With the popularity of complex hydrologic models. the time taken to run these models is increasing substantially. Comparing and evaluating the efficacy of different optimizationalgorithms;for calibrating computationa...
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With the popularity of complex hydrologic models. the time taken to run these models is increasing substantially. Comparing and evaluating the efficacy of different optimizationalgorithms;for calibrating computationally intensive hydrologic models is becoming, a nontrivial issue. In this study, live global optimization algorithms (genetic algorithms. shuffled complex evolution, particle swarm optimization, differential evolution, and artificial immune system) were tested for automatic parameter calibration of a complex hydrologic model, Soil and Water Assessment Too] (SWAT), in four watersheds. The results show that genetic algorithms (GA) outperform the other four algorithms given model evaluation numbers larger than 2000. while particle swarm optimization (PSO) call obtain better parameter solutions than other algorithms given fewer number of model runs (less than 2000). Given limited computational time, the PSO algorithm is preferred, while GA should be chosen given plenty of computational resources. When applying GA and PSO for parameter optimization of SWAT, small population Size should be chosen. Copyright (C) 2008 John Wiley & Sons, Ltd.
In this paper, the method of magnetic poles shifting was combined with optimization method to reduce cogging torque in solid-rotor permanent-magnet synchronous motors. Although the finite-element method (FEM) can calc...
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In this paper, the method of magnetic poles shifting was combined with optimization method to reduce cogging torque in solid-rotor permanent-magnet synchronous motors. Although the finite-element method (FEM) can calculate the cogging torque accurately, to find the peak value of cogging torque, the cogging torque for different relative positions between permanent magnets and slots must be calculated;thus, the optimization will take a long time. To reduce optimization time, a novel analytical method was proposed to determine the initial value and feasible range of the shifting angles. Then the optimization method and FEM were used to minimize the cogging torque. Two prototype motors were analyzed and optimized, respectively. It was proved that the cogging torque can be greatly reduced by the proposed method.
The design of printed reflectarray antennas (RAs) could be quite complex and computationally expensive, since the need of providing high performances and satisfying requirements that could be also in contrast each oth...
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ISBN:
(纸本)9781424449682
The design of printed reflectarray antennas (RAs) could be quite complex and computationally expensive, since the need of providing high performances and satisfying requirements that could be also in contrast each other could require the use of a large number of re-radiating advanced element configurations. A possible strategy for the RA design could be therefore of carrying it out adopting an evolutionary optimization tool. In this work, an artificial neural network (ANN) model of the RA single element is presented as convenient interface between antenna design and global optimization algorithms. In order to prove the effectiveness of the model, it will be used in the design of a dual-band dual-layer reflectarray.
The aim of this paper is to present a thorough reassessment of the Snyman-Fatti (SF) Multi-start global Minimization algorithm with Dynamic Search Trajectories, first published twenty years ago. The reassessment is do...
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The aim of this paper is to present a thorough reassessment of the Snyman-Fatti (SF) Multi-start global Minimization algorithm with Dynamic Search Trajectories, first published twenty years ago. The reassessment is done with reference to a slightly modified version of the original method, the essentials of which are summarized here. Results of the performance of the current code on an extensive set of standard test problems commonly in use today, are presented. This allows for a fair assessment to be made of the performance of the SF algorithm relative to that of the popular Differential Evolution (DE) method, for which test results on the same standard set of test problems used here for the SF algorithm, are also given. The tests show that the SF algorithm, that requires relatively few parameter settings, is a reliably robust and competitive method compared to the DE method. The results also indicate that the SF trajectory algorithm is particularly promising to solve minimum potential energy problems to determine the structure of atomic and molecular clusters.
In order to acquire more accurate crop yield information,the global optimization algorithm SCE-UA was used to integrate leaf area index derived from remote sensing with crop growth model EPIC to simulate regional summ...
