一种有效的VLSI布图规划算法

提出了一种有效的基于遗传算法的VLSI布图规划方法，在染色体的表达中，对软模块不同形状和硬模块的布局方向进行了编码，并采用了有效的启发式解码方法进行解码，测试结果表明，本算法比已有算法得到了更优的结果。     

Memetic Algorithms for Parallel Code Optimization

Discovering the optimum number of processors and the distribution of data on distributed memory parallel computers for a given algorithm is a demanding task. A memetic algorithm (MA) is proposed here to find the best number of processors and the best data distribution method to be used for each stage of a parallel program. Steady state memetic algorithm is compared with transgenerational memetic algorithm using different crossover operators and hill-climbing methods. A self-adaptive MA is also implemented, based on a multimeme strategy. All the experiments are carried out on computationally intensive, communication intensive, and mixed problem instances. The MA performs successfully for the illustrative problem instances.     

基于遗传算法的VLSI电路划分方法

电路划分是降低超大规模集成电路设计复杂性有效方法，提出了一种基于遗传算法的电路划分算法，该算法不仅适用于电路的二划分和K划分问题，而且可以满足划分对子集的大小和面积等多约束的要求。     

A hardware Memetic accelerator for VLSI circuit partitioning

During the last decade, the complexity and size of circuits have been rapidly increasing, placing a stressing demand on industry for faster and more efficient CAD tools for VLSI circuit layout. One major problem is the computational requirements for optimizing the place and route operations of a VLSI circuit. Thus, this paper investigates the feasibility of using reconfigurable computing platforms to improve the performance of CAD optimization algorithms for the VLSI circuit partitioning problem. The proposed Genetic algorithm architecture achieves up-to 5× speedup over conventional software implementation while maintaining on average 88% solution quality. Furthermore, a reconfigurable computing based Hybrid Memetic algorithm improves upon this solution while using a fraction of the execution time required by the conventional software based approach.     

Memetic search for the max-bisection problem

Given an undirected graph G=(V,E)$G=(V,E)$ with weights on the edges, the max-bisection problem (MBP) is to find a partition of the vertex set V$V$ into two subsets V₁ and V₂ of equal cardinality such that the sum of the weights of the edges crossing V₁ and V₂ is maximized. Relaxing the equal cardinality, constraint leads to the max-cut problem (MCP). In this work, we present a memetic algorithm for MBP which integrates a grouping crossover operator and a tabu search optimization procedure. The proposed crossover operator preserves the largest common vertex groupings with respect to the parent solutions while controlling the distance between the offspring solution and its parents. Extensive experimental studies on 71 well-known G-set benchmark instances demonstrate that our memetic algorithm improves, in many cases, the current best known solutions for both MBP and MCP.     

A hybrid multi-objective PSO algorithm with local search strategy for VLSI partitioning

Very large scale integration （VLSI） circuit par- titioning is an important problem in design automation of VLSI chips and multichip systems; it is an NP-hard combi- national optimization problem. In this paper, an effective hy- brid multi-objective partitioning algorithm, based on discrete particle swarm optimzation （DPSO） with local search strat- egy, called MDPSO-LS, is presented to solve the VLSI two- way partitioning with simultaneous cutsize and circuit delay minimization. Inspired by the physics of genetic algorithm, uniform crossover and random two-point exchange operators are designed to avoid the case of generating infeasible so- lutions. Furthermore, the phenotype sharing function of the objective space is applied to circuit partitioning to obtain a better approximation of a true Pareto front, and the theorem of Markov chains is used to prove global convergence. To improve the ability of local exploration, Fiduccia-Matteyses （FM） strategy is also applied to further improve the cutsize of each particle, and a local search strategy for improving circuit delay objective is also designed. Experiments on IS- CAS89 benchmark circuits show that the proposed algorithm is efficient.     

A hybrid multi-objective PSO algorithm with local search strategy for VLSI partitioning

Very large scale integration (VLSI) circuit partitioning is an important problem in design automation of VLSI chips and multichip systems; it is an NP-hard combinational optimization problem. In this paper, an effective hybrid multi-objective partitioning algorithm, based on discrete particle swarm optimzation (DPSO) with local search strategy, called MDPSO-LS, is presented to solve the VLSI twoway partitioning with simultaneous cutsize and circuit delay minimization. Inspired by the physics of genetic algorithm, uniform crossover and random two-point exchange operators are designed to avoid the case of generating infeasible solutions. Furthermore, the phenotype sharing function of the objective space is applied to circuit partitioning to obtain a better approximation of a true Pareto front, and the theorem of Markov chains is used to prove global convergence. To improve the ability of local exploration, Fiduccia-Matteyses (FM) strategy is also applied to further improve the cutsize of each particle, and a local search strategy for improving circuit delay objective is also designed. Experiments on ISCAS89 benchmark circuits show that the proposed algorithm is efficient.     

