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检索条件"主题词=Multiobjective genetic algorithm"

共 150 条记录，以下是121-130 订阅

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A Multi-Objective genetic algorithm Framework for Design Space Exploration of Reliable FPGA-based Systems

A Multi-Objective Genetic Algorithm Framework for Design Spa...

引用

2010 IEEE World Congress on Computational Intelligence

作者： Bolchini, Cristiana Lanzi, Pier Luca Miele, Antonio Politecn Milan Dipartimento Elettron Informaz I-20133 Milan Italy

ISBN: (纸本)9781424481262

This paper presents a framework for the design space exploration of reliable FPGA systems based on a multi-objective genetic algorithm (NSGA-II). The framework takes into account several design metrics and outputs a set of Pareto-optimal design solutions. The framework is compared to the multi-objective version of simulated annealing (AMOSA) and it is empirically studied in terms of scalability using three real-world circuits and a set of synthetic problems of different sizes. Our results show that the proposed approach generates a rich set of Pareto-optimal solutions whereas AMOSA tends to find suboptimal solutions. Our empirical scalability analysis shows that, while the problem space is exponential in the number n of functional units constituting the system, the number of required by our framework grows as O (n(3.6)).

关键词： FPGA system Pareto optimal design solution Pareto optimisation design metrix field programmable gate arrays genetic algorithms integrated circuit design multiobjective genetic algorithm real world circuit scalability analysis simulated annealing simulated annealing space exploration design suboptimal solution synthetic problem

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A Methodology for Building Fuzzy Rule-based Systems Integrating Expert and Data Knowledge 3

A Methodology for Building Fuzzy Rule-based Systems Integrat...

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3rd Brazilian Conference on Intelligent Systems (BRACIS)

作者： de Lima, Helano Povoas Camargo, Heloisa de Arruda Fed Univ Sao Carlos UFSCar Dept Comp Sci Sao Carlos SP Brazil

ISBN: (纸本)9781479956180

Historically, since Mamdani proposed his model of fuzzy rule-based system, a lot has changed in the construction process of this type of models. For a long time, the research efforts were directed towards the automatic construction of accurate models starting from data, making fuzzy systems almost mere function approximators. Realizing that this approach escapes from the original concept of fuzzy theory, more recently, researchers attention - focused on the automatic construction of more interpretable models. However, such models, although interpretable, can still not make sense to the expert. This paper proposes an interactive methodology for constructing fuzzy rulebased systems, which aims to integrate the knowledge extracted from experts and induced from data, hoping to contribute to the solution of the mentioned problem.

关键词： fuzzy system construction methodology domain expert multiobjective genetic algorithm

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Multi-Objective genetic algorithm Downlink Resource Allocation in LTE: exploiting the Cell-edge vs. Cell-center trade-off 21

Multi-Objective Genetic Algorithm Downlink Resource Allocati...

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21st IEEE Symposium on Communications and Vehicular Technology in the BeNeLux (IEEE SCVT)

作者： Chiumento, Alessandro Blanch, Carolina Desset, Claude Pollin, Sofie Van der Perre, Liesbet Lauwereins, Rudy Interuniv Microelect Ctr IMEC Vzw Kapeldreef 75 B-3001 Leuven Belgium Katholieke Univ Leuven Dept Elect Engn ESAT Leuven Belgium

ISBN: (纸本)9781479980307

Resource allocation in LTE networks is a challenging problem as multiple users, under a variety of channel conditions, compete for scarce network resources. Moreover, targeting the optimization of the network capacity while still guaranteeing a minimum quality of service complicates the problem considerably. Traditional schedulers can achieve a single trade-off between the conflicting capacity optimization and fairness objectives. This paper presents a novel allocation strategy based on genetic algorithms in which both objectives are simultaneously optimized. This is done by maximizing both the total cell rate and cell-edge rate. The results show that the proposed algorithm offers a range of Pareto optimal solutions that outperform all other reference strategies.

关键词： Long Term Evolution Pareto optimisation genetic algorithms quality of service resource allocation telecommunication scheduling LTE networks Pareto optimal solutions cell rate cell-center trade-off cell-edge multiobjective genetic algorithm network capacity optimization Europe Interference OFDM Phase shift keying Signal to noise ratio Resource utilization Quality of service genetic algorithm (GA) Long Term Evolution Pareto optimisation management of resources signal to noise ratios Phase shift keying Local thermodynamic equilibrium Orthogonal frequency division multiplexing optimal solution Cell rates network capacity

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Evaluated Preference genetic algorithm and Its Engineering Applications

Evaluated Preference Genetic Algorithm and Its Engineering A...

