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Evaluation of a Stepwise, Multiobjective, Multivariable Parameter optimization Method for the APEX Model

JOURNAL OF ENVIRONMENTAL QUALITY 2014年第4期43卷 1381-1391页

作者： Senaviratne, G. M. M. M. Anomaa Udawatta, Ranjith P. Baffaut, Claire Anderson, Stephen H. Univ Missouri Dep Soil Environ & Atmos Sci Columbia MO 65211 USA Univ Missouri Ctr Agroforestry Columbia MO 65211 USA Univ Missouri USDA ARS Cropping Syst & Water Qual Res Unit Columbia MO 65211 USA

Hydrologic models are essential tools for environmental assessment of agricultural nonpoint-source pollution. The automatic calibration of hydrologic models, though efficient, demands significant computational power, limiting their application. The study objective was to develop and evaluate a stepwise, multiobjective, multivariable automatic calibration method for the Agricultural Environmental Policy eXtender (APEX) model for simulating runoff, sediment, total phosphorus (TP), and total nitrogen (TN). The most sensitive parameters were grouped according to the process they primarily affect (runoff, sediment transport, soil biological activity, TP transport, and TN transport) and were optimized separately and consecutively. Two multiobjective functions comprising combinations of coefficient of determination (r(2)), regression slope, and Nash-Sutcliffe coefficient (NSC) and a global objective function, the Generalized Likelihood Uncertainty Estimation, were considered to select the optimal parameter combination. A previously manually calibrated and validated APEX model for three adjacent row-crop field-size watersheds in northeast Missouri was used as the baseline. The greatest improvements in model performance for sediment, TP, and TN, but not for runoff, were found after runoff parameter optimization, indicating that runoff parameter optimization was crucial for good simulation of sediment and nutrients. The r(2) values for sediment, TP, and TN improved from 0.59-0.87 to 0.77-0.94. The NSC values for TP also improved after soil biological activity and TP parameter optimizations, but subsequent optimizations did not improve sediment or TN simulations. The objective function based on r(2), slope, and NSC outperformed the other objective functions. Modelers can benefit from this cost-efficient optimization technique (2570 runs for 23 parameters).

关键词： Models Calibration Colleges & universities Automation Runoff optimization algorithms Business metrics Watersheds Mean square errors Methods

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Ant Colony optimization Model for Tsunamis Evacuation Routes

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COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING 2014年第10期29卷 723-737页

作者： Forcael, Eric Gonzalez, Vicente Orozco, Francisco Vargas, Sergio Pantoja, Alejandro Moscoso, Pablo Univ Bio Bio Dept Civil & Environm Engn Concepcion Chile Univ Auckland Dept Civil & Environm Engn Auckland 1 New Zealand Univ Panamer Sch Engn Guadalajara Jalisco Mexico

Natural disasters such as earthquakes and tsunamis foster the creation of effective evacuation strategies to prevent the loss of human lives. This article proposes a simulation model to find out optimum evacuation routes, during a tsunami using Ant Colony optimization (ACO) algorithms. ACO is a discrete optimization algorithm inspired by the ability of ants to establish the shortest path from their nest to a food source, and vice versa, using pheromones. The validation of the model was carried out through two drills, which were conducted in the coastal town of Penco, Chile. This town was strongly affected by an 8.8 Mw earthquake and tsunami over February 2010. The first drill was held with minimum information, leaving the population to act randomly and intuitively. The second drill was carried out with information provided by the model, inducing people to use the optimized routes generated by the ACO algorithm. The results showed that, in case of an emergency, conventional evacuation routes showed longer escape times compared to those produced by the model developed in this research.

关键词： Model checking shortest path nests ADMINISTRATIVE CONSENT ORDER Escape Routes Simulation models Food sources tsunamis Natural Disasters Earthquakes Ants EVACUATION PLANS Ant colony optimization Evacuation optimization algorithms

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Quasi-Three-Dimensional Aerodynamic Solver for Multidisciplinary Design optimization of Lifting Surfaces

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JOURNAL OF AIRCRAFT 2014年第2期51卷 547-558页

作者： Mariens, J. Elham, A. van Tooren, M. J. L. Delft Univ Technol Flight Performance & Prop Grp Fac Aerosp Engn NL-2629 HS Delft Netherlands

This paper presents the development of a quasi-three-dimensional aerodynamic solver, which provides accurate results for wing drag comparable to the higher-fidelity aerodynamic solvers at significantly lower computational costs. The proposed solver calculates the viscous wing drag using the combination of a two-dimensional airfoil analysis tool with a vortex lattice code. Validation results show that the results of the quasi-three-dimensional solver are in good agreement with higher-fidelity computational fluid dynamics solvers. The quasi-three-dimensional solver is used for a wing shape multidisciplinary design optimization. A multidisciplinary design optimization problem is formulated to design the wing shape of a typical passenger aircraft. The aircraft maximum takeoff weight is considered as the objective function. Two optimization algorithms, a local and a global optimum finder, are implemented in the multidisciplinary design optimization system. The optimization results indicate that the global optimization algorithm shows a slightly greater reduction in maximum takeoff weight. However, finding the global optimum needs about 20 times the computational time of the local optimization algorithm.