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In order to acquire more accurate crop yield information,the global optimization algorithm SCE-UA was used to integrate leaf area index derived from remote sensing with crop growth model EPIC to simulate regional summer maize yield and field management information in Huanghuaihai Plain in China. The results showed that the mean relative error of estimated summer maize yield was 4.37% and RMSE was 0.44t/ha. Compared with the actual field observation data,the mean relative error of simulated sowing date,plant density and net nitrogen fertilization application rate was 1.85%,-7.78% and -10.60% respectively. These above simulated results could meet need of accuracy of crop growth simulation and yield estimation at regional scale. It was proved that integrating remotely sensed LAI with EPIC model based on SCE-UA for simulating regional summer maize yield and field management information was feasible and reliable.
A new global optimization algorithm is proposed that solves a class of fractional Problems Management (P). In order to generate a tighter linear relaxations for each subproblem and to ensure convergence to a global op...
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ISBN:
(纸本)9780769537283
A new global optimization algorithm is proposed that solves a class of fractional Problems Management (P). In order to generate a tighter linear relaxations for each subproblem and to ensure convergence to a global optimum. The proposed global optimization algorithm which connects the branch and bound method with the cutting-plane method successfully is convergent to the global minimum through the successive refinement of the linear relaxation of the feasible region of the objective function and the solutions of a series of linear relaxation Problems *** our result is slightly more general.
A global optimization algorithm for generation of the carbon cluster structures was developed based on the parallel fast annealing evolutionary algorithm with Brenner potential. Using the proposed algorithm, the struc...
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A global optimization algorithm for generation of the carbon cluster structures was developed based on the parallel fast annealing evolutionary algorithm with Brenner potential. Using the proposed algorithm, the structures with the lowest energies of the carbon clusters C-N with N from 2 to 71 were successfully obtained. It was found that the geometry is linear for the carbon clusters with N less than 5, mono-ring for the carbon clusters with N = 5-17, and fullerene-like cage or fullerene for the carbon clusters with N greater than 17. Furthermore. the factors affecting the stability of the carbon clusters were analyzed. It was found that apart from the number of the bonds, the symmetry of the structure. and the number of the adjacent pentagons, the curvature of the cage surface and the distribution of the adjacent pentagons are also key factors. (C) 2004 Elsevier B.V. All rights reserved.
This paper focuses on a class of discrete-time interconnected pendulum-like systems. Sufficient conditions for the global convergence of systems with both structured and unstructured uncertainties in its linear part a...
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This paper focuses on a class of discrete-time interconnected pendulum-like systems. Sufficient conditions for the global convergence of systems with both structured and unstructured uncertainties in its linear part are established in terms of linear matrix inequalities (LMIs). In the traditional decentralized control of large scale systems, the effects of interconnections were seldom studied. In this paper, a square matrix specifying the interconnecting structure is introduced in order to discuss the effects of nonlinear interconnections. It is shown that the global convergence of the whole interconnected system can be achieved by designing an appropriate interconnection matrix. In order to solve the nonlinear matrix inequalities (NMIs) arising in the synthesis problem, a global optimization algorithm is presented which can be used to handle a class of NMI problems. An illustrative example is given to demonstrate the applicability and validity of the main results and the presented algorithm.
Aperture patterns play a vital role in coded aperture imaging ( CAI) applications. In recent years, many approaches were presented to design optimum or near-optimum aperture patterns. Uniformly redundant arrays (URAs)...
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Aperture patterns play a vital role in coded aperture imaging ( CAI) applications. In recent years, many approaches were presented to design optimum or near-optimum aperture patterns. Uniformly redundant arrays (URAs) are, undoubtedly, the most successful for constant sidelobe of their periodic autocorrelation function. Unfortunately, the existing methods can only be used to design URAs with a limited number of array sizes and fixed autocorrelation sidelobe-to-peak ratios. In this paper, we present a novel method to design more flexible URAs. Our approach is based on a searching program driven by DIRECT, a global optimization algorithm. We transform the design question to a mathematical model, based on the DIRECT algorithm, which is advantageous for computer implementation. By changing determinative conditions, we obtain two kinds of types of URAs, including the filled URAs which can be constructed by existing methods and the sparse URAs which have never been mentioned by other authors as far as we know. Finally, we carry out an experiment to demonstrate the imaging performance of the sparse URAs.
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