• Structural Engineering and Mechanics
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• v.65 no.2
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• pp.173-181
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• 2018

In this paper, finite element (FE) model updating based on multi-objective optimization with the surrogate model for a steel plate girder bridge is investigated. Conventionally, FE model updating for bridge structures uses single-objective optimization with finite element analysis (FEA). In the case of the conventional method, computational burden occurs considerably because a lot of iteration are performed during the updating process. This issue can be addressed by replacing FEA with the surrogate model. The other problem is that the updating result from single-objective optimization depends on the condition of the weighting factors. Previous studies have used the trial-and-error strategy, genetic algorithm, or user's preference to obtain the most preferred model; but it needs considerable computation cost. In this study, the FE model updating method consisting of the surrogate model and multi-objective optimization, which can construct the Pareto-optimal front through a single run without considering the weighting factors, is proposed to overcome the limitations of the single-objective optimization. To verify the proposed method, the results of the proposed method are compared with those of the single-objective optimization. The comparison shows that the updated model from the multi-objective optimization is superior to the result of single-objective optimization in calculation time as well as the relative errors between the updated model and measurement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4357-4378
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• 2017

In cooperative multi-relay networks, the relay nodes which are selected are very important to the system performance. How to choose the best cooperative relay nodes is an optimization problem. In this paper, multi-relay selection schemes which consider either single objective or multi-objective are proposed based on evolutionary algorithms. Firstly, the single objective optimization problems of multi-relay selection considering signal to noise ratio (SNR) or power efficiency maximization are solved based on the quantum bee colony optimization (QBCO). Then the multi-objective optimization problems of multi-relay selection considering SNR maximization and power consumption minimization (two contradictive objectives) or SNR maximization and power efficiency maximization (also two contradictive objectives) are solved based on non-dominated sorting quantum bee colony optimization (NSQBCO), which can obtain the Pareto front solutions considering two contradictive objectives simultaneously. Simulation results show that QBCO based multi-relay selection schemes have the ability to search global optimal solution compared with other multi-relay selection schemes in literature, while NSQBCO based multi-relay selection schemes can obtain the same Pareto front solutions as exhaustive search when the number of relays is not very large. When the number of relays is very large, exhaustive search cannot be used due to complexity but NSQBCO based multi-relay selection schemes can still be used to solve the problems. All simulation results demonstrate the effectiveness of the proposed schemes.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.332-332
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• 2023

농업용 저수지는 농촌유역의 홍수조절에 핵심적인 역할을 하지만 기본적인 기능은 이수 기능이기 때문에 농업용수 수량을 충분하게 확보하면서 동시에 홍수에도 대비할 수 있도록 저수지를 적절히 운영하는 것이 중요하다. 이에 농업용 저수지마다 홍수기 제한수위를 설정하여 홍수조절용량과 농업용수를 적절하게 확보하도록 하고 있으나, 현재 홍수기 제한수위는 저수지별 특성을 고려하지 않고 70% 정도의 저수율 기준이 일괄적으로 적용된 경우가 대부분이며 시기별 농업용수공급 특성을 고려하지 못한다. 이에 본 연구에서는 농업용 저수지의 이수안전도 및 치수안전도를 이용하여 이·치수 기능을 모두 고려한 홍수기 제한수위 설정 방안을 제시하였다. 홍수기 제한수위는 초기 (6/21~7/20), 중기 (7/21~8/20), 후기 (8/21~9/20)에 대해 각각 설정하였고, 오봉저수지와 묘곡저수지에 대해 1973~2022년의 기상자료를 이용하여 이·치수를 모의하였다. 관개기 최저저수량 및 이듬해 4월의 저수량을 기준으로 이수안전도를 산정하고, 재현기간 100년/200년/500년 홍수에 대해 저수지 운영을 모의하여 최고저수량을 기준으로 치수안전도를 산정하였다. 초기/중기/후기 제한수위 조합에 대한 이수안전도 및 치수안전도 값을 좌표공간에 점으로 도시하고 Pareto front를 도시하여 최적의 제한수위 범위를 도출하였다. 분석 결과, 오봉저수지의 이수안전도는 후기 제한수위에 의해 대부분 결정되며, 초기/중기 제한수위는 큰 영향을 주지 못하는 것으로 나타났다. 반면 묘곡저수지의 이수안전도는 초기 제한수위에 의해 크게 영향을 받으며, 그 영향이 이듬해까지 이어지는 것으로 나타났다. 이를 토대로 최적의 제한수위를 도출한 결과, 오봉저수지의 경우 초기 65%, 중기 65~80%, 후기 95% (유효저수량에 대한 비율)로 시기별 수문 운영이 필요한 것으로 나타났고, 묘곡저수지의 경우 초기 100%, 중기 95%, 후기 65~80%로 취수 구조물 또는 비상수문 설치를 통한 방류가 필요한 것으로 나타났다. 본 연구에서 제시한 방법을 통해 저수지별 특성을 고려하여 홍수기 제한수위를 설정함으로써 효율적인 저수지 운영이 가능할 것으로 기대된다.

