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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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optimization of ultrawideband matched microwave loads by a computational-experimental method

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JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS 1999年第2期44卷 155-161页

作者： Bogdanov, AM Meshchanov, VP Popova, NF

A new efficient method is developed for optimizing various scientific and engineering systems (ESs) intended for arbitrary purposes. The method is based on the optimization algorithm, which uses a simplified mathematical model of the system and results of either a full-scale or a numerical experiment. In the full-scale experiment, the system under consideration is manufactured, and its parameters are then measured. The numerical experiment is performed on the basis of a higher-level model. With the help of this model, the internal profile of a matching element of ultrawideband loads is optimized in the band 0-26 GHz. Test specimens in the coaxial line with a cross-section of 3.50/1.52 mm provide the standing-wave ratio (SWR) that does not exceed 1.18.

关键词： Ultra wideband technologies optimization algorithms MICROWAVE LOAD

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Corona virus optimization (CVO): a novel optimization algorithm inspired from the Corona virus pandemic

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JOURNAL OF SUPERCOMPUTING 2022年第4期78卷 5712-5743页

作者： Salehan, Alireza Deldari, Arash Univ Torbat Heydarieh Dept Comp Engn Torbat Heydarieh Iran

This research introduces a new probabilistic and meta-heuristic optimization approach inspired by the Corona virus pandemic. Corona is an infection that originates from an unknown animal virus, which is of three known types and COVID-19 has been rapidly spreading since late 2019. Based on the SIR model, the virus can easily transmit from one person to several, causing an epidemic over time. Considering the characteristics and behavior of this virus, the current paper presents an optimization algorithm called Corona virus optimization (CVO) which is feasible, effective, and applicable. A set of benchmark functions evaluates the performance of this algorithm for discrete and continuous problems by comparing the results with those of other well-known optimization algorithms. The CVO algorithm aims to find suitable solutions to application problems by solving several continuous mathematical functions as well as three continuous and discrete applications. Experimental results denote that the proposed optimization method has a credible, reasonable, and acceptable performance.

关键词： Corona virus disease Corona virus optimization COVID-19 SIR model optimization algorithms Meta-heuristic algorithms

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Distributed stochastic gradient tracking methods with momentum acceleration for non-convex optimization

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COMPUTATIONAL optimization AND APPLICATIONS 2023年第2期84卷 531-572页

作者： Gao, Juan Liu, Xin-Wei Dai, Yu-Hong Huang, Yakui Gu, Junhua Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Hebei Univ Technol Inst Math Tianjin 300401 Peoples R China Chinese Acad Sci Acad Math & Syst Sci ICMSEC LSEC Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

We consider a distributed non-convex optimization problem of minimizing the sum of all local cost functions over a network of agents. This problem often appears in large-scale distributed machine learning, known as non-convex empirical risk minimization. In this paper, we propose two accelerated algorithms, named DSGT-HB and DSGT-NAG, which combine the distributed stochastic gradient tracking (DSGT) method with momentum accelerated techniques. Under appropriate assumptions, we prove that both algorithms sublinearly converge to a neighborhood of a first-order stationary point of the distributed non-convex optimization. Moreover, we derive the conditions under which DSGT-HB and DSGT-NAG achieve a network-independent linear speedup. Numerical experiments for a distributed non-convex logistic regression problem on real data sets and a deep neural network on the MNIST database show the superiorities of DSGT-HB and DSGT-NAG compared with DSGT.

关键词： Distributed non-convex optimization Machine learning Momentum methods optimization algorithms Convergence rate

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optimization of forging processes using Finite Element simulations

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2010年第5期42卷 797-810页

作者： Bonte, Martijn H. A. Fourment, Lionel Do, Tien-tho van den Boogaard, A. H. Huetink, J. Univ Twente NL-7500 AE Enschede Netherlands Mines ParisTech CEMEF CNRS UMR 7635 F-06904 Sophia Antipolis France

During the last decades, simulation software based on the Finite Element Method (FEM) has significantly contributed to the design of feasible forming processes. Coupling FEM to mathematical optimization algorithms offers a promising opportunity to design optimal metal forming processes rather than just feasible ones. In this paper Sequential Approximate optimization (SAO) for optimizing forging processes is discussed. The algorithm incorporates time-consuming nonlinear FEM simulations. Three variants of the SAO algorithm-which differ by their sequential improvement strategies-have been investigated and compared to other optimization algorithms by application to two forging processes. The other algorithms taken into account are two iterative algorithms (BFGS and SCPIP) and a Metamodel Assisted Evolutionary Strategy (MAES). It is essential for sequential approximate optimization algorithms to implement an improvement strategy that uses as much information obtained during previous iterations as possible. If such a sequential improvement strategy is used, SAO provides a very efficient algorithm to optimize forging processes using time-consuming FEM simulations.

关键词： Sequential Approximate optimization optimization algorithms Forming processes Finite Element simulations Forging

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An adaptive context-aware optimization framework for multimedia adaptation service selection

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NEURAL COMPUTING & APPLICATIONS 2022年第17期34卷 14239-14251页

作者： Laboudi, Zakaria Moudjari, Abdelkader Saighi, Asma Draa, Amer Hadjadj, Selma Univ Oum El Bouaghi Res Lab Comp Sci Complex Syst ReLaCS 2 Oum El Bouaghi Algeria Univ Constantine 2 Dept Fundamental Comp Sci & Applicat Constantine Algeria

In pervasive systems, context is a direct cause to adapt the content of multimedia documents so that they comply, as far as possible, with the current constraints. In this respect, several adaptation approaches have already been proposed, in which adaptation services are often selected from shortlists of services. Practically speaking, adaptation services are provided in various instances and ways, thus making the selection task more difficult. Furthermore, existing approaches for the service selection paradigm cannot be properly applied mainly because constraints on execution time and the availability of computation resources must be considered. To deal with this issue, we propose a framework for adaptive service selection using a bag of metaheuristics ranging from local to global search methods. Depending on the contextual constraints, a sub-bag of algorithms is selected, for which the budget is distributed, using a reinforcement learning mechanism related to their performances. The proposal is validated through a set of experiments and comparisons. The obtained results are satisfactory and encouraging.

