Multimodal optimization: An effective framework for model calibration

Chica, Manuel; Barranquero, José; Kajdanowicz, Tomasz; Damas, Sergio; Cordón, Óscar

doi:10.1016/j.ins.2016.09.048

Cited by 32 publications

(4 citation statements)

References 56 publications

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“…Среди современных зарубежных работ можно отметить исследование, выполненное в Испании [18]. В данной работе приводится подход, заключающийся в мультимодальной оптимизации (вычисления в различных режимах работы оборудования) как основы для калибровки математической модели.…”

Section: эффективная методика настройки математических моделей теплоэunclassified

Effective adjustment technique of mathematical models of thermal power equipment on its actual condition

Kler¹,

Alekseiuk²

2019

PNRPU Bulletin. Electrotechnics, Informational Technologies, Co

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The problems of state estimation of thermal power system operation and identification of mathematical model parameters have not been acceptable solved due to the complexity of objects of studies and their mathematical models, and the lack of effective methods, algorithms and computer programs to solve the required mathematical tasks. The results of solving the indicated problems are of importance in themselves and play a large part in the qualitative solution to thermal power units' control problems, e.g., the tasks of optimal load distribution among the thermal power plants units and optimal control of thermal power units operating conditions. The current thermal power units, such as boiler units and steam turbines, are technical systems with rather complex engineering flow diagrams, diverse elemental composition and operation conditions. Hence, the main instruments to study thermal power equipment are the methods of mathematical modeling and optimization of its schemes and parameters. The paper describes a description an improved technique for identification (adjustment) of mathematical models for current main thermal power equipment. The technique allows us to reveal more effectively the gross errors in measurements of control parameters used to identify the mathematical model of the equipment under study, evaluate the correctness and correct the errors in constructing the mathematical model itself and improve the accuracy of identification. Among other things, the article discusses the issue of assessing the accuracy of identification the parameters of mathematical models of heat and power equipment, depending on the accuracy of measurements of control parameters used to set up the model, as well as on the correctness of the construction of the mathematical model itself and the calculation method used.

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Section: эффективная методика настройки математических моделей теплоэunclassified

Effective adjustment technique of mathematical models of thermal power equipment on its actual condition

Kler¹,

Alekseiuk²

2019

PNRPU Bulletin. Electrotechnics, Informational Technologies, Co

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“…На втором этапе решения задачи идентификации была сформирована оптимизационная задача (8)- (13), в которой количество оптимизируемых параметров составило 110, а суммарное количество ограничений-неравенств -867. В качестве целевой функции выступала сумма квадратов относительных невязок среди всех параметров во всех рассматриваемых режимах работы исследуемого энергоблока (18). Оптимизируемый на первом этапе идентификации вспомогательный коэффициент  был исключен из списка оптимизируемых и зафиксирован на значении 3,90.…”

Section: идентификация параметров математической модели существующегоunclassified

“…Среди современных зарубежных работ можно отметить исследование, выполненное в Испании [18]. В данной работе приводится подход, заключающийся в мультимодальной оптимизации (вычисления в различных режимах работы оборудования) как основа для калибровки математической модели.…”

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Improving the accuracy of identifying mathematical model parameters of the existing thermal power equipment

Kler¹,

Alekseyuk²

2019

Science Bulletin of the NSTU

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“…In [5], the au thors proposed a fram ework, called MOMCA (m ultimodal optimization for model calibration), which is based on niching genetic algorithms. It provides a set of alternative calibration solutions, which is useful to analyse the parameters, model's response, and doing sensitivity analysis.…”

Section: Introductionmentioning

confidence: 99%

Finding Multiple Solutions of Multimodal Optimization Using Spiral Optimization Algorithm with Clustering

Sidarto¹,

Kania²,

Sumarti³

2017

mendel

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Multimodal optimization is one of the interesting problems in optimization which arises frequently in a widerange of engineering and practical applications. The goal of this problem is to find all of optimum solutions in a single run. Some algorithms fail to find all solutions that have been proven their existence analytically. In our paper [1], a method is proposed to find the roots of a system of non-linear equations using a clustering technique that combine with Spiral Optimization algorithm and Sobol sequence of points. An interesting benefit using this method is that the same inputs will give the same results. Most of the time this does not happen in meta-heuristic algorithms using random factors. Now the method is modified to find solutions of multimodal optimization problems. Generally in an optimization problem, the differential form of the objective function is needed. In this paper, the proposed method is to find optimum points of general multimodal functions that its differential form is not required. Several problems with benchmark functions have been examined using our method and they give good result.

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Multimodal optimization: An effective framework for model calibration

Cited by 32 publications

References 56 publications

Effective adjustment technique of mathematical models of thermal power equipment on its actual condition

Effective adjustment technique of mathematical models of thermal power equipment on its actual condition

Improving the accuracy of identifying mathematical model parameters of the existing thermal power equipment

Finding Multiple Solutions of Multimodal Optimization Using Spiral Optimization Algorithm with Clustering

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