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Integration of evolutionary based assimilation into Kalman-type methods for streamflow simulations in ungauged watersheds

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JOURNAL OF HYDROLOGY 2012年 475卷 428-440页

作者： Dumedah, Gift Coulibaly, Paulin Monash Univ Dept Civil Engn Melbourne Vic 3800 Australia McMaster Univ Sch Geog & Earth Sci Dept Civil Engn Hamilton ON L8S 4L8 Canada

Data assimilation (DA) has emerged as a valuable tool for the design and application of streamflow forecasting systems. But DA applications for streamflow simulations in ungauged basins are still very limited primarily because most updated ensemble members are not usually associated with converged state and model parameterizations. Other limitations include the evaluation of massive number of ensemble members, weak/unknown relationships between parameter values and predictors, and the transfer of several members from gauged watersheds to ungauged ones is computationally expensive. But the inherent dynamics of DA to account for uncertainties in model, forcing data, and imperfect observation provide an appealing approach to simulate watershed response in ungauged basins. This study proposes a DA method namely the Pareto-Particle-Ensemble Kalman Filter (ParetoParticleEnKF) to generate and archive a small number of continuously evolved members using multi-objective evolutionary strategy where these members are updated using particle and ensemble Kalman filtering methods. The archived members for gauged watersheds are combined using inverse distance weighting where they are applied to simulate watershed response in ungauged basins. The proposed method is demonstrated by assimilating daily streamflow into the Sacramento Soil Moisture Accounting (SAC-SMA) model for 10 watersheds in southern Ontario, Canada. After successfully transferring ensemble members from gauged watersheds to ungauged ones, the updated ensembles were applied to simulate streamflow for up to 10-days ahead to determine how long into the future would the quality/accuracy of simulations persist before they begin to deteriorate in the ungauged basin. The results show that the designed method can facilitate simulation of accurate streamflows for any time step, and generate accurate simulations for up to 10 days ahead in the ungauged basins. A unique evaluation procedure is the transfer of updated members in

关键词： Data assimilation Ungauged basins Regionalization Ensemble Kalman filter Particle filter multi-objective evolutionary algorithms

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multi-objective evolutionary Optimization for Generating Ensembles of Classifiers in the ROC Space 12

Multi-Objective Evolutionary Optimization for Generating Ens...

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14th International Conference on Genetic and evolutionary Computation Conference (GECCO)

作者： Levesque, Julien-Charles Durand, Audrey Gagne, Christian Sabourin, Robert Univ Laval Dep Genie Elect & Genie Informat Lab Vis & Syst Numer Quebec City PQ Canada

ISBN: (纸本)9781450311779

In this paper, we propose a novel approach for the multi-objective optimization of classifier ensembles in the ROC space. We first evolve a pool of simple classifiers with NSGA-II using values of the ROC curves as the optimization objectives. These simple classifiers are then combined at the decision level using the Iterative Boolean Combination method (IBC). This method produces multiple ensembles of classifiers optimized for various operating conditions. We perform a rigorous series of experiments to demonstrate the properties and behaviour of this approach. This allows us to propose interesting venues for future research on optimizing ensembles of classifiers using multi-objective evolutionary algorithms.

关键词： Machine learning ROC curve multi-objective evolutionary algorithms Ensembles of classifiers Genetic programming

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VMODEX: A novel visualization tool for rapid analysis of heuristic-based multi-objective design space exploration of heterogeneous MPSoC architectures

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SIMULATION MODELLING PRACTICE AND THEORY 2012年 22卷 166-196页

作者： Taghavi, Toktam Pimentel, Andy D. Sabeghi, Mojtaba Univ Amsterdam Inst Informat Comp Syst Architecture Grp Amsterdam Netherlands Delft Univ Technol Comp Engn Lab Delft Netherlands

System-level computer architecture simulations create large volumes of simulation data to explore alternative architectural solutions. Interpreting and drawing conclusions from this amount of simulation results can be extremely cumbersome. In other domains that also struggle with interpreting large volumes of data, such as scientific computing, data visualization is an invaluable tool. Such visualization is often domain specific and has not become widely studied and utilized for evaluating the results of computer architecture simulations. In this paper, we introduce our novel interactive visualization tool, called VMODEX, which is developed to support system-level design space exploration of MPSoC architectures. In our tool, the design space is modeled as a tree in which both the design parameters and criteria are shown in a single view. VMODEX is able to handle large design spaces and allows designers to look at the data from different perspectives and at multiple levels of abstraction. Due to the exponential design space in real problems and multiple criteria to be considered, heuristic searching algorithms are often used to trim down a large design space into a finite set of points and provide the designer a set of tradable solutions with respect to the design criteria. In VMODEX, besides the techniques provided for visualizing the DSE results, additional capabilities are developed to understand the dynamic search behavior of heuristic searching algorithms. (C) 2011 Elsevier B.V. All rights reserved.

