An adaptive single-point algorithm for global numerical optimization

This paper describes a novel algorithm for numerical optimization, called Simple Adaptive Climbing (SAC). SAC is a simple efficient single-point approach that does not require a careful fine-tunning of its two parameters. SAC algorithm shares many similarities with local optimization heuristics, such as random walk, gradient descent, and hill-climbing. SAC has a restarting mechanism, and a powerful adaptive mutation process that resembles the one used in Differential Evolution. The algorithms SAC is capable of performing global unconstrained optimization efficiently in high dimensional test functions. This paper shows results on 15 well-known unconstrained problems. Test results confirm that SAC is competitive against state-of-the-art approaches such as micro-Particle Swarm Optimization, CMA-ES or Simple Adaptive Differential Evolution.     

基于HAGA的D2D-NOMA资源分配优化算法

针对无线系统带宽资源有限、基站负载压力大、传输时延长等问题,提出一种基于非正交多址接入技术的D2D系统吞吐量最大化资源分配算法。在不同用户的服务质量约束条件下,建立D2D系统吞吐量最大化资源分配模型。该模型的优化目标是一个混合整数非线性规划问题,将其解耦为信道匹配与功率分配2个子问题并分别进行处理,利用自适应惩罚函数法处理约束条件并提出一种基于爬山策略的自适应遗传算法以对问题进行求解。仿真结果表明,与GA、AGA算法相比,该算法能够有效提高D2D系统的吞吐量,且收敛性能更好。     

改进遗传单纯形混合算法在光纤对接中的应用

本文首先给出了纤端光场分布的3D模型并针对单模光纤与激光器的对接分析了错位、角度偏差等对接效率影响因素。通过与传统的爬山法进行比较,提出将遗传单纯形混合算法应用到光纤对接中。单纯形法是一种局部搜索方法,这种寻优方法收敛速度快,它与遗传算法组合起来可以改善单独使用遗传算法收敛速度慢的缺陷。同时,针对光纤对接要求初始种群小所造成的个体多样性差的缺点,改进了单纯形的反射、压缩系数,有效地避免了遗传操作的过早收敛,并通过仿真结果证明了混合算法可以在10代之内找到光纤对接的最佳耦合点。     

永磁同步风力发电系统最大功率追踪新方法

风力发电系统的输出功率随着风速变化而变化,引起输出功率的波动。因此提出一种采用永磁同步电机作为发电机实现最大功率输出的永磁同步风力发电控制系统。建立了风力机模型、最大功率追踪算法模型,采用二分法和停止机制算法,使风机按最大功率点跟踪方式运行,通过二分法实现变步长,并加入停止机制降低转速振荡,防止在最大功率点的波动。介绍了传统爬山法和改进的爬山法,通过对比仿真研究,表明新方法可以有效提高最大功率追踪效率,并减少系统在最大功率点的波动。     

混合遗传算法在圆形件优化排样中的应用研究

本文研究的是圆形件优化排样问题,是将卷材切成若干种圆形毛坯,使所产生的废料最少以达到节约材料的目的。本文在我们提出的放置算法(ASA)的基础上,采用混合遗传算法作为搜索策略;实验测试结果表明,本文算法可以和排样领域著名的法国学者Hifi在国外检索刊物上提出的算法相媲美,该算法的计算时间可以满足一般实践应用的要求,所得排样方案的材料利用率较高。     

Modified quadratic hill-climbing with SAS/IML

This article describes EZClimb, a set of SAS/IML steps useful in solving numerical optimization problems. The program uses the method of modified quadratic hill-climbing with either analytical or numerical derivatives to maximize a user-defined criterion function. Modified quadratic hill-climbing is one of the more powerful algorithms known for function optimization but is not widely available outside of the software package GQOPT. The efficacy of the SAS steps is illustrated using Rosenbrock's function, Savin and White's Box-Cox extended autoregressive model, and Klein's Model I.Abbreviations BHHH Berndt, Hall, Hall, and Hausman's optimization algorithm - FIML full-information maximum-likelihood - SAS/IML SAS Institute's Interactive Matrix Language - TSP Time Series Processor software package     

遗传算法改进策略在套裁排样中的应用

针对遗传算法存在的局限性,总结了提高遗传算法性能和避免遗传算法早熟的一些策略。并将一些策略应用于我们研发的圆形套裁下料系统。实验证实这些策略有良好的效果。     

Human optimization with moving optima

Abstract

Earlier work had examined how humans optimize a two-variable function. This paper extends the study to optimization when the position of the optimum can change with time. Thirty student subjects performed a blind hill climbing task under two levels of display noise and three levels of movement of the optimum. Performance was studied using 13 measures, which were found to group into three factors, ‘deviation size’, ‘hits on target’ and ‘speed’. It was found that the movement of the optimum degraded performance once the general area of the optimum was reached. Display noise also degraded performance. Subjects with formal optimization training performed better on the ‘deviation size’ factor but had no advantage on ‘hits on target’ and ‘speed’ factors.     

Energy capture by a small wind-energy conversion system

Furling is the most common method used by the small wind-turbine industry to control the aerodynamic power extraction from the wind. A small wind-turbine with furling mechanism and its resulting dynamics are modelled using Matlab/Simulinkplatform in this paper. The model simulates regulating the speed of the wind-turbine via a load-control method. Tip-speed ratio and hill-climbing control methods for maximum-power extraction from a small wind-turbine are investigated. Two dynamic controllers are designed and their behaviours simulated. In the first method, the controller uses the wind-speed and rotor speed information to control the load in order to operate the wind-turbine at its optimal tip-speed ratio. In the second method, the controller compares the output power of the turbine with the previous power, and controls the load based on the power difference. In order to determine a suitable control strategy for the small wind-energy conversion system, several tests are performed. Wind-speed versus power-curve and annual energy capture of the system for each control method are determined for wind conditions in St. John’s, Newfoundland. The annual energy-capture is determined using the bin’s power-curve method. Wind-speed data and Rayleigh distribution of St. John’s, Newfoundland are used to determine the annual energy-capture. The results of the simulations indicate that the energy capture of a wind-turbine depends not only on the control strategy but on the wind-speed and Rayleigh distribution. The results of the investigation lead to the conclusion that the hill-climbing method of control results in a greater annual energy-output.     

Cost-driven vertical class partitioning for methods in object oriented databases

In object-oriented databases (OODBs), a method encapsulated in a class typically accesses a few, but not all the instance variables defined in the class. It may thus be preferable to vertically partition the class for reducing irrelevant data (instance variables) accessed by the methods. Our prior work has shown that vertical class partitioning can result in a substantial decrease in the total number of disk accesses incurred for executing a set of applications, but coming up with an optimal vertical class partitioning scheme is a hard problem. In this paper, we present two algorithms for deriving optimal and near-optimal vertical class partitioning schemes. The cost-driven algorithm provides the optimal vertical class partitioning schemes by enumerating, exhaustively, all the schemes and calculating the number of disk accesses required to execute a given set of applications. For this, a cost model for executing a set of methods in an OODB system is developed. Since exhaustive enumeration is costly and only works for classes with a small number of instance variables, a hill-climbing heuristic algorithm (HCHA) is developed, which takes the solution provided by the affinity-based algorithm and improves it, thereby further reducing the total number of disk accesses incurred. We show that the HCHA algorithm provides a reasonable near-optimal vertical class partitioning scheme for executing a given set of applications.Received: 29 March 1999, Accepted: 11 March 2002, Published online: 3 April 2003This research has been supported, in part, by Hong Kong UGC Research Grants Council under grant CityU 733/96E and CityU 1119/99E.