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检索条件"主题词=multiobjective optimization problem"

共 83 条记录，以下是61-70 订阅

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DeSpErate: Speeding-up Design Space Exploration by using Predictive Simulation Scheduling

DeSpErate: Speeding-up Design Space Exploration by using Pre...

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Design, Automation and Test in Europe Conference and Exhibition (DATE)

作者： Mariani, Giovanni Palermo, Gianluca Zaccaria, Vittorio Silvano, Cristina Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9783981537024

The design space exploration (DSE) phase is used to tune configurable system parameters and it generally consists of a multiobjective optimization (MOO) problem. It is usually done at pre-design phase and consists of the evaluation of large design spaces where each configuration requires long simulation. Several heuristic techniques have been proposed in the past and the recent trend is reducing the exploration time by using analytic prediction models to approximate the system metrics, effectively pruning sub-optimal configurations from the exploration scope. However, there is still a missing path towards the effective usage of the underlying computing resources used by the DSE process. In this work, we will show that an alternative and almost orthogonal approach-focused on exploiting the available parallelism in terms of computing resources - can be used to better schedule the simulations and to obtain a high speedup with respect to state of the art approaches, without compromising the accuracy of exploration results. Experimental results will be presented by dealing with the DSE problem of a shared memory multi-core system considering a variable number of available parallel resources to support the DSE phase(1).

关键词： integrated circuit design multiprocessing systems optimisation scheduling DSE DeSpErate MOO problem analytic prediction models computing resources configurable system parameters design space exploration heuristic techniques large design spaces multiobjective optimization problem parallel resources pre-design phase predictive simulation scheduling shared memory multicore system state of the art approach sub-optimal configurations system metrics Analytical models Computational modeling optimization Parallel processing Prediction algorithms Predictive models Space exploration

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A Multi-Objective Approach for Community Detection in Complex Network

A Multi-Objective Approach for Community Detection in Comple...

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2010 IEEE World Congress on Computational Intelligence

作者： Shi, Chuan Zhong, Cha Yan, Zhenyu Cai, Yanan Wu, Bin Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China Fair Isaac Corp Dept Res San Rafael CA 94903 USA

ISBN: (纸本)9781424481262

Detecting community structure is crucial for uncovering the links between structures and functions in complex networks. Most contemporary community detection algorithms employ single optimization criteria (e. g., modularity), which may have fundamental disadvantages. This paper considers the community detection process as a Multi-Objective optimization problem (MOP). Correspondingly, a special Multi-Objective Evolutionary Algorithm (MOEA) is designed to solve the MOP and two model selection methods are proposed. The experiments in artificial and real networks show that the multi-objective community detection algorithm is able to discover more accurate community structures.

关键词： complex network complex networks evolutionary computation multiobjective community structure detection algorithm multiobjective evolutionary algorithm multiobjective optimization problem network theory (graphs) optimization criteria two model selection method

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multiobjective Robust Network Design under Uncertain Traffic An approach based on Evolutionary Algorithm 41

MultiObjective Robust Network Design under Uncertain Traffic...

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Latin American Computing Conference (CLEI)

作者： Arteta, Adolfo Pinto-Roa, Diego P. Univ Nacl Asuncion Fac Politecn San Lorenzo Paraguay

ISBN: (纸本)9781467391436

Given the importance and complexity of the problem of robust network design, this work, studies the robust network design subject to guarantee certain level of quality of service, so that the reservation of an adjustable bandwidth for each node, the network is not negatively influenced by traffic from the rest of the network. Therefore a multiobjective Evolutionary Algorithm (MOEA) is proposed to solve and find the robust. network design, inspired by the concept of price of robustness, which simultaneously minimizes the cost of network, inequity traffic and maximizes the trallic service in the worst case scenario. Finally, experimental results show the benefits of the proposed approach to get a set of non-dominated solution on which it can make, a better decision making.

关键词： Robust network design hose model price of robustness fairness multiobjective optimization problem multiobjective Evolutionary Algorithm

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Multi-Objective optimization of Aircraft Networks for Weight, Performance and Survivability 7

Multi-Objective Optimization of Aircraft Networks for Weight...