关键词： Wings global optimum lift Solvers optimization algorithms computational time multidisciplinary design optimization Optimal design lifting surfaces

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Parameters' fine tuning of differential evolution algorithm

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COMPUTER SYSTEMS SCIENCE AND ENGINEERING 2015年第2期30卷 125-139页

作者： Al-Dabbagh, Rawaa Dawoud Mekhilef, Saad Baba, Mohd Sapiyan Univ Malaya Dept Artificial Intelligence Kuala Lumpur 50603 Selangor Malaysia Univ Malaya Dept Elect Engn Power Elect & Renewable Energy Res Lab PEARL Kuala Lumpur 50603 Selangor Malaysia Gulf Univ Sci & Technol Kuwait Kuwait

Most heuristic search method's performances are dependent on parameter choices. These parameter settings govern how new candidate solutions are generated and then applied by the algorithm. They essentially play a key role in determining the quality of the solution obtained and the efficiency of the search. Their fine-tuning techniques are still an on-going research area. Differential Evolution (DE) algorithm is a very powerful optimization method and has become popular in many fields. Based on the prolonged research work on DE, it is now arguably one of the most outstanding stochastic optimization algorithms for real-parameter optimization. One reason for its popularity is its widely appreciated property of having only a small number of parameters to tune. This paper presents a detailed review of DE parameter tuning with a table compromised a recommended guidelines for these parameters, along with a full description of the basic DE algorithm and its corresponding operators, overlooked by previous studies. It is aimed at practitioners to help them achieve better results when adopting DE as an optimization method for their problems with less time and effort. Moreover, an experimental study has been conducted over fifteen test problems and the results obtained prove the reliability of the setting values.

关键词： optimization algorithms Natural Computing Evolutionary algorithms (EAs) Differential Evolution (DE) Parameter Setting Parameter Tuning Differential Evolution Mutation review

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Active noise control using an adaptive bacterial foraging optimization algorithm

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SIGNAL IMAGE AND VIDEO PROCESSING 2014年第8期8卷 1507-1516页

作者： Gholami-Boroujeny, Shiva Eshghi, Mohammad Shahid Beheshti Univ Elect & Comp Engn Fac Tehran Iran

This paper presents an adaptive bacterial foraging optimization (ABFO) algorithm for an active noise control system. The conventional active noise control (ANC) systems often use the gradient-based filtered-X least mean square algorithms to adapt the coefficients of the adaptive controller. Hence, there is a possibility to converge to local minima. In addition, this class of algorithms needs prior identification of the secondary path. The ABFO algorithm helps the ANC system to prevent falling into local minima. The proposed ANC system is also simpler since it does not need any prior information of the secondary path. Moreover, the adaptive strategy of the algorithm results in improved search performance compared with the basic bacterial foraging optimization algorithm, as well as other conventional algorithms. Experimental studies are performed for nonlinear primary path along with linear and nonlinear secondary path. The results show the effectiveness of the proposed ABFO-based ANC system for different kinds of input noise.

关键词： Active noise control (ANC) Adaptive filter optimization algorithms Bacterial foraging optimization (BFO) Adaptive bacterial foraging optimization (ABFO)

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Dynamic CMOS Load Balancing and Path Oriented in Time optimization algorithms to Minimize Delay Uncertainties from Process Variations

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VLSI DESIGN 2010年第unknown期2010卷 230783-1-230783-13页

作者： Yelamarthi, Kumar Chen, Chien-In Henry Cent Michigan Univ Sch Engn & Technol Mt Pleasant MI 48859 USA Wright State Univ Dept Elect Engn Dayton OH 45435 USA

The complexity of timing optimization of high-performance circuits has been increasing rapidly in proportion to the shrinking CMOS device size and rising magnitude of process variations. Addressing these significant challenges, this paper presents a timing optimization algorithm for CMOS dynamic logic and a Path Oriented IN Time (POINT) optimization flow for mixed-static-dynamic CMOS logic, where a design is partitioned into static and dynamic circuits. Implemented on a 64-b adder and International Symposium on Circuits and Systems (ISCAS) benchmark circuits, the POINT optimization algorithm has shown an average improvement in delay by 38% and delay uncertainty from process variations by 35% in comparison with a state-of-the-art commercial optimization tool.

关键词： Load balancing dynamic circuit process change Byways optimization algorithms Oriented delay

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Turning Functions in Optimal Synthesis of Mechanisms

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JOURNAL OF MECHANICAL DESIGN 2015年第6期137卷 062302-062302页

作者： Nadal, Fernando Cabrera, Juan A. Bataller, Alex Castillo, Juan J. Ortiz, Antonio Univ Malaga Dept Mech Engn E-29071 Malaga Spain

In this paper, we describe the use of turning functions to compare errors between the coupler and the target paths. The main reason to use turning functions is that the measured error does not depend on the mechanism scale or the position and rotation of the fixed link. Therefore, the searching space for the optimization algorithm is reduced. To carry out mechanism synthesis, we use an evolutionary algorithm. The effectiveness of the proposed method has been demonstrated in five synthesis examples.

