• 한국통신학회논문지
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• 제32권11A호
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• pp.1079-1085
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• 2007

NSGA (Non-dominated Sorting Algorithm) 는 다목적 최적화 분야에서 널리 사용되고 있는 비지배 정렬 기반의 유전자 알고리즘으로 최적화를 요구하는 분야에서 널리 사용되고 있다. 하지만 연산의 복잡도, 사전 우수해 선별 조건의 미흡함과 공유 변수값 결정의 어려움등이 문제로 제기 되었고, 이러한 단점을 보완한 NSGA-II(Non-dominated Sorting Algorithm-B) 알고리즘이 제안되었다. 그러나 기존의 NSGA-II알고리즘은 다목적 최적화 알고리즘과 동일하게 목적치를 최대화 또는 최소화시키는 방향으로 최적화가 진행되어 선택적인 최적화 수행이 어렵다. 이러한 문제점을 보완하기 위하여 본 논문에서는 NSGA-II알고리즘이 가지는 장점을 바탕으로 설계자의 요구조건에 종속적으로 최적화 과정을 수행할 수 있는 GBNSGA-II (Goal-pareto Based NSGA-II)를 제안하고 기존의 NSGA-II알고리즘과 비교를 통해 성능의 우수성을 검증하였다.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.490-498
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• 2013

This paper discusses the application of evolutionary multi-objective optimization algorithms namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified NSGA-II (MNSGA-II) for solving the Combined Economic Emission Dispatch (CEED) problem with valve-point loading. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a non-smooth optimization problem. IEEE 57-bus and IEEE 118-bus systems are taken to validate its effectiveness of NSGA-II and MNSGA-II. To compare the Pareto-front obtained using NSGA-II and MNSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Furthermore, three different performance metrics such as convergence, diversity and Inverted Generational Distance (IGD) are calculated for evaluating the closeness of obtained Pareto-fronts. Numerical results reveal that MNSGA-II algorithm performs better than NSGA-II algorithm to solve the CEED problem effectively.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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• pp.423-432
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• 2014

Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).

• Journal of Electrical Engineering and Technology
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• 제5권1호
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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.

• 한국유체기계학회 논문집
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• 제17권4호
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• pp.19-29
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• 2014

In addition to the development of micro hydro turbine, the challenge in micro hydro turbine design as sustainable hydro devices is focused on the optimization of turbine runner blade which have decisive effect on the turbine performance to reach higher efficiency. A multi-objective optimization method to optimize the performance of runner blade of propeller turbine for micro turbine has been studied. For the initial design of planar blade cascade, singularity distribution method and the combination of the Bezier curve parametric technology is used. A non-dominated sorting genetic algorithm II(NSGA II) is developed based on the multi-objective optimization design method. The comparision with model test show that the blade charachteristics is optimized by NSGA-II has a good efficiency and load distribution. From model test and scale up calculation, the maximum prototype efficiency of the runner blade reaches as high as 90.87%.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회B
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• pp.2690-2695
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• 2007

This work presents numerical optimization for design of a blade stacking line of a low speed axial flow fan with a fast and elitist Non-Dominated Sorting of Genetic Algorithm (NSGA-II) of multi-objective optimization using three-dimensional Navier-Stokes analysis. Reynolds-averaged Navier-Stokes (RANS) equations with ${\kappa}-{\varepsilon}$ turbulence model are discretized with finite volume approximations and solved on unstructured grids. Regression analysis is performed to get second order polynomial response which is used to generate Pareto optimal front with help of NSGA-II and local search strategy with weighted sum approach to refine the result obtained by NSGA-II to get better Pareto optimal front. Four geometric variables related to spanwise distributions of sweep and lean of blade stacking line are chosen as design variables to find higher performed fan blade. The performance is measured in terms of the objectives; total efficiency, total pressure and torque. Hence the motive of the optimization is to enhance total efficiency and total pressure and to reduce torque.

• 한국정보과학회논문지:소프트웨어및응용
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• 제32권12호
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• pp.1217-1228
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• 2005

