基于T2FCM-FE-HMMs的隐马氏模型参数估计算法

把2型模糊集的思想引入到了基于模糊聚类的离散HMM参数训练中,提出了改进的T 2FCM-FE-HMM s算法。     

基于S型曲线加减速的数控加工刀具运动优化控制

数控机床加工折线时,存在因进给系统反复骤停而加工效率低、加工冲击大等问题.提出折线光滑转接加工优化控制算法,在指定加工误差的条件下,通过引入转接段降低实际转接点附近轨迹精度,从而实现了转接时的速度连续,并给出了转接速度的约束控制条件,对锐角、钝角转接进行了误差分析,验证了控制算法的有效性.针对由直线段和圆弧段组成的连续曲线整体加工控制问题,建立基于S型曲线加减速的加工控制模型,分析了圆弧半径对算法效率的影响.最后针对S型曲线加减速算法中加加速度存在阶跃变化的缺陷,提出了一种加加速度连续的改进算法,从而提高了加工质量.     

一种改进的FastICA算法

FastICA算法是一种快速独立分量分析(Independent Component Analysis:ICA)算法,但它是基于牛顿迭代方法和合理近似的一种算法,所以具有改进空间.近年来提出了许多改进的具有更高阶收敛性质的牛顿迭代方法.将一种3阶收敛的牛顿迭代方法引入ICA算法的推导中,在合理近似的基础上,提出了一种改进的两步迭代FastICA算法.与传统FastICA算法相比,提出的改进的FastICA算法一次迭代的计算量有所增加.但是,实验结果表明,新提出的改进的FastICA算法更稳健、具有更快的收敛速度.     

一种改进的动态步长的次梯度算法

次梯度法是解决大规模凸优化问题的经典和有效的方法之一,步长的选取对次梯度法的收敛性起着至关重要的作用.Goffino等(1999)提出了动态步长次梯度算法,通过改进其中的一个参数,提出了改进的动态步长次梯度算法,并证明了改进算法的收敛性.最后,通过数值实验可以看出改进的算法比原来的算法更有效.     

求解全局优化问题的改进蝙蝠算法[英文]

蝙蝠算法(BA)是一类基于试探技巧的群智能优化算法,该算法已被广泛用于诸多领域问题的求解.本文提出一个改进的蝙蝠算法NIBA.在算法中,为了加强蝙蝠算法的局部和全局搜索能力,提出了三个改进策略.首先,为了改进蝙蝠的局部搜索能力,在当前最优解处给出了一个新的搜索方程.其次,为了改进算法的全局搜索能力,平衡算法的开发能力和探索能力,算法吸收并改进了和声搜索机制.最后,为了进一步提高NIBA算法的搜索能力,在当前最优解处,算法采用了混沌搜索机制.为了验证算法的性能,针对18个标准测试函数进行了数值实验.与其它算法的比较结果显示,NIBA算法具有更好的稳定性,且效率更高.     

分层不动点和变分不等式公共解的迭代算法

目的是利用全渐近非扩张映象研究分层不动点和变分不等式公共不动点的迭代算法.在适当条件下,某些强收敛定理被证明.结果改进和推广了Yao Y H(2010)和ZHANG S S等人(2011)的最新结果.     

大规模多设施Weber问题的改进Cooper算法

 多设施Weber问题（multi-source Weber problem,MWP）是设施选址中的重要模型之一,而Cooper算法是求解MWP最为常用的数值方法.Cooper算法包含选址步和分配步,两步交替进行直至达到局部最优解.本文对Cooper算法的选址步和分配步分别引入改进策略,提出改进Cooper算法：选址步中将Weiszfeld算法和adaptive Barzilai-Borwein （ABB）算法结合,提出收敛速度更快的ABB-Weiszfeld算法求解选址子问题;分配步中提出贪婪簇分割策略来处理退化设施,由此进一步提出具有更好性质的贪婪混合策略.数值实验表明本文提出的改进策略有效地提高了Cooper算法的计算效率,改进算法有着更好的数值表现.     