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7th International Workshop on Reliable Networks Design Modeling (RNDM)

作者： Geyer, Fabien Elefsiniotis, Alexandros Schupke, Dominic Schneele, Stefan Airbus Grp Innovat Dept TX4CP & TX4CW D-81663 Munich Germany

ISBN: (纸本)9781467380515

With a current utilization of around 25% of its total weight, optimization of cabling and harness plays an important role in the design of an aircraft. This is a multi-objective optimization problem as the physical properties of the harness ( e. g., length and weight) as well as the logical properties of the underlying network ( survivability and performance) have to be taken into account. We propose in this paper to investigate this multi-objective optimization problem and provide different algorithms to solve it. We first focus on expressing it as a mixed integer binary linear program. Due to the complexity of solving this class of linear programs, we propose various heuristics for providing a good solution to this problem.

关键词： aircraft integer programming linear programming aircraft design aircraft networks cabling harness length harness physical properties harness weight logical properties mixed integer binary linear program multiobjective optimization problem network performance network survivability Bandwidth Graph theory Heuristic algorithms Network topology optimization Throughput Topology linear programming Aircraft aircraft design Heuristic algorithms Network topology Graph theory Integer Programming Throughput Bandwidth survivability topology Logical property

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On Tradeoffs Between Cross-ISP P2P Traffic and P2P Streaming Performance

On Tradeoffs Between Cross-ISP P2P Traffic and P2P Streaming...

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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Yang, Peng Xu, Lisong Univ Nebraska Dept Comp Sci & Engn Lincoln NE 68588 USA

ISBN: (纸本)9781424456383

Peer-to-peer (P2P) technology greatly scales up traditional Internet streaming service. However, the philosophy that peers help one another distribute video content produces a large amount of cross-ISP P2P traffic. Newly proposed ISP-friendly P2P mechanisms reduce the cross-ISP P2P traffic by using locality-based peer selection methods with which peers tend to connect to other peers in the same ISP domain. However, one unanswered question is that how we can achieve the tradeoffs between the cross-ISP P2P traffic and the P2P streaming performance if there are conflicts between them. In this paper, we propose a rate allocation mechanism for achieving the tradeoffs between the cross-ISP P2P traffic and the P2P streaming performance. We model the tradeoffs as a multiobjective optimization problem and solve it using the Goal Attainment method. The evaluation result shows that our mechanism is able to achieve the tradeoffs between the conflicts and maintain good fairness among peers and streaming overlays.

关键词： ISP domain Internet Internet streaming service P2P streaming performance cross-ISP P2P traffic goal attainment method mathematical programming multiobjective optimization problem peer selection methods peer-to-peer computing peer-to-peer technology rate a

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The Effects of Genetic Encoding on the Realization of Smooth Trajectory for a Helicopter Pilot in flying a Simple Maneuver

The Effects of Genetic Encoding on the Realization of Smooth...

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2010 IEEE World Congress on Computational Intelligence

作者： Orike, Sunny Rivers State Univ Sci & Technol Dept Elect Comp Engn Port Harcourt Nigeria

ISBN: (纸本)9781424481262

The paper aims to investigate the effects of two genetic encoding methods while developing a model helicopter pilot and realizing smooth trajectory in flying a simple maneuver - acceleration/deceleration maneuver. It makes use of both inverse simulation and genetic algorithms to optimize the performance of the model pilot. The overall goal of is to define the maneuver and have the developed pilot find control settings that carry out the given task. Discrete and continuous controls encoding methods were implemented, showing their effects on the development of the pilot in flying the maneuver. The helicopter pilot was formulated as a multi-objective optimization problem with four objectives imposed as penalties. A novel approach proposed in this work, termed maxPenalty, compared and returned the biggest of the four penalties in an attempt to maximize the fitness function, while minimizing the pilot's total workload.

关键词： acceleration control acceleration maneuver aerospace control continuous control encoding method deceleration maneuver discrete control encoding method discrete systems encoding fitness function genetic algorithm genetic algorithms genetic encoding helicopter pilot helicopters inverse problems inverse simulation maxPenalty multiobjective optimization problem smooth trajectory

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multiobjective Virtual Machine Placement in Cloud Environment

Multiobjective Virtual Machine Placement in Cloud Environmen...

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International Conference on Cloud and Ubiquitous Computing and Emerging Technologies (CUBE)

作者： Adamuthe, Amol C. Pandharpatte, Rupali M. Thampi, Gopakumaran T. RIT Dept CSE Islampur MS India TSEC Dept IT Bombay Maharashtra India

ISBN: (纸本)9781479922345;9781479922352

In this paper, the virtual machine placement problem is formulated as a multi-objective optimization problem. The objectives are maximizing profit, maximizing load balancing and minimizing recourse wastage. Results of Genetic algorithms, Non-dominated Sorting Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II are compared with common solution representation, penalty and benefit values. All the three algorithms reported good solutions whereas GA and NSGA are subjected to premature convergence and duplicate solutions. NSGA-II gives good and diversified range of solutions.

