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9th international workshop on Discrete Event Systems

作者： Mao, Jianfeng Cassandras, Christos G. Boston Univ Dept Mfg Engn Brookline MA 02446 USA

ISBN: (纸本)9781424425921

Discrete event systems with real-time constraints involve tasks that must be completed within specified hard deadlines. Guaranteeing such constraints requires a control effort measured through a cost function, giving rise to a class of nonlinear optimization problems. We overview recent developments aimed at solving such problems in an efficient scalable fashion suitable for on-line applications with limited computational resources. We describe how structural properties of the optimal state trajectory in such problems can be exploited and present a Critical Task Decomposition Algorithm (CTDA) when tasks are processed by a single-stage system. When tasks are processed over multiple stages and are subject to end-to-end real-time constraints, a different set of structural properties is exploited leading to a Virtual Deadline Algorithm (VDA).

关键词： discrete event system scalability real-time constraints

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A Particle Swarm optimization Algorithm for Finding DNA Sequence Motifs

A Particle Swarm Optimization Algorithm for Finding DNA Sequ...

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IEEE international Conference on Bioinformatics and Biomedicine

作者： Lei, Chengwei Ruan, Jianhua Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA

ISBN: (纸本)9781424428908

Discovering short DNA motifs from a set of co-regulated genes is an important step towards deciphering the complex gene regulatory networks and understanding gene functions. Despite significant improvement in the last decade, it still remains one of the most challenging problems in both computer science and molecular biology. In this work, we propose a novel motif finding algorithm based on a population-based stochastic optimization technique called Particle Swarm optimization (PSO), which has been shown to be effective in optimizing difficult multidimensional problems in many fields. However PSO has mainly been applied to problems in continuous domains. The motif finding problem, which is essentially a multiple local alignment problem, is discrete, as a slight shift in one sequence completely changes the alignment. Therefore, we propose to use a word dissimilarity graph to remap the neighborhood structure of the solution space, which transforms the motif finding problem into a contiguous integer domain, and propose a modification of the naive PSO algorithm to accommodate integer variables. In order to improve efficiency, we also propose several strategies for escaping from local optima, and determining the termination criteria automatically. Experimental results on simulated challenge problems show that our method is both more efficient and more accurate than several existing algorithms. applications to several sets of real promoter sequences also show that our approach is able to detect known transcription factor binding sites, and outperforms two of the most successful existing algorithms.

关键词： Molecular biology

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Genetic algorithm-based optimization of service composition and deployment 08

Genetic algorithm-based optimization of service composition ...

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international Conference on Pervasive Services, ICPS 2008 - 3rd international workshop on Services Integration in Pervasive Environments, SIPE'08

作者： Vanrompay, Yves Rigole, Peter Berbers, Yolande Department of Computer Science Katholieke Universiteit Leuven Celestijnenlaan 200A 3001 Heverlee Belgium

ISBN: (纸本)9781605582085

Services running on mobile systems must be able to adapt themselves to changing user needs and availability of resources. We propose to use Genetic algorithms to search for the best service variant in the current context. The chosen service composition is then deployed on a set of available nodes in an optimal way. We illustrate that Genetic algorithms provide a scalable and self-organizing solution to service composition and deployment. We argue that the approach meets some main requirements demanded by services running on mobile systems. A motivating scenario is presented in which a distributed server allows users to share content and run applications in mobile ad-hoc networks. Copyright 2008 ACM.

关键词： Genetic algorithms

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Solving constrained optimization via a modified genetic particle swarm optimization 1

Solving constrained optimization via a modified genetic part...

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1st international Conference on Forensic applications and Techniques in Telecommunications, Information, and Multimedia and workshop, e-Forensics 2008

作者： Zhiming, Liu Cheng, Wang Jian, Li Hubei Key Laboratory of Digital Valley Science and Technology Huazhong University of Science and Technology China Hubei University of Education Wuhan Hubei430074 China

ISBN: (纸本)9789639799196

The genetic particle swarm optimization (GPSO) was derived from the original particle swarm optimization (PSO), which is incorporated with the genetic reproduction mechanisms, namely crossover and mutation. Based on which a modified genetic particle swarm optimization (MGPSO) was introduced to solve constrained optimization problems. In which the differential evolution (DE) was incorporated into GPSO to enhance search performance. At each generation GPSO and DE generated a position for each particle, respectively, and the better one was accepted to be a new position for the particle. To compare and ranking the particles, the lexicographic order ranking was introduced. Moreover, DE was incorporated to the original PSO with the same method, which was used to be compared with MGSPO. MGPSO were experimented with wellknown benchmark functions. By comparison with original PSO algorithms and the evolution strategy, the simulation results have shown its robust and consistent effectiveness. © 2008 ICST.