• Journal of Ocean Engineering and Technology
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• v.28 no.5
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• pp.404-410
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• 2014

The purpose of this study was to find the optimal arrangement of FPSO equipment in a module while considering the economic value and fire risk. We estimated the economic value using the pipe connections and pump installation cost in an HP (high pressure) gas compression module. The equipment risks were also analyzed using fire scenarios based on historical data. To consider the wind effect during a fire accident, fuzzy modeling was applied to improve the accuracy of the analysis. The objective functions consisted of the economic value and fire risk, and the constraints were the equipment maintenance and weight balance of the module. We generated a Pareto-optimal front group using a multi-objective GA (genetic algorithm) and suggested an equipment arrangement method that included the opinions of the designer.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.1
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• pp.124-130
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• 2012

An injection mold cooling circuit for an automotive front bumper was optimally designed in order to simultaneously minimize the average of the standard deviations of the temperature and the difference in mean temperatures of the upper and lower molds for uniform cooling. The temperature distribution for a specified design was evaluated by Moldflow Insight 2010, a commercial injection molding analysis tool. For efficient design, PIAnO (Process Integration, Automation and Optimization), a commercial PIDO tool, was used to integrate and automate injection molding analysis procedure. The weighted-sum method was used to handle the multi-objective optimization problem and PQRSM, a function-based sequential approximate optimizer equipped in PIAnO, to handle numerically noisy responses with respect to the variation of design variables. The optimal average of the standard deviations and difference in mean temperatures were found to be reduced by 9.2% and 56.52%, respectively, compared to the initial ones.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.70-78
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• 2010

Elitist nondominated sorting genetic algorithm (NSGA-II) is adopted and improved for multiobjective optimal reactive power flow (ORPF) problem. Multiobjective ORPF, formulated as a multiobjective mixed integer nonlinear optimization problem, minimizes real power loss and improves voltage profile of power grid by determining reactive power control variables. NSGA-II-based ORPF is tested on standard IEEE 30-bus test system and compared with four other state-of-the-art multiobjective evolutionary algorithms (MOEAs). Pareto front and outer solutions achieved by the five MOEAs are analyzed and compared. NSGA-II obtains the best control strategy for ORPF, but it suffers from the lower convergence speed at the early stage of the optimization. Several problem-specific local search strategies (LSSs) are incorporated into NSGA-II to promote algorithm's exploiting capability and then to speed up its convergence. This enhanced version of NSGA-II (ENSGA) is examined on IEEE 30 system. Experimental results show that the use of LSSs clearly improved the performance of NSGA-II. ENSGA shows the best search efficiency and is proved to be one of the efficient potential candidates in solving reactive power optimization in the real-time operation systems.

