改进型蚁群算法的多处理机任务调度研究

蚁群算法是一种新型的模拟进化算法,具有正反馈、分布式计算等特点,是一种解决组合优化问题的有效算法。在介绍蚁群算法基本原理以及探讨该算法的缺陷基础上,针对多处理器任务调度问题,提出了一种基于改进型蚁群算法的调度策略。仿真研究表明,该算法具有优良的全局优化性能,效果令人满意。     

一种新的融合分布估计的蚁群优化算法

提出了一种新的融合分布估计的蚁群优化算法。该算法突破了传统蚁群过早收敛的局限性,且蚁群中的每个蚂蚁具有更全面的学习能力,从而能够有效地解决组合优化问题。仿真实验结果表明该算法的性能优于现有的其它几种蚁群优化算法。     

基于蚁群算法的PID参数寻优

蚁群算法是一种新型的模拟进化算法,该算法用于离散空间问题的求解取得了较好的结果.该文将蚁群算法引入连续空间,研究了基于蚁群算法的PID参数优化问题,给出了仿真实例,结果表明蚁群算法用于解决连续空间优化问题是可行且有效的.蚁群算法具有较好的鲁棒性,它采用分布式计算,具有本质并行性.     

蚁群算法局部最优解决机制的探讨

启发式蚁群算法是模拟蚂蚁群体觅食行为的一种仿生智能优化算法。该算法集结了多种仿生智能算法的优点,解决了许多复杂优化问题,比如著名的旅行商(TSP)问题,但启发式蚁群算法无法避免陷入局部最优的寻优困境。介绍了蚁群算法的工作原理,针对蚁群算法容易陷入局部最优的特点,提出通过轮盘选择来解决求解的随机性,从而避免陷入局部最优的解决机制。     

混合智能算法在多约束优化问题中的应用*

提出了一种将蚁群算法、遗传算法和粒子种群优化融合的混合智能算法来解决多约束最优路径和QoS路由问题。采用蚁群算法进行寻径生成初始群体,利用遗传算法对路径进行优化,利用PSO算法来优化蚁群算法中的信息素,优势互补。仿真结果表明该算法是可行、有效的。     

基于蚁群优化的分布式Qos多播路由方法研究

提出了一种基于蚁群优化的分布式QoS多播路由算法,蚁群算法是解决多QoS约束组播路由问题的一种启发式算法,多QoS约束的组播路由技术是当前实现分布式网络多媒体的关键技术.给出了该算法实现的步骤,还结合多播路由问题的特点对算法进行了改进.通过仿真实验讨论了该方法的性能,并与传统的蚁群算法对比,证实了该方法的有效性.     

多蚁群协同进化的滑行道调度优化

针对当前机场滑行道调度效率较低的问题,提出一种基于协同进化的多蚁群算法。利用蚁群算法在解决复杂的组合优化问题方面的显著优势,在种群内部搜索滑行时间短且没有冲突的路径,在种群间利用蚁群算法良好的协同能力同时进行多个航班的调度,解决滑行的路径搜索问题,实现调度顺序的优化调整。冲突解决是解决滑行道调度问题的关键,采用基于两段锁的思想解决冲突。利用公共数据集对该算法进行验证,实验结果表明了该算法的有效性。     

蚁群算法在QoS组播路由问题中的应用

研究了该算法在QoS组播路由问题中的应用,描述了QoS路由优化问题。基于多个不相关可加度量的QoS路由问题是NP完全问题,目前采用的方法多为启发式算法。由于蚁群算法是一种基于蚁群系统原理的、具有自组织能力的、新型的启发式优化算法,利用其能够寻找最短路径这一特性,提出了一种基于蚁群系统原理,用于解决时延和时延抖动约束问题的组播路由问题的QoS组播路由算法。该算法改进了路径选择策略,优化了信息素更新公式。仿真结果表明,该算法能够迅速、准确地找到最优解。     

基于改进蚁群算法求解迷宫最优路径

蚁群算法是一种基于群体智能原理的优化模型,用于解决组合优化问题。该文在蚁群算法的选择策略方面进行改进,提出了基于改进蚁群算法求解迷宫最优路径的算法。     

基于交互式的并行蚁群优化算法

传统蚁群优化算法研究已经取得了很多重要的成果,但是在解决大规模组合优化问题时仍存在早熟收敛,搜索时间长等缺点.为此,将邻域搜索技术与蚁群优化算法进行融合,提出一种新的并行蚁群优化算法,实验结果表明,在解决大规模TSP问题时,该算法求解质量和稳定性更好,在短时间内即可得到较高质量的解.     