최근 들어 DNA 컴퓨팅이 활발하게 연구되면서, DNA 컴퓨팅에서 가장 기본적이고도 중요한 DNA 서열 디자인 문제가 부각되고 있다. 기존의 연구에서 DNA 서열 디자인 문제를 다중목적 최적화 문제로 정의하고, elitist non-dominated sorting genetic algorithm(NSGA-II)를 이용하여 성공적으로 DNA 서열을 디자인하였다. 그런데, NSGA-II는 계산속도가 느리다는 단점이 있어서, 이를 극복하기 위해 본 논문에서는 $\varepsilon$-다중목적함수 진화알고리즘(r-Multiobjective evolutionary algorithm, $\varepsilon$-MOEA)을 DNA 서열 디자인에 이용하였다. 우선, 두 알고리즘의 성능을 보다 자세히 비교하기 위해서 DTLZ2 벤치 마크 문제에 대해서 적용한 결과, 목적함수의 개수가 작은 경우에는 큰 차이가 없으나, 목적함수의 개수가 많을 경우에는 $\varepsilon$-MOEA가 NSGA-II에 대해서 최적해를 찾는 정도(Convergence)와 다양한 해를 찾는 정도 (diversity)에 있어서 각각 $70\%,\;73\%$ 향상된 성능을 보여주었고, 또한 최적해를 찾는 속도도 비약적으로 개선되었다. 이러한 결과를 바탕으로 기존의 DNA 서열 디자인 방법론으로 디자인된 DNA 서열들과 7-순환외판원 문제 해결에 필요한 DNA 서열을 NSGA-II와 $\varepsilon$-MOEA로 재디자인하였다. 대부분의 경우 $\varepsilon$-MOEA가 우수한 결과를 보였고, 특히 7-순환외판원 문제에 대해서 NSGA-II와 비교하여 convergence와 diversity의 측면에서 유사한 결과를 2배 이상 빨리 발견하였고, 동일한 계산 시간을 이용해서는 $22\%$ 정도 보다 다양하게 해를 발견하였으며, $92\%$ 우수한 최적해를 발견하는 것을 확인하였다.

• 한국수자원학회논문집
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• 제40권9호
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• pp.687-696
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• 2007

본 연구는 다목적 유전자알고리즘을 이용하여 Tank 모형의 매개변수를 추정하는데 있어서 선호적순서화(preference ordering)를 적용한 연구로써, 목적함수의 개수가 여러 개인 경우에 발생할 수 있는 파레토최적화의 단점을 해결하기 위한 것이다. 최적화를 위한 목적함수는 모두 4가지를 사용하였으며, 선호적순서화를 통해서 구한 2차 효율성(2nd order efficiency)을 가지면서 정도(degree)가 3인 4개의 해 중에서 1개의 해만을 최우선해로 선정하였다. NSGA-II로 도출된 최우선해의 적합성을 살펴보기 위해서, 자동보정방법인 Powell 방법과 SGA(simple genetic algorithm)를 매개변수 자동보정 방법으로 이용하고 하나의 단일목적함수로 사용해서 최적화한 결과와 비교해보았으며, 비교결과 다목적 유전자 알고리즘을 4개의 목적함수에 모두 적용해서 한번에 도출된 매개변수를 이용한 결과가 보정기간뿐만 아니라 검정기간에 대해서도 비교적 양호한 결과를 나타내는 것으로 나타났다.

• International Journal of Air-Conditioning and Refrigeration
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• 제16권1호
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• pp.1-8
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• 2008

Numerical optimization for design of a blade stacking line of a low speed axial flow fan with a fast and elitist Non-Dominated Sorting of Genetic Algorithm(NSGA-II) of multi-objective optimization using three-dimensional Navier-Stokes analysis is presented in this work. Reynolds-averaged Navier-Stokes(RANS) equations with ${\kappa}-{\varepsilon}$ turbulence model are discretized with finite volume approximations and solved on unstructured grids. Regression analysis is performed to get second order polynomial response which is used to generate Pareto optimal front with help of NSGA-II and local search strategy with weighted sum approach to refine the result obtained by NSGA-II to get better Pareto optimal front. Four geometric variables related to spanwise distributions of sweep and lean of blade stacking line are chosen as design variables to find higher performed fan blade. The performance is measured in terms of the objectives; total efficiency, total pressure and torque. Hence the motive of the optimization is to enhance total efficiency and total pressure and to reduce torque.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.656-657
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• 2015

Recently, Multi-Objective Optimization of design elements is an important issue in building design. Design variables that considering the specificities of the different environments should use the appropriate algorithm on optimization process. The purpose of this study is to compare and analyze the optimal solution using three evolutionary algorithms and energy modeling simulation. This paper consists of three steps: i)Developing three evolutionary algorithm model for optimization of design elements ; ii) Conducting Multi-Objective Optimization based on the developed model ; iii) Conducting comparative analysis of the optimal solution from each of the algorithms. Including Non-dominated Sorted Genetic Algorithm (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO) and Random Search were used for optimization. Each algorithm showed similar range of result data. However, the execution speed of the optimization using the algorithm was shown a difference. NSGA-II showed the fastest execution speed. Moreover, the most optimal solution distribution is derived from NSGA-II.