关键词： Context-aware pervasive systems Multimedia documents adaptation QoS-aware adaptation service selection optimization algorithms

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optimization of reconfigurable multi-core system-on-chips for multi-standard applications

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2011年第2期15卷 89-98页

作者： Ahmadinia, Ali Fernandez-Canque, Hernando Glasgow Caledonian Univ Sch Engn & Comp Glasgow Lanark Scotland

Today there is an increasing need for high performance chips that can provide very low power consumption, yet can operate over a number of application domains. For example chips operating a number of telecommunication standards depending on which country the device is in or playing a video format depending on different player formats, etc. For this reason, there is an urgent need for a design tool which aids design engineers to design such complex system on chip devices rapidly in order to satisfy the fast time to market demand. This paper presents a new framework to enable the design of flexible systems by incorporating different range of reconfigurability in an embedded platform within an SoC design automatically. The SoC design automation involves identifying the best architectural features for the SoC platform, the configuration setting of reconfigurable cores, the type of interconnection schemes, their associated parameters such as data bandwidth, and placement of embedded cores in the communication infrastructures. For this optimization problem, a two-stage multi-objective optimization algorithm is presented. A multi-standard wireless telecommunication protocol is used to demonstrate our optimized designs in terms of area, power and performance.

关键词： Complex systems Core samples embedded platform Computer chips CARRIER SYSTEM design tools telecommunication standards optimization algorithms system-on-chip core Computer-Aided Design HIGH-PERFORMANCE Communications infrastructure

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A transformer fault diagnosis method based on hybrid improved grey wolf optimization and least squares-support vector machine

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第10期16卷 1950-1963页

作者： Wu, Yuhan Sun, Xiaobo Dai, Baofu Yang, Pengfei Wang, Zhihao Hubei Minzu Univ Coll Informat Engn Enshi Hubei Peoples R China

In recent years, support vector machine has been widely used in the field of transformer fault diagnosis, but its fault diagnosis performance is still to be improved. A power transformer fault diagnosis model based on hybrid improved grey wolf optimization optimized least squares-support vector machine will be proposed as an attempt to improve the efficiency and accuracy of its fault diagnosis performance. First, the hybrid improvement method will be introduced with a view to solving the three problems that grey wolf optimization is troubled with, that is, premature convergence, slow convergence, and low population diversity. Then, six benchmark functions and Wilcoxon rank sum test are used to test hybrid improved grey wolf optimization and the other optimization algorithms, and the results show that hybrid improved grey wolf optimization has better optimization performance and significance. Finally, dissolved gas analysis data will be used for example simulation which will be further examined from three aspects in order to testify the fault diagnosis performance of the proposed method. The results show that the validity of the proposed method is much higher than that of the other models. Therefore, it can be concluded that the proposed method can greatly improve the performance of transformer fault diagnosis, and has great practical significance in engineering.

关键词： power transformer fault diagnosis model fault diagnosis performance hybrid improved grey wolf optimization optimized least squares-support vector machine optimisation support vector machines dissolved gas analysis data transformer fault diagnosis method slow convergence fault diagnosis statistical testing Wilcoxon rank sum test hybrid improvement method optimization algorithms optimization performance low population diversity premature convergence least squares approximations power engineering computing power transformers

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A novel artificial bee colony algorithm for HVAC optimization problems

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HVAC&R RESEARCH 2013年第6期19卷 715-731页

作者： Zhang, Xin Fong, Kwong Fai Yuen, Shiu Yin City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China City Univ Hong Kong Div Bldg Sci & Technol Kowloon Hong Kong Peoples R China

The strategy of simulation-optimization is popular in handling the design and energy management of centralized HVAC systems. An effective optimization algorithm is paramount to obtain the optimal design and operation of HVAC systems. Due to the multi-dimensional and highly constrained nature commonly found in HVAC problems, traditional optimization methods are generally not applicable. Instead, a metaheuristc algorithm, e.g., the genetic algorithm, is a good choice to achieve these purposes. In recent years, the paradigm of the artificial bee colony is emerging in the field of evolutionary computation. The artificial bee colony optimizes the problem through the foraging behavior of honey bees. This article presents a novel approach developed from this paradigm, called the one-position inheritance artificial bee colony algorithm, for handling HVAC system optimization. Through two HVAC example problems, it is found that the one-position inheritance artificial bee colony can more effectively and efficiently search the optimal solutions against the genetic algorithm and original artificial bee colony. This suggests that the one-position inheritance artificial bee colony is a good optimization algorithm to solve other HVAC engineering problems with a multi-dimensional, mixed discrete-continuous, and highly constrained search landscape.

关键词： bee colonies High Voltage Alternating Current Systems artificial bee colony algorithm heating ventilation and air conditioning optimization algorithms Bee Dosage Form Not Applicable Genetic Algorithm (GA) Inheritance

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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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