关键词： Nondominated Sorting Genetic Algorithm Resource allocation multiobjective optimization problem

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Route Assignment using Multi-Objective Evolutionary Search 11

Route Assignment using Multi-Objective Evolutionary Search

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11th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Chira, Camelia Bazzan, Ana L. C. Tech Univ Cluj Napoca Dept Comp Sci Cluj Napoca 400027 Romania Univ Fed Rio Grande do Sul Inst Informat BR-91501970 Porto Alegre RS Brazil

ISBN: (纸本)9781467382007

Traffic assignment aims to assign trips in a road network such that the travel time of each car is minimized given multiple alternative routes and origin-destination pairs. The number of alternative routes between two points considered in traffic assignment is an important factor in discovering an optimal distribution of cars to routes. This paper investigates the influence of using different number of routes in traffic assignment modeled as a multi-objective optimization problem. An evolutionary algorithm is used to find route solutions for all users in the network considering a different set of best possible routes between origin and destination points. The performance of the multi-objective evolutionary models considered is assessed using several parameter settings for a non-trivial road network. Results show the limitations and advantages of different settings to detect efficient route assignments in terms of network performance.

关键词： evolutionary computation optimisation road traffic alternative route evolutionary algorithm multiobjective evolutionary model multiobjective evolutionary search multiobjective optimization problem network performance optimal distribution origin-destination pair road network route assignment route solution traffic assignment Automobiles Computational modeling optimization Roads Sociology Statistics Traffic assignment evolutionary algorithm quadruple distribution ALTERNATIVE ROUTING road network optimal distribution Sociology Automobiles Cars Road traffic Computational modeling Origin and destination Road

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Density estimation for selecting leaders and mantaining archive in MOPSO

Density estimation for selecting leaders and mantaining arch...

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IEEE Congress on Evolutionary Computation

作者： Hu, Wang Yen, Gary G. Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Sichuan Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

ISBN: (纸本)9781479904549;9781479904532

Leader selection and archive maintenance are the two key issues, which have an important impact on the performance of the obtained approximate Pareto front, to be tackled when extending Single-Objective Particle Swarm optimization to Multi-Objective Particle Swarm optimization (MOPSO). In this paper, a new method of density estimation is proposed for selecting leaders and maintaining archive in MOPSO. The density of a nondominated solution in archive is calculated according to the Parallel Cell Distance after the archive is mapped from Cartesian Coordinate System into Parallel Cell Coordinate System. A new MOPSO is proposed based on this method of density estimation for selecting leaders and maintaining archive to improve the performance of convergence and diversity. The experimental results show that the proposed algorithm is significantly superior to the five chosen state-of-the-art MOPSOs on 12 test problems in term of hypervolume performance indicator.

关键词： particle swarm optimization multiobjective optimization problem evolutionary computation multiobjective particle swarm optimization

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Data Mining Using Parallel Multi-Objective Evolutionary Algorithms on Graphics Hardware

Data Mining Using Parallel Multi-Objective Evolutionary Algo...

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2010 IEEE World Congress on Computational Intelligence

作者： Wong, Man-Leung Cui, Geng Lingnan Univ Dept Comp & Decis Sci Tuen Mun Hong Kong Peoples R China Lingnam Univ Dept Mkt & Int Business Tuen Mun Hong Kong Peoples R China

ISBN: (纸本)9781424481262

An important and challenging data mining application in marketing is to learn models for predicting potential customers who contribute large profit to a company under resource constraints. In this paper, we first formulate this learning problem as a constrained optimization problem and then converse it to an unconstrained Multi-objective optimization problem (MOP). A parallel Multi-Objective Evolutionary Algorithm (MOEA) on consumer-level graphics hardware is used to handle the MOP. We perform experiments on a real-life direct marketing problem to compare the proposed method with the parallel Hybrid Genetic Algorithm, the DMAX approach, and a sequential MOEA. It is observed that the proposed method is much more effective and efficient than the other approaches.

关键词： DMAX approach computer graphic equipment consumer-level graphics hardware coprocessors data mining data mining genetic algorithms marketing multiobjective optimization problem parallel algorithms parallel hybrid genetic algorithm parallel multiobjective evolutionary algorithms potential customer prediction real-life direct marketing problem resource constraints sequential MOEA

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