关键词： multi-objective design space exploration Visualization multi-objective evolutionary algorithms MPSoC architectures

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A New multi-objective evolutionary Algorithm Based on a Performance Assessment Indicator 12

A New Multi-Objective Evolutionary Algorithm Based on a Perf...

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14th International Conference on Genetic and evolutionary Computation Conference (GECCO)

作者： Rodriguez Villalobos, Cynthia A. Coello Coello, Carlos A. CINVESTAV IPN Dept Comp Sci Mexico City 14000 DF Mexico

ISBN: (纸本)9781450311779

An emerging trend in the design of multi-objective evolutionary algorithms (MOEAs) is to select individuals through the optimization of a quality assessment indicator. However, the most commonly adopted indicator in current use is the hypervolume which becomes very expensive (computationally speaking) as we increase the number of objectives. In this paper, we propose, instead, the use of another indicator called Delta (p). Although the Delta(p) indicator is not Pareto compliant, we show here how it can be incorporated into the selection mechanism of an evolutionary algorithm (for that sake, we adopt differential evolution as our search engine) in order to produce a MOEA. The resulting MOEA (called Delta (p)-Differential Evolution, or DDE) is validated using standard test problems and performance indicators reported in the specialized literature. Our results are compared with respect to those obtained by both a Pareto-based MOEA (NSGA-II) and a hypervolume-based MOEA (SMS-EMOA). Our preliminary results indicate that our proposed approach is competitive with respect to these two MOEAs for continuous problems having two and three objective functions. Additionally, our proposed approach is better than NSGA-II and provides competitive results with respect to SMS-EMOA for continuous many-objective problems. However, in this case, the main advantage of our proposal is that its computational cost is significantly lower than that of SMS-EMOA.

关键词： multi-objective optimization multi-objective evolutionary algorithms performance assessment indicators

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Dynamic economic emission dispatch based on group search optimizer with multiple producers

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ELECTRIC POWER SYSTEMS RESEARCH 2012年 86卷 8-16页

作者： Guo, C. X. Zhan, J. P. Wu, Q. H. Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China Univ Liverpool Dept Elect Engn Liverpool L69 3BX Merseyside England

This paper presents a new method for dynamic economic emission dispatch (DEED) of power systems, using a novel multi-objective evolutionary algorithm, group search optimizer with multiple producers (GSOMP) that includes a constraint handling scheme introduced to deal with complex constraints. The DEED is divided into 24 decomposed DEEDs, which are then solved hour by hour in the time sequence. A technique for order preference similar to an ideal solution (TOPSIS), is then developed to determine the final solution from the Pareto-optimal solutions considering a decision maker's preference. The performance of GSOMP has been evaluated on the DEEDs of the IEEE 30-bus and 118-bus systems, respectively, in comparison with those of multi-objective particle swarm optimizer (MOPSO) and non-dominated sorting genetic algorithm-II (NSGA-II). The simulation results show that DEED is well solved by the proposed method as a set of widely distributed Pareto-optimal solutions can be obtained and that GSOMP has better convergence performance than MOPSO and NSGA-II and consumes much less time than NSGA-II. All the NOx, CO2 and SO2 are integrated into the emission objective function of the DEED, on which the solution obtained can have relatively low emission of each pollutant. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Dynamic economic dispatch Dynamic economic emission dispatch Group search optimizer with multiple producers multi-objective evolutionary algorithms Constraint handling multi-criterion decision making

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On providing quality of service in grid computing through multi-objective swarm-based knowledge acquisition in fuzzy schedulers

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2012年第2期53卷 228-247页

作者： Prado, R. P. Hoffmann, F. Garcia-Galan, S. Munoz Exposito, J. E. Bertram, T. Univ Jaen Telecommun Engn Dept Jaen Spain Univ Dortmund D-44221 Dortmund Germany

Nowadays, grid computing is increasingly showing a service-oriented tendency and as a result, providing quality of service (QoS) has raised as a relevant issue in such highly dynamic and non-dedicated systems. In this sense, the role of scheduling strategies is critical and new proposals able to deal with the inherent uncertainty of the grid state are needed in a way that QoS can be offered. Fuzzy rule-based schedulers are emerging scheduling schemas in grid computing based on the efficient management of grid resources imprecise state and expert knowledge application to achieve an efficient workload distribution. Given the diverse and usually conflicting nature of the scheduling optimization objectives in grids considering both users and administrators requirements, these strategies can benefit from multi-objective strategies in their knowledge acquisition process greatly. This work suggests the QoS provision in the grid scheduling level with fuzzy rule-based schedulers through multi-objective knowledge acquisition considering multiple optimization criteria. With this aim, a novel learning strategy for the evolution of fuzzy rules based on swarm intelligence, Knowledge Acquisition with a Swarm Intelligence Approach (KASIA) is adapted to the multi-objective evolution of an expert grid meta-scheduler founded on Pareto general optimization theory and its performance with respect to a well-known genetic strategy is analyzed. In addition, the fuzzy scheduler with multi-objective learning results are compared to those of classical scheduling strategies in grid computing. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Soft computing Grid scheduling Fuzzy rule-based systems Automatic learning multi-objective evolutionary algorithms

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An Algorithm for Calculating the Hypervolume Contribution of a Set

An Algorithm for Calculating the Hypervolume Contribution of...