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International Conference on Materials, Mechatronics and Automation (ICMMA 2011)

作者： Liu, Tung-Kuan Chang, Hsin-Yuan Wu, Wen-Ping Chen, Chiu-Hung Ho, Min-Rong Takming Univ Sci & Technol Dept Insurance & Financial Management Taipei Taiwan Takming Univ Sci & Technol Dept Insurance & Finacial Management Taipei Taiwan Takming Univ Sci & Technol Dept Business Adm Taipei Taiwan

ISBN: (纸本)9783037850176

This paper proposes a novel multiobjective genetic algorithm (MOGA), Evaluated Preference genetic algorithm (EPGA), for efficiently solving engineering multiobjective optimization problems. EPGA utilizes a preferred objective vector to perform a fast multiobjective ranking schema within a low computation complexity O(MNlogN) where N is the size of genetic population and M is the number of objectives. For verifying the proposed algorithms, this paper studies two engineering problems in which multiple mutual-conflicted objectives should be considered. According to the experimental results, the proposed EPGA can efficiently explore the Pareto front and provide very good solution capabilities for the engineering optimization problems.

关键词： EPGA multiobjective genetic algorithm engineering optimization

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Multi-Objective Optimal Powertrain Design of Parallel Hybrid Vehicles with Respect to Fuel Consumption and Driving Performance 13

Multi-Objective Optimal Powertrain Design of Parallel Hybrid...

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13th European Control Conference (ECC)

作者： Boehme, Thomas J. Frank, Benjamin Schori, Markus Jeinsch, Torsten IAV Automot Engn Dept Gasoline Engine Syst D-38518 Gifhorn Germany Univ Rostock Inst Automatisierungstech D-18119 Rostock Germany

ISBN: (纸本)9783952426913

In the past decade, Hybrid Electric Vehicles have been demonstrated to significantly reduce the fuel consumption and emissions. However, this capability strongly depends on the sizing of the components and on the quality of the energy management. These challenges require new optimization procedures for a systematical exploration of the design space to find the optimal component sizings and control trajectories. A novel two-layer optimization strategy based on a multi-objective problem formulation is proposed. The first layer consists of a multi-objective genetic algorithm for determining the best system design parameters with respect to fuel consumption and driving performance. The second layer solves a deterministic hybrid optimal control problem (HOCP) to find for each individual of the population pool the optimal continuous and discrete control trajectories for the energy management. The proposed optimization strategy is benchmarked to a one-layer genetic algorithm approach on a parallel hybrid design study.

关键词： genetic algorithms hybrid electric vehicles optimal control power transmission (mechanical) design space deterministic hybrid optimal control problem discrete control trajectories driving performance energy management fuel consumption multiobjective genetic algorithm multiobjective optimal powertrain design multiobjective problem formulation one-layer genetic algorithm approach optimal component sizings parallel hybrid vehicles system design parameters two-layer optimization strategy Energy management Fuels Gears Ice Mechanical power transmission Optimization Torque Hybrid electric vehicles Energy management Fuel consumption genetic algorithm (GA) Powertrain Gears Optimal control Fuels Torque Ice design parameter

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Study on Effect of MOGA with Interactive Island Model using Visualization

Study on Effect of MOGA with Interactive Island Model using ...

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2010 IEEE World Congress on Computational Intelligence

作者： Masafumi, Yamamoto Tomohiro, Yoshikawa Takeshi, Furuhashi Nagoya Univ Dept Comp Sci & Engn Nagoya Aichi 4648601 Japan

ISBN: (纸本)9781424481262

genetic algorithm is one of the most effective optimization algorithms, on which a lot of studies have been reported. Some studies on the application of island model, which is one of the representative methods to keep a diversity of solutions, to Multi-Objective genetic algorithm (MOGA) have been conducted. In MOGA, it is difficult to find the solutions which satisfy all objective functions because of their trade-off. Especially when there are many objective functions, it is obvious that it needs a lot of time to search for effective Pareto solutions and find them. This paper proposes the interactive way of addition and deletion of islands to the original ones based on user's requirements with the visualization of acquired solutions in island model for MOGA. This paper applies the proposed method to Nurse Scheduling Problem (NSP) using the visualization by Principal Component Analysis (PCA). Through the experiment, it is confirmed that an interactive tuning of the weights for the objective functions leaded to the acquisition of better Pareto solutions which a user wants while they are difficult to be acquired by the prepared weights.

关键词： MOGA algorithm Pareto optimisation Pareto solutions data visualisation genetic algorithms interactive island model multiobjective genetic algorithm nurse scheduling problem principal component analysis principal component analysis scheduling visualization

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A multiobjective optimization method for strategic sourcing and inventory replenishment

A multiobjective optimization method for strategic sourcing ...

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IEEE International Conference on Robotics and Automation

作者： Ding, HW Benyoucef, L Xie, XL French Natl Inst Res Comp Sci & Control INRIA F-57000 Metz France

ISBN: (纸本)0780382323

A simulation-based multiobjective optimization method is proposed in this paper for joint decision-making on strategic sourcing and inventory replenishment. More specifically, a multiobjective genetic algorithm is developed to determine the optimal supplier portfolio and inventory control parameters in order to reach best compromise of two conflicting criteria: costs and demand fill-rate. Discrete-event simulation is used to provide faithful evaluation of these two criteria. Numerical results on a variant of a real case study are presented.