关键词： Constrained optimization

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Generating Routes with Bio-inspired algorithms under Uncertainty

Generating Routes with Bio-inspired Algorithms under Uncerta...

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3rd international workshop on Hybrid Artificial Intelligence Systems

作者： Vaquerizo Garcia, Maria Belen Burgos Univ Languages & Syst Area Burgos Spain

ISBN: (纸本)9783540876557

The planning of an optimal design of routes is a complex problem of optimization and belongs to the type of NP-Hard problems. In this case, to find an exact solution is nonviable, and, therefore, it needs methods that assure the optimal management of the real resources to the design of the new routes under the best criteria about times and costs. This paper proposes the use of heuristic algorithms bio-inspired for the optimization in the design of the routes under diverse restrictions in the collective urban public transport in a town. This is because there are many applications in the transport field where this type of heuristic have proved to be very efficient. Moreover, among the variables that have greatest impact in developing this work, is the passenger demand, seen as uncertain data. For his treatment, it is suggested the use of the Fuzzy Sets Theory. Therefore, the purpose of this study is to establish a model for solving a complex and uncertain problem.

关键词： Collective Urban Public Transport Routes Generator Combinatorial Complexity Ant Colony optimization Fuzzy Demand Triangular Fuzzy Numbers

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Generation of energy-efficient architecture solutions applying GENE_ARCH: An evolution-based generative design system

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ADVANCED ENGINEERING INFORMATICS 2008年第1期22卷 59-70页

作者： Caldas, Luisa Univ Tecn Lisboa Inst Super Tecn Dept Civil Engn & Architecture ICIST Lisbon Portugal

As the field of design automation and generative design systems (GDS) evolve, more emphasis is placed on issues of design evaluation. This paper focus on the presentation of different applications of GENE ARCH, an evolution-based GDS aimed at helping architects to achieve energy-efficient and sustainable architectural solutions. The system applies goal-oriented design, combining a genetic algorithm (GA) as the search engine, with the DOE2. 1E building energy simulation software as the evaluation module. Design evaluation is based on energy spent for heating, cooling, ventilation and artificial lighting in the building, and on sustainability issues like greenhouse gas emissions associated with the embodied energy of construction materials. The GA can work either as a standard GA or as a Pareto GA, for multicriteria search and optimization. In order to provide a broad view of the capabilities of the software, different applications are discussed: (1) standard GA: testing and validating the software;(2) standard GA: incorporation of architecture design intentions, using a building by architect Alvaro Siza;(3) Pareto GA: choice of construction materials, considering cost, building energy use, and embodied energy;(4) Pareto GA: application to Siza's building, considering thermal and lighting behavior separately;(5) standard GA:shape generation with single objective function;(6) Pareto GA: shape generation with multicriteria, evaluation;(7) Pareto GA: application to an urban and housing context. Overall conclusions from the different applications are discussed, as well as current challenges and limitations, and directions for further work. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： genetic algorithms generative design systems Pareto multicriteria optimization design automation sustainable construction bioclimatic architecture energy efficiency embodied energy life-cycle analysis

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Memory-constrained block processing for DSP software optimization

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2008年第2期50卷 163-177页

作者： Ko, Ming-Yung Shen, Chung-Ching Bhattacharyya, Shuvra S. Sandbridge Technol Inc White Plains NY USA Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA

Digital signal processing (DSP) applications involve processing long streams of input data. It is important to take into account this form of processing when implementing embedded software for DSP systems. Task-level vectorization, or block processing, is a useful dataflow graph transformation that can significantly improve execution performance by allowing subsequences of data items to be processed through individual task invocations. In this way, several benefits can be obtained, including reduced context switch overhead, increased memory locality, improved utilization of processor pipelines, and use of more efficient DSP oriented addressing modes. On the other hand, block processing generally results in increased memory requirements since it effectively increases the sizes of the input and output values associated with processing tasks. In this paper, we investigate the memory-performance trade-off associated with block processing. We develop novel block processing algorithms that carefully take into account memory constraints to achieve efficient block processing configurations within given memory space limitations. Our experimental results indicate that these methods derive optimal memory-constrained block processing solutions most of the time. We demonstrate the advantages of our block processing techniques on practical kernel functions and applications in the DSP domain.

