• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.204-206
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• 2007

In this study, we introduce the optimization method of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation, The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods. Particularly, in parameter identification, we use the UNDX operator which uses multiple parents and generate offsprings around the geographic center off mass of these parents.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.187-188
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• 2011

This paper proposes the hybrid Complex-PSO algorithm based on the complex search method and particle swarm optimization (PSO) for unconstrained optimization. This hybridization intends to produce faster and more accurate convergence to the optimum value. These hybrid will concentrate on determining the dynamic load model parameters, the ZIP model and induction motor model parameters. Measurement-based parameter estimation, which employs measurement data to derive load model parameters, is used. The theoretical foundation of the measurement-based approach is system identification. The main objective of this paper is to demonstrate how the standard particle swarm optimization and complex method can be improved through hybridization of the two methods and the results will be compared with that of their original forms.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.10
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• pp.381-386
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• 2016

An optimization algorithm is developed based on a simulation case of a single family house model equipped with PV arrays. To increase the nationwide use of PV power generation facilities, a market-competitive electricity price needs to be introduced, which is determined based on the time of use. In this study, quadratic programming optimization was applied to minimize the electricity bill while maintaining the indoor temperature within allowable error bounds. For optimization, it is assumed that the weather and electricity demand are predicted. An EnergyPlus-based house model was approximated by using an equivalent RC circuit model for application as a linear constraint to the optimization. Based on the RC model, model predictive control was applied to the management of the cooling load and electricity for the first week of August. The result shows that more than 25% of electricity consumed for cooling can be saved by allowing excursions of temperature error within an affordable range. In addition, profit can be made by reselling electricity to the main grid energy supplier during peak hours.

• Structural Engineering and Mechanics
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• v.66 no.2
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• pp.173-183
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• 2018

This paper proposes a developed optimization model for steel frames with semi-rigid beam-to-column connections and fixed bases using teaching-learning-based optimization (TLBO) and genetic algorithm (GA) techniques. This method uses rotational deformations of frame members ends as an optimization variable to simultaneously obtain the optimum cross-sections and the most suitable beam-to-column connection type. The total cost of members plus connections cost of the frame are minimized. Frye and Morris (1975) polynomial model is used for modeling nonlinearity of semi-rigid connections, and the $P-{\Delta}$ effect and geometric nonlinearity are considered through a stepped analysis process. The stress and displacement constraints of AISC-LRFD (2016) specifications, along with size fitting constraints, are considered in the design procedure. The developed model is applied to three benchmark steel frames, and the results are compared with previous literature results. The comparisons show that developed model using both LTBO and GA achieves better results than previous approaches in the literature.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.240-245
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• 1990

A on-line optimization scheme based on model in a binary distillation process is proposed. A reduced-order model utilized the concept of collocation is used as a process model and the recursive prediction error method is employed to identify the reduced-order model. The concentrations of end products are controlled by nonlinear adaptive predictive control algorithm. The objective function is constructed to find optimum operate condition for saving utility cost. The proposed optimization is scheme is tested through simulation studies in 13-staged water-methanol distillation column.

• Structural Engineering and Mechanics
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• v.90 no.2
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• pp.189-208
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• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.1972-1986
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• 2017

With the advent of the knowledge economy, science and technology resources have played an important role in economic competition, and their optimal allocation has been regarded as very important across the world. Thus, allocation optimization research for regional science and technology resources is significant for accelerating the reform of regional science and technology systems. Regional science and technology resource allocation optimization is modeled as a double-layer optimization model: the entire system is characterized by top-layer optimization, whereas the subsystems are characterized by bottom-layer optimization. To efficaciously solve this optimization problem, we propose a mixed search method based on the orthogonal genetic algorithm and sensitivity analysis. This novel method adopts the integrated modeling concept with a combination of the knowledge model and heuristic search model, on the basis of the heuristic search model, and simultaneously highlights the effect of the knowledge model. To compare the performance of different methods, five methods and two channels were used to address an application example. Both the optimized results and simulation time of the proposed method outperformed those of the other methods. The application of the proposed method to solve the problem of entire system optimization is feasible, correct, and effective.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.3
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• pp.260-268
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• 2013

Based on classical Berotti discrete iron loss calculation model, the iron loss analysis mathematical model of alternator was proposed in this paper. Considering characteristics of high speed and changing frequency of the alternator, Maxwell 3-D model was built to analyze iron loss corresponding to each running speed in alternator. Based on iron loss model of alternator at rated speed, the rotor claw pole size was made an optimization design. The optimization results showed that alternator's output performance had been improved. A new idea was explored in size optimization design of claw pole alternator.

• Journal of Ocean Engineering and Technology
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• v.35 no.2
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• pp.131-140
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• 2021

The paper deals with comparative study of various surrogate models based approximate optimization in the structural design of the passive type deck support frame under design load conditions. The passive type deck support frame was devised to facilitate both transportation and installation of 20,000 ton class topside. Structural analysis was performed using the finite element method to evaluate the strength performance of the passive type deck support frame in its initial design stage. In the structural analysis, the strength performances were evaluated for various design load conditions. The optimum design problem based on surrogate model was formulated such that thickness sizing variables of main structure members were determined by minimizing the weight of the passive type deck support frame subject to the strength performance constraints. The surrogate models used in the approximate optimization were response surface method, Kriging model, and Chebyshev orthogonal polynomials. In the context of numerical performances, the solution results from approximate optimization were compared to actual non-approximate optimization. The response surface method among the surrogate models used in the approximate optimization showed the most appropriate optimum design results for the structure design of the passive type deck support frame.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4181-4194
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• 2022

Main steam safety valves are commonly used in nuclear power plants to provide final protections from overpressure events. Blowdown and dynamic stability are two critical characteristics of safety valves. However, due to the parameter sensitivity and multi-parameter features of safety valves, using traditional method to design and/or optimize them is generally difficult and/or inefficient. To overcome these problems, a surrogate model-based valve design optimization is carried out in this study, of particular interest are methods of valve surrogate modeling, valve parameters global sensitivity analysis and valve performance optimization. To construct the surrogate model, Design of Experiments (DoE) and Computational Fluid Dynamics (CFD) simulations of the safety valve were performed successively, thereby an ensemble surrogate model (E-AHF) was built for valve blowdown and stability predictions. With the developed E-AHF model, global sensitivity analysis (GSA) on the valve parameters was performed, thereby five primary parameters that affect valve performance were identified. Finally, the k-sigma method is used to conduct the robust optimization on the valve. After optimization, the valve remains stable, the minimum blowdown of the safety valve is reduced greatly from 13.30% to 2.70%, and the corresponding variance is reduced from 1.04 to 0.65 as well, confirming the feasibility and effectiveness of the optimization method proposed in this paper.