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World Automation Congress (WAC)

作者： Zhou, Xiuling Guo, Ping Chen, C. L. Philip Beijing Normal Univ Image Proc & Pattern Recognit Lab Beijing 100875 Peoples R China Beijing Normal Univ Artificial Intelligence Inst Beijing Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Uni Macau Fac Sci & Technol Macau Peoples R China

ISBN: (纸本)9781467344975

The reliability model can be optimized with a multi-objective optimization algorithm, while hypervolume-based multi-objective evolutionary algorithms (MOEAs) have been shown to produce better results for multi-objective problem in practice. When hypervolume is used in some MOEAs as archiving strategy, diversity mechanism or selection criterion to guide the search, it is necessary to determine which subset contributes the least hypervolume contribution. Few algorithms have been designed for this purpose. In this paper a new algorithm based on HSO (hypervolume by slicing objective) is proposed for calculating the exclusive hypervolume contributions of each subset to the whole nondominated set directly for small dimension. The new algorithm is composed of two parts: the algorithm SHSO (set hypervolume contribution by slicing objective) and the algorithm SHSO*. SHSO is used to calculate the exclusive hypervolume contribution of a subset to the whole nondominated set. SHSO* is applied to select the subset which contributes the least hypervolume contribution by repeated application of SHSO. Compared with HSO adopted for calculating the exclusive hypervolume contribution, SHSO* outperforms HSO for all of the test fronts with small dimension.

关键词： Reliability model multi-objective evolutionary algorithms Hypervolume contribution

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Design and techno-economical optimization for stand-alone hybrid power systems with multi-objective evolutionary algorithms

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2007年第3期31卷 315-328页

作者： Shi, Jun-Hai Zhu, Xin-Jian Cao, Guang-Yi Shanghai Jiao Tong Univ Fuel Cell Inst Shanghai 200030 Peoples R China

The optimal design of the hybrid energy system can significantly improve the economical and technical performance of power supply. However, the problem is formidable because of the uncertain renewable energy supplies, the uncertain load demand, the nonlinear characteristics of some components, and the conflicting techno-economical objectives. In this work. the optimal design of the hybrid energy system has been formulated as a multi-objective optimization problem. We optimize the techno-economical performance of the hybrid energy system and analyse the trade-offs between the multi-objectives using multi-objective genetic algorithms. The proposed method is tested on the widely researched hybrid PV-wind power system design problem. The optimization seeks the compromise system configurations with reference to three incommensurable techno-economical criteria, and uses an hourly time-step simulation procedure to determine the design criteria with the weather resources and the load demand for one reference year. The well-known efficient multi-objective genetic algorithm. called NGAS-II (the fast elitist non-dominated sorting genetic algorithm), is applied on this problem. A hybrid PV-wind power system has been designed with this method and several Methods in the literature. The numerical results demonstrate that the proposed method is superior to the other methods. It can handle the optimal design of the hybrid energy system effectively and facilitate the designer with a range of the design solutions and the trade-off information. For this particular application, the hybrid PV-wind power system using more solar panels achieves better technical performance while the one using more wind power is more economical. Copyright (c) 2006 John Wiley & Sons, Ltd.

关键词： hybrid energy systems optimal design multi-objective evolutionary algorithms multiobjective optimization technical and economical optimization hybrid PV-wind systems

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A Framework of Quantum-inspired multi-objective evolutionary algorithms and its Convergence Condition 07

A Framework of Quantum-inspired Multi-Objective Evolutionary...

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Annual Conference of Genetic and evolutionary Computation Conference

作者： Li, Zhiyong Rudolph, Guenter Hunan Univ Sch Comp & Commun Changsha 410082 Hunan Peoples R China Univ Dortmund Fachbereich Informat Lehrstuhl Algorithm Engn D-44221 Dortmund Germany

A general framework of quantum-inspired multi-objective evolutionary algorithms as well as one of its sufficient convergence conditions to Pareto optimal set is proposed.

ISBN: (纸本)9781595936974

A general framework of quantum-inspired multi-objective evolutionary algorithms as well as one of its sufficient convergence conditions to Pareto optimal set is proposed.

关键词： Quantum computing multi-objective evolutionary algorithms Pareto optimal set stochastic convergence

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An Algorithm for Calculating the Hypervolume Contribution of a Set

An Algorithm for Calculating the Hypervolume Contribution of...

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World Automation Congress

作者： Xiuling Zhou Ping Guo C. L. Philip Chen Image Processing and Pattern Recognition Lab Beijing Normal University Beijing 100875 China The Faculty of Science and Technology University of Macau Macau SAR China

ISBN: (纸本)9781467344975

关键词： Keyword - Reliability model multi-objective evolutionary algorithms Hypervolume contribution

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