efficient message delivery underlying the small‐world phenomenon. The results of community detection for some classic networks, such as Ucinet and Pajek networks, indicate that our algorithm achieves better community structure than other methodologies based on modularity optimization, such as the algorithms based on betweenness analysis, simulated annealing, or Tasgin and Bingol's genetic algorithm. © 2009 Wiley Periodicals, Inc. Complexity, 2010     

A Hybrid Genetic Algorithm with Boltzmann Convergence Properties

Stochastic global search algorithms such as genetic algorithms are used to attack difficult combinatorial optimization problems. However, genetic algorithms suffer from the lack of a convergence proof. This means that it is difficult to establish reliable algorithm braking criteria without extensive a priori knowledge of the solution space. The hybrid genetic algorithm presented here combines a genetic algorithm with simulated annealing in order to overcome the algorithm convergence problem. The genetic algorithm runs inside the simulated annealing algorithm and provides convergence via a Boltzmann cooling process. The hybrid algorithm was used successfully to solve a classical 30-city traveling salesman problem; it consistently outperformed both a conventional genetic algorithm and a conventional simulated annealing algorithm. This work was supported by the University of Colorado at Colorado Springs.     

A DERIVATIVE-FREE ALGORITHM FOR UNCONSTRAINED OPTIMIZATION

In this paper a hybrid algorithm which combines the pattern search method and the genetic algorithm for unconstrained optimization is presented. The algorithm is a deterministic pattern search algorithm,but in the search step of pattern search algorithm,the trial points are produced by a way like the genetic algorithm. At each iterate, by reduplication,crossover and mutation, a finite set of points can be used. In theory,the algorithm is globally convergent. The most stir is the numerical results showing that it can find the global minimizer for some problems ,which other pattern search algorithms don＇t bear.