The Ant Lion Optimizer

This paper proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the hunting mechanism of antlions in nature. Five main steps of hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps are implemented. The proposed algorithm is benchmarked in three phases. Firstly, a set of 19 mathematical functions is employed to test different characteristics of ALO. Secondly, three classical engineering problems (three-bar truss design, cantilever beam design, and gear train design) are solved by ALO. Finally, the shapes of two ship propellers are optimized by ALO as challenging constrained real problems. In the first two test phases, the ALO algorithm is compared with a variety of algorithms in the literature. The results of the test functions prove that the proposed algorithm is able to provide very competitive results in terms of improved exploration, local optima avoidance, exploitation, and convergence. The ALO algorithm also finds superior optimal designs for the majority of classical engineering problems employed, showing that this algorithm has merits in solving constrained problems with diverse search spaces. The optimal shapes obtained for the ship propellers demonstrate the applicability of the proposed algorithm in solving real problems with unknown search spaces as well. Note that the source codes of the proposed ALO algorithm are publicly available at http://www.alimirjalili.com/ALO.html.     

GVF Snake模型中初始轮廓线设置算法的研究

在详细分析了动态轮廓模型抗干扰性差、运算量大、不能逼近比较复杂轮廓、初始轮廓线设置复杂等问题原因的基础上,提出了轮廓线"有效逼近域"概念,进一步研究发现,这些问题都可以通过设置初始轮廓线到"有效逼近域"内,并有效地控制"有效逼近域"范围而得以解决。由于初始轮廓线只要求设置在"有效逼近域"内,因此符合基于小波变换多尺度边缘检测算法的特性,"有效逼近域"也可以通过控制GVF力场迭代次数来有效控制其大小。基于此,通过采用小波变换多尺度边缘检测获得不同分辨率边缘点,合理控制GVF力场迭代次数,提出初始轮廓线连接算法,成功地实现了初始轮廓线的有效设置。实验结果表明,能够准确地将初始轮廓线设置在"有效逼近域"内,并且"有效逼近域"的大小可以减小到真实轮廓左右5个像素以内,运算量有效的减小,抗干扰性也得到了很大的提高。     

OCSVM集成模型解决支持向量机的大规模训练问题

该文针对集成方法实现支持向量机大规模训练的相关问题进行了深入研究,提出了一种称为"DD-Boosting"的成员分类器产生算法,能够在大规模数据集情况下利用类似Boosting技术产生稳定、高泛化性能的成员分类器。在此基础上,推导出基于OCSVM的分类器集成模型,实验仿真表明,该集成模型能够获得比主投票方法更好的泛化性能,且通过调整正则参数避免了训练过拟合问题。     

The Power of Priority Algorithms for Facility Location and Set Cover

We apply and extend the priority algorithm framework introduced by Borodin, Nielsen, and Rackoff to define greedy-like algorithms for the (uncapacitated) facility location problems and set cover problems. These problems have been the focus of extensive research from the point of view of approximation algorithms and for both problems greedy-like algorithms have been proposed and analyzed. The priority algorithm definitions are general enough to capture a broad class of algorithms that can be characterized as greedy-like while still possible to derive non-trivial lower bounds on the approximability of the problems by algorithms in such a class. Our results are orthogonal to complexity considerations, and hence apply to algorithms that are not necessarily polynomial time.     

A neural-network learning theory and a polynomial time RBFalgorithm

This paper presents a new learning theory (a set of principles for brain-like learning) and a corresponding algorithm for the neural-network field. The learning theory defines computational characteristics that are much more brain-like than that of classical connectionist learning. Robust and reliable learning algorithms would result if these learning principles are followed rigorously when developing neural-network algorithms. This paper also presents a new algorithm for generating radial basis function (RBF) nets for function approximation. The design of the algorithm is based on the proposed set of learning principles. The net generated by this algorithm is not a typical RBF net, but a combination of "truncated" RBF and other types of hidden units. The algorithm uses random clustering and linear programming (LP) to design and train this "mixed" RBF net. Polynomial time complexity of the algorithm is proven and computational results are provided for the well known Mackey-Glass chaotic time series problem, the logistic map prediction problem, various neuro-control problems, and several time series forecasting problems. The algorithm can also be implemented as an online adaptive algorithm.     