基于Memetic框架和改进DFP局部搜索的改进差分进化算法

为提高差分进化(DE)算法对性连续优化问题的求解能力、增强算法的适应性,提出了一种基于局部快速收敛算法的Memetic进化算法。改进了Davidon-Fletcher-Powell方法,得到了具有强搜索能力的局部搜索算法——NDFP。当进化过程中出现具有优秀特质的个体时,NDFP可以使该个体沿着局部最优解的方向快速进化。为综合NDFP和DE的优势,提出局部搜索的执行策略来平衡全局搜索和局部搜索的关系,使得NDFP对DE的优化具有更为广泛的适应性。在CEC2005和CEC2013 Benchmark的53个测试函数上的实验结果表明,同DE/current-to-best/1、SaDE和EPSDE算法相比,NDFP-DE进化算法具有更高的求解精度和稳定性。     

Effective local search algorithms for high school timetabling problems

This paper addresses the high school timetabling problem. The problem consists in building weekly timetables for meetings between classes and teachers with the goal of minimizing violations of specific requirements. In the last decades, several mixed-integer programs have been proposed and tested for this family of problems. However, medium and large size instances are still not effectively solved by these programs using state-of-the-art solvers and the scientific community has given special attention to the devising of alternative soft computing algorithms. In this paper, we propose a soft computing approach based on Iterated Local Search and Variable Neighborhood Search metaheuristic frameworks. Our algorithms incorporate new neighborhood structures and local search routines to perform an effective search. We validated the proposed algorithms on variants of the problem using seven public instances and a new dataset with 34 real-world instances including large cases. The results demonstrate that the proposed algorithms outperform the state-of-the-art approaches in both cases, finding the best solutions in 38 out of the 41 tested instances.     

Memetic algorithms based on local search chains for large scale continuous optimisation problems: MA-SSW-Chains

Nowadays, large scale optimisation problems arise as a very interesting field of research, because they appear in many real-world problems (bio-computing, data mining, etc.). Thus, scalability becomes an essential requirement for modern optimisation algorithms. In a previous work, we presented memetic algorithms based on local search chains. Local search chain concerns the idea that, at one stage, the local search operator may continue the operation of a previous invocation, starting from the final configuration reached by this one. Using this technique, it was presented a memetic algorithm, MA-CMA-Chains, using the CMA-ES algorithm as its local search component. This proposal obtained very good results for continuous optimisation problems, in particular with medium-size (with up to dimension 50). Unfortunately, CMA-ES scalability is restricted by several costly operations, thus MA-CMA-Chains could not be successfully applied to large scale problems. In this article we study the scalability of memetic algorithms based on local search chains, creating memetic algorithms with different local search methods and comparing them, considering both the error values and the processing cost. We also propose a variation of Solis Wets method, that we call Subgrouping Solis Wets algorithm. This local search method explores, at each step of the algorithm, only a random subset of the variables. This subset changes after a certain number of evaluations. Finally, we propose a new memetic algorithm based on local search chains for high dimensionality, MA-SSW-Chains, using the Subgrouping Solis Wets’ algorithm as its local search method. This algorithm is compared with MA-CMA-Chains and different reference algorithms, and it is shown that the proposal is fairly scalable and it is statistically very competitive for high-dimensional problems.     

Time-varying clustering for local lighting and material design

This paper presents an interactive graphics processing unit (GPU)-based relighting system in which local lighting condition, surface materials and viewing direction can all be changed on the fly. To support these changes, we simulate the lighting transportation process at run time, which is normally impractical for interactive use due to its huge computational burden. We greatly alleviate this burden by a hierarchical structure named a transportation tree that clusters similar emitting samples together within a perceptually acceptable error bound. Furthermore, by exploiting the coherence in time as well as in space, we incrementally adjust the clusters rather than computing them from scratch in each frame. With a pre-computed visibility map, we are able to efficiently estimate the indirect illumination in parallel on graphics hardware, by simply summing up the radiance shoots from cluster representatives, plus a small number of operations of merging and splitting on clusters. With relighting based on the time-varying clusters, interactive update of global illumination effects with multi-bounced indirect lighting is demonstrated in applications to material animation and scene decoration. Supported by the National Basic Research Program of China (Grant No. 2009CB320802), the National Natural Science Foundation of China (Grant No. 60833007), the National High-Tech Research & Development Progran of China (Grant No. 2008AA01Z301), and the Research Grant of the University of Macau     