关于同顺序m×n排序问题消去法的两点注记

本文对[1,2]中指出的一类同顺序m×n排序问题有关的消去法提出两点注记。这里补充提出了一种不涉及下界计算并且检验方法简单的消去准则,同时,对[2]中给出的下界B(S…S′)的算法做了改进,从而使下界B(S…S′)的估值精度有所提高。 一、一个消去准则 首先说明,文中运用的术语和记号除特殊说明外均与[2]的定义相同,不再另述。 同顺序m×n排序问题是指:设有n个零件J_1,J_2,…,J_n和m台机床M_1,M_2,     

基于改进二进制粒子群算法的个性化网络学习资源推荐方法

针对目前启发式算法用于解决个性化网络学习资源推荐问题时存在推荐速度较慢、不稳定等问题,文章提出基于改进二进制粒子群算法的个性化网络学习资源推荐方法(AsyBPSO-RA).该方法将个性化网络学习资源推荐问题建构为适应度函数,利用改进二进制粒子群算法(AsyBPSO)优化此适应度函数,生成推荐结果;AsyBPSO采用非对称映射函数,取代基本二进制粒子群算法中的S型映射函数,以更好地平衡算法的探索和开发阶段.通过五组实验结果对比分析发现,AsyBPSO收敛能力强,稳定性高,表明AsyBPSO-RA是较为有效的个性化网络学习资源推荐方法.     

解微分方程组的Adams预测校正算法的改进方法

针对Adams预测校正算法求解微分方程组作了改进.算法的主要改进之处是在校正环节用已经计算出来的"新值"取代"旧值".最后,基于提出的改进方法和传统的Adams预测校正算法对同一微分方程组问题作了数值比较实验.数值实验结果表明改进的算法与传统的Adams预测校正算法的计算量一样,但精度大大提高.     

Fuzzy-2D-HMMS算法及其收敛性

通过对Dat Tran和Michael Wagner等提出的FCM-FE-HMMS算法研究,并把它与2维隐马氏模型联系起来,提出了Fuzzy-2D-HMMS算法,得出在给定初值的情况下该算法将收敛到一个局部最优解.     

A hybrid heuristic algorithm for the 2D variable-sized bin packing problem

In this paper, we consider the two-dimensional variable-sized bin packing problem (2DVSBPP) with guillotine constraint. 2DVSBPP is a well-known NP-hard optimization problem which has several real applications. A mixed bin packing algorithm (MixPacking) which combines a heuristic packing algorithm with the Best Fit algorithm is proposed to solve the single bin problem, and then a backtracking algorithm which embeds MixPacking is developed to solve the 2DVSBPP. A hybrid heuristic algorithm based on iterative simulated annealing and binary search (named HHA) is then developed to further improve the results of our Backtracking algorithm. Computational experiments on the benchmark instances for 2DVSBPP show that HHA has achieved good results and outperforms existing algorithms.     

Global Maximization of a Generalized Concave Multiplicative Function

This article presents a branch-and-bound algorithm for globally solving the problem (P) of maximizing a generalized concave multiplicative function over a compact convex set. Since problem (P) does not seem to have been studied previously, the algorithm is apparently the first algorithm to be proposed for solving this problem. It works by globally solving a problem (P1) equivalent to problem (P). The branch-and-bound search undertaken by the algorithm uses rectangular partitioning and takes place in a space which typically has a much smaller dimension than the space to which the decision variables of problem (P) belong. Convergence of the algorithm is shown; computational considerations and benefits for users of the algorithm are given. A sample problem is also solved.     

Gbest-guided artificial bee colony algorithm for numerical function optimization

Artificial bee colony (ABC) algorithm invented recently by Karaboga is a biological-inspired optimization algorithm, which has been shown to be competitive with some conventional biological-inspired algorithms, such as genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO). However, there is still an insufficiency in ABC algorithm regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by PSO, we propose an improved ABC algorithm called gbest-guided ABC (GABC) algorithm by incorporating the information of global best (gbest) solution into the solution search equation to improve the exploitation. The experimental results tested on a set of numerical benchmark functions show that GABC algorithm can outperform ABC algorithm in most of the experiments.     