关键词： multiobjective genetic algorithm simulation supplier selection inventory replenishment

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Distributed Generation and Network Upgrades Placements for Distribution System Expansion Planning

Distributed Generation and Network Upgrades Placements for D...

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International Conference on Materials Science and Information Technology (MSIT 2011)

作者： Su, Chun-Lien Natl Kaohsiung Marine Univ Dept Marine Engn Kaohsiung Taiwan

ISBN: (纸本)9783037853191

In contrast with the traditional distribution expansion planning approach, significant giants can be realized in terms of outage loss, supply quality, and load carrying capacity from the distribution expansion with a suitable size and siting of distributed generation (DG). However, in order to achieve these planning goals, it often requires network upgrades to accommodate the anticipated power flows in the distribution system with DG. To improve utilization and expansion of distribution system, this paper proposes a methodology to derive the optimal strategy of generation expansion and network upgrades including line capacity upgrade, line switches, and network reconfiguration for multiobjective distribution expansion planning by minimizing network investment cost, feeder losses, customer interruption cost, and voltage drop. A multiobjective genetic algorithm (MOGA) is employed to quantify the effects of DG and network upgrades placements on distribution network under load increases and during distribution outages. To demonstrate the effectiveness of the proposed methodology, an IEEE feeder test system is selected for computer simulation to explore the benefits of DG placement for distribution system utilization and expansion.

关键词： Distributed energy resources System planning multiobjective genetic algorithm

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Multi-objective Optimal Reservoir Operation

Multi-objective Optimal Reservoir Operation

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2010 IEEE World Congress on Computational Intelligence

作者： Scola, Luis A. Neto, Oriane M. Takahashi, Ricardo H. C. Cerqueira, Sergio A. A. G. Univ Fed Sao Joao del Rei Dept Thermal & Fluid Sci Praca Frei Orlando 170 BR-36307352 Sao Joao Del Rei Brazil Univ Fed Minas Gerais Dept Elect Engn BR-31270 Belo Horizonte MG Brazil Univ Fed Minas Gerais Dept Mat BR-31270 Belo Horizonte MG Brazil Fed Univ Sao Joao Dept Mech Engn BR-36307 Orlando Brazil

ISBN: (纸本)9781424481262

The need for the efficient operation of hidropower plants, which provides most of the electrical power consumed in Brazil, is related not only to the issue of energy conservation, but has also been highlighted by the increasing opposition to the construction of new large reservoirs, for ecological and social reasons. In this work, a multi-objective genetic algorithm is applied to problem of the optimization of a single Brazilian hydropower plant, with the objectives of increasing the net energy generation along the year and reducing the peak of demand of non-renewable energy sources. To increase the performance of the algorithm, two new formulations for the problem are proposed, with different ways of dealing with the operational constraints. In comparison with the more traditional approach, this results not only in efficiency gains, but also in an expanded Pareto front, which adds more flexibility to the system, by revealing new possible configurations of system operation.

关键词： Brazilian hydropower plant Pareto front Pareto optimisation electrical power energy conservation genetic algorithms hydroelectric power stations multiobjective genetic algorithm multiobjective optimal reservoir operation net energy generation nonrenewable energy sources reservoirs

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Control of a benchmark structure using GA-optimized fuzzy logic control

Control of a benchmark structure using GA-optimized fuzzy lo...

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作者： Shook, David Adam Texas A&M University

Mitigation of displacement and acceleration responses of a three story benchmark structure excited by seismic motions is pursued in this study. Multiple 20-kN magnetorheological (MR) dampers are installed in the three-story benchmark structure and managed by a global fuzzy logic controller to provide smart damping forces to the benchmark structure. Two configurations of MR damper locations are considered to display multiple-input, single-output and multiple-input, multiple-output control capabilities. Characterization tests of each MR damper are performed in a laboratory to enable the formulation of fuzzy inference models. Prediction of MR damper forces by the fuzzy models shows sufficient agreement with experimental results. A controlled-elitist multi-objective genetic algorithm is utilized to optimize a set of fuzzy logic controllers with concurrent consideration to four structural response metrics. The genetic algorithm is able to identify optimal passive cases for MR damper operation, and then further improve their performance by intelligently modulating the command voltage for concurrent reductions of displacement and acceleration responses. An optimal controller is identified and validated through numerical simulation and fullscale experimentation. Numerical and experimental results show that performance of the controller algorithm is superior to optimal passive cases in 43% of investigated studies. Furthermore, the state-space model of the benchmark structure that is used in numerical simulations has been improved by a modified version of the same genetic algorithm used in development of fuzzy logic controllers. Experimental validation shows that the state-space model optimized by the genetic algorithm provides accurate prediction of response of the benchmark structure to base excitation.

关键词： structural control fuzzy logic control magnetorheological dampers experimental testing neuro-fuzzy modeling system identification acceleration feedback control multiobjective genetic algorithm Book Thesis

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