关键词： Turning algorithms Errors Shapes Design optimization algorithms

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3I Buildings: Intelligent, Interactive and Immersive Buildings

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Procedia Engineering 2015年 123卷 7-14页

作者： António Aguiar Costa Pedro Mira Lopes André Antunes Izunildo Cabral António Grilo Filipe Martins Rodrigues CERIS Instituto Superior Técnico Universidade de Lisboa Portugal Unidemi Faculdade de Ciências e Tecnologia Universida Nova de Lisboa Portugal Siemens Lisboa Portugal

This research presents the architecture of a technology platform capable of integrating different types of data from building sensors and providing an interface to manage and operate facility devices, which is supported by advanced optimization algorithms. This interface is potentiated by a BIM-based interface presenting real-time data of the building. The solution, called 3i buildings - Intelligent, Interactive, and Immersive Buildings, is a tool to monitor and manage smart buildings, as well as optimize users experience, energy consumptions and environment quality. This is achieved by a grid of sensors and devices that continuously gather information (structural conditions of the building, occupancy, comfort of occupants, energy consumptions and CO2, COV's and Humidity levels, etc.), which is processed by predictive models able to learn over time. The 3D representation of the models allows managers to take advantage of the virtual environment, by augmenting the facility model and including information about the facility, making it easier and perceptible to users and owners, helping them to make better decisions. To support our research, the system will be installed in three different environments, Luz's hospital, Lisbon Aquarium and Norte Shopping, to test the solution under different conditions, objectives and users. In the first two cases the objectives are to monitor building air quality, consumptions and occupancy and in the Norte Shopping case the objectives are to monitor people flows, interact with tem and help the response in case of crisis according to the adopted emergency plan. These types of systems might help reducing energy consumptions as well as increasing comfort and satisfaction of occupants, maintaining a constant concentration of CO2 and humidity within the facility. The optimized algorithms will allow the system to learn, predicting and reacting to different conditions, giving a more reliable and smooth response to occupants needs.

关键词： Intelligent Buildings Energy Efficiency optimization algorithms BIM IFC

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Differential simulated annealing: a robust and efficient global optimization algorithm for parameter estimation of biological networks

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MOLECULAR BIOSYSTEMS 2014年第6期10卷 1385-1392页

作者： Dai, Ziwei Lai, Luhua Peking Univ Ctr Quantitat Biol Beijing 100871 Peoples R China Peking Univ Coll Chem & Mol Engn BNLMS State Key Lab Struct Chem Unstable & Stable Speci Beijing 100871 Peoples R China Peking Univ Coll Chem & Mol Engn Peking Tsinghua Ctr Life Sci Beijing 100871 Peoples R China

Ordinary differential equations (ODEs) are widely used to model the dynamic properties of biological networks. Due to the complexity of biological networks and limited quantitative experimental data available, estimating kinetic parameters for these models remains challenging. We present a novel global optimization algorithm, differential simulated annealing (DSA), for estimating kinetic parameters for biological network models robustly and efficiently. DSA was tested on 95 models sizing from a few to several hundreds of parameters from the BioModels database and compared with other five widely used algorithms for parameter estimation, including both deterministic and stochastic optimization algorithms. Our study showed that DSA gave the highest success rate in the whole dataset and performed especially well for large models. Further analysis revealed that DSA outperformed the five algorithms compared in both accuracy and efficiency.

关键词： differential estimating parameters Parameter estimation simulated annealing optimization algorithms Biological Kinetic parameters Global optimization dynamic strain aging dynamic characteristics Differential

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Nelder-Mead Simplex optimization Routine for Large-Scale Problems: A Distributed Memory Implementation

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COMPUTATIONAL ECONOMICS 2014年第4期43卷 447-461页

作者： Klein, Kyle Neira, Julian Univ Calif Santa Barbara Santa Barbara CA 93106 USA

The Nelder-Mead simplex method is an optimization routine that works well with irregular objective functions. For a function of parameters, it compares the objective function at the vertices of a simplex and updates the worst vertex through simplex search steps. However, a standard serial implementation can be prohibitively expensive for optimizations over a large number of parameters. We describe an implementation of the Nelder-Mead method in parallel using a distributed memory. For processors, each processor is assigned vertices at each iteration. Each processor then updates its worst local vertices, communicates the results, and a new simplex is formed with the vertices from all processors. We also describe how the algorithm can be implemented with only two MPI commands. In simulations, our implementation exhibits large speedups and is scalable to large problem sizes.

关键词： Parallel computing optimization algorithms Nelder-Mead

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