一种新的双模式盲均衡算法研究

针对恒模算法(CMA)收敛速度较慢、收敛后均方误差较大的缺点,提出一种新的双模式盲均衡算法.在算法初期,利用能快速收敛的归一化恒模算法(NCMA)进行冷启动,在算法收敛后切换到判决引导(DD-LMS)算法,减少误码率.计算机仿真表明,提出的新算法有较快的收敛速度和较低的误码率.     

A new memetic algorithm based on cellular learning automata for solving the vertex coloring problem

Vertex coloring problem is a combinatorial optimization problem in graph theory in which a color is assigned to each vertex of graph such that no two adjacent vertices have the same color. In this paper a new hybrid algorithm which is obtained from combination of cellular learning automata (CLA) and memetic algorithm (MA) is proposed for solving the vertex coloring problem. CLA is an effective probabilistic learning model combining cellular automata and learning automaton (LA). Irregular CLA (ICLA) is a generalization of CLA in which the restriction of rectangular grid structure in CLA is removed. The proposed algorithm is based on the irregular open CLA (IOCLA) that is an extension of ICLA in which the evolution of CLA is influenced by both local and global environments. Similar to other IOCLA-based algorithms, in the proposed algorithm, local environment is constituted by neighboring LAs of any cell and the global environment consists of a pool of memes in which each meme corresponds to a certain local search method. Each meme is represented by a set of LAs from which the history of the corresponding local search method can be extracted. To show the superiority of the proposed algorithm over some well-known algorithms, several computer experiments have been conducted. The results show that the proposed algorithm performs better than other methods in terms of running time of algorithm and the required number of colors.     

Hybrid algorithm for the two-dimensional rectangular layer-packing problem

In this paper, a rectangular layer-packing algorithm (RLPA) combined with modified genetic algorithm (GA) or particle swarm optimization (PSO) algorithm is developed to solve the problem with emerging restraints, which is raised from the two-dimensional rectangular packing problem with some small rectangles that need to be packed into a fixed rectangular object. RLPA is designed from the BL algorithm and lowest horizontal line algorithm. GA and PSO are also modified to satisfy the constraint conditions. Best GA or PSO parameters are obtained by conducting experiments on some typical instances. The results are also compared, which validate the quality of the solutions and show the effectiveness of the modified algorithm.     

Branch-and-price algorithm for a combined problem of virtual path establishment and traffic packet routing in a layered communication network

This paper considers a combined problem of establishing virtual paths (VPs) and routing traffic (packet) demands through the virtual paths in a layered communication network where each physical link is subject to its capacity constraints. The problem is modeled as a path-based formulation for which a branch-and-price solution algorithm is derived. The solution algorithm is composed of an efficient pricing algorithm, and also a branching rule based on a variable dichotomy, which does not destroy the structure of the associated pricing problems. Computational experiments are performed to test the efficiency of the algorithm, which show that the algorithm works quite well in finding optimal solutions (for the test instances) within reasonable time. Its immediate application may be made to a centralized network management on mid-term global reconfiguration and long-term VP planning.     

Structure Algorithms for Partially Ordered Isotonic Regression

Abstract

An algorithm for isotonic regression is called a structure algorithm if it searches for a “solution partition”—that is, a class of sets on each of which the isotonic regression is a constant. We discuss structure algorithms for partially ordered isotonic regression. In this article we provide a new class of structure algorithms called the isotonic block class (IBC) type algorithms. One of these is called the isotonic block class with recursion method (IBCR) algorithm, which works for partially ordered isotonic regression. It is a generalization of the pooled adjacent violators algorithm and is simpler than the min-max algorithm. We also give a polynomial time algorithm—the isotonic block class with stratification (IBCS) algorithm for matrix-ordered isotonic regression. We demonstrate the efficiency of our IBCR algorithm by using simulation to estimate the relative frequencies of the numbers of level sets of isotonic regressions on certain two-dimensional grids with the matrix order.     

On the convergence of a modified algorithm for the spherical facility location problem

We study the spherical facility location problem which is a more realistic model than the Euclidean facilities location. We present a modified algorithm for this problem, which has the following good properties: (a) It is very easy to initialize the algorithm with an arbitrary point as its starting point; (b) Under suitable assumptions, it is proved that the algorithm globally converges to a global minimizer of the problem.