Memetic Algorithm based on Improved Inver-over operator and Lin-Kernighan local search for the Euclidean traveling salesman problem

In this study, an Improved Inver-over operator is proposed to solve the Euclidean traveling salesman problem (TSP) problem. The Improved Inver-over operator is tested on 14 different TSP examples selected from TSPLIB. The application of the Improved Inver-over operator gives much more effective results regarding to the best and average error values than the Basic Inver-over operator. Then an effective Memetic Algorithm based on Improved Inver-over operator and Lin-Kernighan local search is implemented. To speed up the convergence capability of the presented algorithm, a restart technique is employed. We evaluate the proposed algorithm based on standard TSP test problems and show that the proposed algorithm performs better than other Memetic Algorithm in terms of solution quality and computational effort.     

局部搜索自适应核模糊聚类方法

核模糊C-均值聚类KFCM是利用核函数将数据映射到高维空间,通过计算数据点与聚类中心的隶属度对数据进行聚类的算法,拥有高效、快捷的特点而被广泛应用于各领域,然而KFCM算法存在对聚类中心的初始值敏感和不能自适应确定聚类数两个局限性。针对这两个问题,提出一种局部搜索自适应核模糊聚类方法,该方法引入核方法提高数据的可分性,并构造基于核函数的评价函数来确定最优的聚类数目和利用部分样本数据进行局部搜索以寻找初始聚类中心。人工数据和UCI数据集上的实验结果验证了该算法的有效性。     

一种基于本地聚类的P2P资源查找算法*

资源检索是P2P系统研究的热点之一,非结构化P2P资源查找普遍采用泛洪机制。随着查询请求的增加,消息数量呈指数增长,网络拥塞和带宽浪费严重,查询效率得不到保障。针对这一问题,给出了一种基于本地聚类的非结构化P2P资源查找算法。通过对资源特征向量的本地Kmeans聚类和相似链接的建立,有效地提高了资源检索效率,避免了查询消息的扩散对网络带宽的浪费。实验表明,该方法能有效缩短资源的平均检索长度,提高查找成功率。     

Algorithms for clustering clickstream data

Clustering is a classic problem in the machine learning and pattern recognition area, however a few complications arise when we try to transfer proposed solutions in the data stream model. Recently there have been proposed new algorithms for the basic clustering problem for massive data sets that produce an approximate solution using efficiently the memory, which is the most critical resource for streaming computation. In this paper, based on these solutions, we present a new model for clustering clickstream data which applies three different phases in the data processing, and is validated through a set of experiments.     

Algorithms for multi-level graph planarity testing and layout

In this paper we consider the problems of testing a multi-level graph for planarity and laying out or, drawing, a multi-level graph in a clear way. We introduce a new abstraction of a common integer linear programming formulation of the problems that we call a vertex-exchange graph. We demonstrate how this concept can be used to solve the problems by providing clear and simple algorithms for testing a multi-level graph for planarity and laying out a multi-level graph when planar.     

Genetic algorithm with iterated local search for solving a location-routing problem

This paper deals with a location routing problem with multiple capacitated depots and one uncapacitated vehicle per depot. We seek for new methods to make location and routing decisions simultaneously and efficiently. For that purpose, we describe a genetic algorithm (GA) combined with an iterative local search (ILS). The main idea behind our hybridization is to improve the solutions generated by the GA using a ILS to intensify the search space. Numerical experiments show that our hybrid algorithm improves, for all instances, the best known solutions previously obtained by the tabu search heuristic.     

图近似查询算法研究

从数据管理中的近似查询方向,对图数据的近似查询算法进行了研究.依据近似查询的类别,分别介绍了近似查询中的经典算法,并对这些算法进行了详细的分析和讨论,从索引单元以及索引机制比较了各种算法适用的范围以及应用领域.重点阐述和比较了各算法的特点及查询性能,分析了各个算法存在的优势和不足.对近似查询中现有算法的不足及未来的研究方向进行了讨论.     

Categorical data fuzzy clustering: An analysis of local search heuristics

The fuzzy c partition of a set of qualitative data is the problem of selecting the optimal c   centroids that are the most representative of the whole population. Moreover, a set of weights _wij$w_{\mathit{ij}}$ must be determined, describing the fuzzy membership function of pattern i to the cluster represented by centroid j. Both problems are formulated by a single mathematical programming problem, that is an extension of the classic p-median models often used for clustering. The new objective function is neither concave nor convex and the application requires the clustering of many thousands of data, therefore heuristic methods are to be developed to find the best fuzzy partition.