基于K-均值的“教”与“学”优化算法

在解决复杂多峰优化问题时,传统的"教"与"学"优化算法易于陷入局部搜索且优化效率较低.针对此问题,提出了一种基于K-均值的"教"与"学"优化改进算法,算法采用K-均值来降低种群规模,又针对"教"和"学"两个阶段进行相应改进,提高全局收敛速度;还加入了"变异"操作来避免算法陷入局部最优.实验对7个单峰值优化问题和2个有代表性的多峰值优化问题进行优化,并与手榴弹爆破算法和传统"教"与"学"优化算法进行比较,实验结果表明,该改进算法在单峰和多峰测试函数中,均能快速高效地寻得全局最优解,优于原始"教"与"学"优化算法.     

The joint movement of pebbles in solving the ($$N^2-1$$)-puzzle suboptimally and its applications in rule-based cooperative path-finding

The present paper deals with the problem of solving the (\(n^2 - 1\))-puzzle and cooperative path-finding (CPF) problems sub-optimally by rule-based algorithms. To solve the puzzle, we need to rearrange \(n^2 - 1\) pebbles in the \(n \times n\)-sized square grid using one vacant position to achieve the goal configuration. An improvement to the existing polynomial-time algorithm is proposed and experimentally analyzed. The improved algorithm represents an attempt to move pebbles in a more efficient way compared to the original algorithm by grouping them into so-called snakes and moving them together as part of a snake formation. An experimental evaluation has shown that the snakeenhanced algorithm produces solutions which are 8–9 % shorter than the solutions generated by the original algorithm. Snake-like movement has also been integrated into the rule-based algorithms used in solving CPF problems sub-optimally, which is a closely related task. The task in CPF consists in moving a group of abstract robots on an undirected graph to specific vertices. The robots can move to unoccupied neighboring vertices; no more than one robot can be placed in each vertex. The (\(n^2 - 1\))-puzzle is a special case of CPF where the underlying graph is a 4-connected grid and only one vertex is vacant. Two major rule-based algorithms for solving CPF problems were included in our study—BIBOX and PUSH-and-SWAP (PUSH-and-ROTATE). The use of snakes in the BIBOX algorithm led to consistent efficiency gains of around 30 % for the (\(n^2 - 1\))-puzzle and up to 50 % in for CPF problems on biconnected graphs with various ear decompositions and multiple vacant vertices. For the PUSH-and-SWAP algorithm, the efficiency gain achieved from the use of snakes was around 5–8 %. However, the efficiency gain was unstable and hardly predictable for PUSH-and-SWAP.     

求解圆形Packing问题的一个启发式算法

求解NP难度问题一直是计算机科学技术中的一个瓶颈任务,自20世纪70年代以来的研究表明,求解NP难度问题不存在既完整严格又不大慢的求解算法,因此,近年来,启发式方法成为研究热点,圆形Packing问题是NP难的,具有很高的理论和实践价值,它的求解目标是录求多个圆在一个大圆内的一个优良布局,使得这些圆互不重叠地放置,基于拟物法以及适者生存启发式思想,为圆形Packing问题的快速求解提出了一个高效的启发式算法,算法的高效性通过计算实例得到了验证。     

CARVE-a constructive algorithm for real-valued examples

A constructive neural-network algorithm is presented. For any consistent classification task on real-valued training vectors, the algorithm constructs a feedforward network with a single hidden layer of threshold units which implements the task. The algorithm, which we call CARVE, extends the "sequential learning" algorithm of Marchand et al. (1990) from Boolean inputs to the real-valued input case, and uses convex hull methods for the determination of the network weights. The algorithm is an efficient training scheme for producing near-minimal network solutions for arbitrary classification tasks. The algorithm is applied to a number of benchmark problems including German and Sejnowski's sonar data, the Monks problems and Fisher's iris data. A significant application of the constructive algorithm is in providing an initial network topology and initial weights for other neural-network training schemes, and this is demonstrated by application to backpropagation.     

A ${bm lambda}$-Cut and Goal-Programming-Based Algorithm for Fuzzy-Linear Multiple-Objective Bilevel Optimization

Bilevel-programming techniques are developed to handle decentralized problems with two-level decision makers, which are leaders and followers, who may have more than one objective to achieve. This paper proposes a ${lambda}$-cut and goal-programming-based algorithm to solve fuzzy-linear multiple-objective bilevel (FLMOB) decision problems. First, based on the definition of a distance measure between two fuzzy vectors using ${lambda}$-cut, a fuzzy-linear bilevel goal (FLBG) model is formatted, and related theorems are proved. Then, using a ${lambda}$-cut for fuzzy coefficients and a goal-programming strategy for multiple objectives, a ${lambda}$-cut and goal-programming-based algorithm to solve FLMOB decision problems is presented. A case study for a newsboy problem is adopted to illustrate the application and executing procedure of this algorithm. Finally, experiments are carried out to discuss and analyze the performance of this algorithm.