关键词： block processing dataflow embedded systems vectorization block diagram compiler activation overhead context switch memory management

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Optimal Random Matchings on Trees and applications

Optimal Random Matchings on Trees and Applications

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11th international workshop on Approximation algorithms for Combinatorial optimization Problems/12th international workshop on Randomization and Computation

作者： Abrahamson, Jeff Csaba, Bela Shokoufandeh, Ali Drexel Univ Dept Comp Sci Philadelphia PA 19104 USA Western Kentucky Univ Dept Math Bowling Green KY USA

ISBN: (纸本)9783540853626

In this paper we will consider tight upper and lower bounds on the weight of the optimal matching for random point sets distributed among the leaves of a tree, as a function of its cardinality. Specifically, given two n sets of points R = {r(1),..., r(n)} and B = {b(1),...,b(n)} distributed uniformly and randomly on the m leaves of lambda-Hierarchically Separated Trees with branching factor b such that each of its leaves is at depth delta, we will prove that the expected weight of optimal matching between R and B is Theta(root nb Sigma(h)(kappa=1) (root b lambda)(kappa)), for h = min(delta, log(b) n). Using a simple embedding algorithm from R-d to HSTs, we are able to reproduce the results concerning the expected optimal transportation cost in [0, 1](d), except for d = 2. We also show that giving random weights to the points does not affect the expected matching weight by more than a constant factor. Finally, we prove upper bounds on several sets for which showing reasonable matching results would previously have been intractable, e.g., the Cantor set, and various fractals.

关键词： Random Matching Hierarchically Separated Trees Supremum Bounds

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A basic toolbox for constrained quadratic 0/1 optimization

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7th international workshop on Experimental algorithms

作者： Buchheim, Christoph Liers, Frauke Oswald, Marcus Univ Cologne Inst Informat Pohligstr 1 D-50969 Cologne Germany Heidelberg Univ Inst Informat D-69210 Heidelberg Germany

ISBN: (纸本)9783540685487

In many practical applications, the task is to optimize a nonlinear function over a well-studied polytope P as, e.g., the matching polytope or the travelling salesman polytope (TSP). In this paper, we focus on quadratic objective functions. Prominent examples are the quadratic assignment and the quadratic knapsack problem;further applications occur in various areas such as production planning or automatic graph drawing. In order to apply branch-and-cut methods for the exact solution of such problems, they have to be linearized. However, the standard linearization usually leads to very weak relaxations. On the other hand, problem-specific polyhedral studies are often time-consuming. Our goal is the design of general separation routines that can replace detailed polyhedral studies of the resulting polytope and that can be used as a black box. As unconstrained binary quadratic optimization is equivalent to the maximum cut problem, knowledge about cut polytopes can be used in our setting. Other separation routines are inspired by the local cuts that have been developed by Applegate, Bixby, Chvatal and Cook for faster solution of large-scale traveling salesman instances. By extensive experiments, we show that both methods can drastically accelerate the solution of constrained quadratic 0/1 problems.

关键词： quadratic programming maximum cut problem local cuts crossing minimization similar subgraphs

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Multi-criteria shortest paths in time-dependent train networks

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7th international workshop on Experimental algorithms

作者： Disser, Yann Mueller-Hannemann, Matthias Schnee, Mathias Tech Univ Darmstadt Dept Comp Sci Hochschulstr 10 D-64289 Darmstadt Germany Univ Halle Wittenberg Dept Comp Sci D-06120 Halle Germany

ISBN: (纸本)9783540685487

We study the problem of finding all Pareto-optimal solutions in a multi-criteria setting of the shortest path problem in time-dependent graphs. This has important applications in timetable information systems for train schedules. We present a new prototype to solve this problem in a fully realistic scenario based on a multi-criteria generalization of Dijkstra's algorithm. As optimization criteria we use travel time and number of train changes, as well as a new criterion "reliability of transfers". The performance of the prototype and various speed-up techniques are analyzed experimentally on a large set of real test instances. In comparison with a base-line implementation, our prototype achieves significant speed-up factors of 20 with respect to the number of label creations and of 138 with respect to label insertions into the priority queue. We also compare our prototype with a time-expanded graph model.

关键词： shortest paths time-dependent graphs multi-criteria optimization speed-up techniques case study

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