电网购电模型求解的一种启发式优化算法

启发式算法利用与所求问题有关的某些特殊住处来控制搜索状态空间的过程,对于某些难于用理论方法解决的问题可以起到独到的作用.在电网购电模型中会有离散和连续两类不同性质的变量,其目标函数为非连续、不可微的函数,模型求解有很大的难度.本文利用购电模型的特点,提出了一种启发式优化算法,该算法不涉及微分计算,只涉及各个发电商报价高低的比较和功率的累加,每一步的计算简单方便,算法的收敛速度快.     

基于LSP时延和拥塞的分布式自适应流量工程算法

研究多协议标签交换(MPLS)网络中的流量工程,提出了基于标签交换路径(LSP)时延和拥塞 来优化网络流量的算法。给出全网流量优化的数学模型,并根据该模型内在的层次性,设计 了分布式自适应流量工程算法。为了使算法具有实时性强、收敛速度快、稳定性较好、运算 和网络开销小等特点,在设计中,采用了禁忌搜索和算法参数的非线性调整来提高算法的性 能。仿真分析表明,该算法能够实现对网络性能的全面优化,适合应用于骨干网的流量优化.     

启发式算法的装配线的平衡改善应用

装配线是普遍存在的一种制造系统,装配线的均衡与否直接关系到生产体系的生产率。通过对某空调主控制板装配线工序的时间研究,利用动作经济原则、“5W1 H”的方法对生产线进行优化改善,制定一个合理的操作方法和标准作业时间,采用启发式算法原理对装配线的平衡计算,达到劳动强度的降低和运行时间最小化,提高生产线的均衡率,从而能适应产品消费市场的变化。     

Recognizing National Hockey League greatness with an ignorance-based heuristic.

This study examined whether people adhered to the recognition heuristic (i.e., inferred that a recognized hockey player had more total career points than an unrecognized player) and whether using this heuristic could yield accurate decisions. On paired comparisons, having participants report whether they recognized each player plus any knowledge they had about each player permitted players to be classified as either unrecognized (UR), merely recognized (MR), or recognized with additional knowledge (RK), thus producing six possible trial types. Participants adhered to the recognition heuristic on 95% of MR-UR trials and were accurate on 81% of those trials. They chose the recognized player on 98% of RK-UR trials, yielding 94% accuracy. Women had less knowledge and recognized fewer players than men, yet they were nearly as accurate as men. Future research should examine the conditions under which the recognition heuristic is an adaptive strategy. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

支持延时约束的覆盖多播路由协议的研究

研究有度和延时约束的覆盖多播路由问题,提出了一个新的覆盖多播路由协议-延时受限的树协议(DBTP)。该协议采用分布式和树优先的策略,使多播组成员之间能自组织地构建一棵基于源的覆盖多播树。DBTP协议采用了一种新的启发式局部优化算法,通过调节启发因子,能灵活地在延时和代价之间进行折衷。仿真实验表明,无论在静态还是动态节点模型下,选择适当的启发参数,DBTP都能获得较高的节点接纳率。     

Pseudocontingencies.

In 3 experiments, the authors explored a contingency illusion termed pseudocontingency (PC) that produces logically unwarranted but potentially useful inferences. PCs arise when bivariate contingencies are inferred from univariate distributions via heuristic alignment processes. For example, in the absence of information about the co-occurrence of TV habits and aggressive behavior within a school class, when the prevalence of both attributes is high, a teacher may infer a positive PC as if students who often watch TV were highly aggressive. Throughout 3 experiments, predictions of the level of 1 variable from the level of another served as a measure of PCs. The illusion could be evoked reliably whether information about target attributes was presented successively or simultaneously, whether common-cause or common-effect models were activated, and whether attributes involved 2 or more levels. The discussion centers on the cognitive processes underlying PCs and their origin and adaptive value. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Research on social network influence maximization algorithm based on time sequential relationship

For the time sequential relationship between nodes in a dynamic social network,social network influence maximization based on time sequential relationship was proved.The problem was to find k nodes on a time sequential social network to maximize the spread of information.Firstly,the propagation probability between nodes was calculated by the improved degree estimation algorithm.Secondly,in order to solve the problem that WCM models based on static social networks could not be applied to time sequential social networks,an IWCM propagation model was proposed and based on this,a two-stage time sequential social network influence maximization algorithm was proposed.The algorithm used the time sequential heuristic phase and the time sequential greedy phase to select the candidate node with the largest influence estimated value inf (u) and the most influential seeds.At last,the efficiency and accuracy of the TIM algorithm were proved by experiments.In addition,the algorithm combines the advantages of the heuristic algorithm and the greedy algorithm,reducing the calculation range of the marginal revenue from all nodes in the network to the candidate nodes,and greatly shortens the running time of the program while ensuring accuracy.     

Minimizing makespan during environmental stress screening using a genetic algorithm and an ant colony optimization

The environmental stress screening (ESS) chamber employed in a typical electronics manufacturing service (EMS) provider’s facility is used for screening products, attempting to expose defects that cannot be detected by visual inspection or electrical testing. The ESS chambers are bottleneck work centers in most of the EMS facilities. This research uses a genetic algorithm (GA) and an ant colony optimization (ACO) to solve the job-scheduling problem at the ESS chamber where the makespan of the process is minimized. The performances of the two approaches were compared against some of the first-fit (FF) techniques from the literature. Both the GA and ACO techniques produced satisfactory results up to a job size of 40. Furthermore, ACO generally requires a longer computational time, while offering more consistent solution quality.     

Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization

Over the last decade, evolutionary and meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. Their broad applicability, ease of use, and global perspective may be considered as the primary reason for their success. The honey-bees mating process may also be considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bees mating. In this paper, the honey-bees mating optimization algorithm (HBMO) is presented and tested with few benchmark examples consisting of highly non-linear constrained and/or unconstrained real-valued mathematical models. The performance of the algorithm is quite comparable with the results of the well-developed genetic algorithm. The HBMO algorithm is also applied to the operation of a single reservoir with 60 periods with the objective of minimizing the total square deviation from target demands. Results obtained are promising and compare well with the results of other well-known heuristic approaches.