• Journal of Magnetics
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• v.21 no.4
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• pp.660-665
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• 2016

The electromagnetic apparatus plays an important role in high power electrical systems. It is of great importance to provide an effective approach for the optimization of the high power electromagnetic apparatus. However, premature convergence and few Pareto solution set of the optimization for electromagnetic apparatus always happen. This paper proposed a modified multi-objective particle swarm optimization algorithm based on the niche sorting strategy. Applying to the modified algorithm, this paper guarantee the better Pareto optimal front with an enhanced distribution. Aiming at shortcomings in the closing bounce and slow breaking velocity of electromagnetic apparatus, the multi-objective optimization model was established on the basis of the traditional optimization. Besides, by means of the improved multi-objective particle swarm optimization algorithm, this paper processed the model and obtained a series of optimized parameters (decision variables). Compared with other different classical algorithms, the modified algorithm has a satisfactory performance in the multi-objective optimization problems in the electromagnetic apparatus.

• ETRI Journal
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• v.44 no.5
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• pp.805-815
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• 2022

The model-based evolutionary algorithms are divided into three groups: estimation of distribution algorithms, inverse modeling, and surrogate modeling. Existing inverse modeling is mainly applied to solve multi-objective optimization problems and is not suitable for many-objective optimization problems. Some inversed-model techniques, such as the inversed-model of multi-objective evolutionary algorithm, constructed from the Pareto front (PF) to the Pareto solution on nondominated solutions using a random grouping method and Gaussian process, were introduced. However, some of the most efficient inverse models might be eliminated during this procedure. Also, there are challenges, such as the presence of many local PFs and developing poor solutions when the population has no evident regularity. This paper proposes inverse modeling using random forest regression and uniform reference points that map all nondominated solutions from the objective space to the decision space to solve many-objective optimization problems. The proposed algorithm is evaluated using the benchmark test suite for evolutionary algorithms. The results show an improvement in diversity and convergence performance (quality indicators).

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.1
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• pp.73-80
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• 2010

The optimal design of water distribution system have started with the least cost design of single objective function using fixed hydraulic variables, eg. fixed water demand and pipe roughness. However, more adequate design is accomplished with considering uncertainties laid on water distribution system such as uncertain future water demands, resulting in successful estimation of real network's behaviors. So, many researchers have suggested a variety of approaches to consider uncertainties in water distribution system using uncertainties quantification methods and the optimal design of multi-objective function is also studied. This paper suggests the new approach of a multi-objective optimization seeking the minimum cost and maximum robustness of the network based on two uncertain variables, nodal demands and pipe roughness uncertainties. Total design procedure consists of two folds: least cost design and final optimal design under uncertainties. The uncertainties of demands and roughness are considered with Latin Hypercube sampling technique with beta probability density functions and multi-objective genetic algorithms (MOGA) is used for the optimization process. The suggested approach is tested in a case study of real network named the New York Tunnels and the applicability of new approach is checked. As the computation time passes, we can check that initial populations, one solution of solutions of multi-objective genetic algorithm, spread to lower right section on the solution space and yield Pareto Optimum solutions building Pareto Front.

• Journal of Computational Design and Engineering
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• v.3 no.3
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• pp.215-225
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• 2016

In recent years, the automotive industry has known a remarkable development in order to satisfy the customer requirements. In this paper, we will study one of the components of the automotive which is the twist beam. The study is focused on the multicriteria design of the automotive twist beam undergoing linear elastic deformation (Hooke's law). Indeed, for the design of this automotive part, there are some criteria to be considered as the rigidity (stiffness) and the resistance to fatigue. Those two criteria are known to be conflicting, therefore, our aim is to identify the Pareto front of this problem. To do this, we used a Normal Boundary Intersection (NBI) algorithm coupling with a radial basis function (RBF) metamodel in order to reduce the high calculation time needed for solving the multicriteria design problem. Otherwise, we used the free form deformation (FFD) technique for the generation of the 3D shapes of the automotive part studied during the optimization process.