• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.114-116
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• 2015

This paper presents a Stochastic Time-Cost Tradeoff analysis system (STCT) that identifies optimal construction methods for activities, hence reducing the project completion time and cost simultaneously. It makes use of schedule information obtained from critical path method (CPM), applies alternative construction methods data obtained from estimators to respective activities, computes an optimal set of genetic algorithm (GA) parameters, executes simulation based GA experiments, and identifies near optimal solution(s). A test case verifies the usability of STCT.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 가을 학술발표논문집 Vol.31 No.2 (2)
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• pp.697-699
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• 2004

Stochastic Neighbor Embedding(SNE) is a probabilistic method of mapping high-dimensional data space into a low-dimensional representation with preserving neighbor identities. Even though SNE shows several useful properties, the gradient-based naive SNE algorithm has a critical limitation that it is very slow to converge. To overcome this limitation, faster optimization methods should be considered by using trust region method we call this method fast TR SNE. Moreover, this paper presents a couple of useful optimization methods(i.e. conjugate gradient method and Newton's method) to embody fast SNE algorithm. We compared above three methods and conclude that TR-SNE is the best algorithm among them considering speed and stability. Finally, we show several visualizing experiments of TR-SNE to confirm its stability by experiments.

• 전기전자학회논문지
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• 제22권2호
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• pp.455-459
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• 2018

In this paper, proposed a multi-channel charging control strategy for electric vehicle. This control strategy can adjust the charging power according to the calculated state-of-charge (SOC). Electric vehicle (EV) charging system using Particle Swarm Optimization (PSO) algorithm is proposed. A stochastic optimization algorithm technique such as PSO in the time-of-use (TOU) price used for the energy cost minimization. Simulation results show that the energy cost can be reduced using proposed method.

• 전자공학회논문지C
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• 제35C권3호
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• pp.79-89
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• 1998

The Error Back-Propagation(EBP) algorithm is widely applied to train a multi-layer perceptron, which is a neural network model frequently used to solve complex problems such as pattern recognition, adaptive control, and global optimization. However, the EBP is basically a gradient descent method, which may get stuck in a local minimum, leading to failure in finding the globally optimal solution. Moreover, a multi-layer perceptron suffers from locking a systematic determination of the network structure appropriate for a given problem. It is usually the case to determine the number of hidden nodes by trial and error. In this paper, we propose a new algorithm to efficiently train a multi-layer perceptron. OUr algorithm uses stochastic perturbation in the weight space to effectively escape from local minima in multi-layer perceptron learning. Stochastic perturbation probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the EGP learning gets stuck to it. Addition of new hidden nodes also can be viewed asa special case of stochastic perturbation. Using stochastic perturbation we can solve the local minima problem and the network structure design in a unified way. The results of our experiments with several benchmark test problems including theparity problem, the two-spirals problem, andthe credit-screening data show that our algorithm is very efficient.

• 전산구조공학
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• 제5권2호
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• pp.93-104
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• 1992

최적화기법의 최종목표는 전체최적점(golbal optimum point)을 정확히 그리고 효율적으로 구하는 것이다. 이를 위해 확률론적인 탐색과정을 가지는 Simulated Annealing과 Genetic Algorithm에 의한 최적화과정을 살펴보고, 수학적함수와 트러스, 보 구조물에 대해 최적설계를 행하여 전체최적점에 도달한 신뢰도 및 계산시간을 기준으로 기존의 확정론적 최적화기법에 의한 결과와 그 유용성을 비교검토하였다.

• 한국공작기계학회논문집
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• 제12권3호
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• pp.82-88
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• 2003

SGA(Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, ${\mu}GA$(Micro-Genetic Algorithm) has recently been developed. In this study, ${\mu}GA$ which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of ${\mu}GA$ were compared with those of SGA. Solutions of ${\mu}GA$ for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that ${\mu}GA$ is a suitable and very efficient optimization algorithm for structural design.

• 한국경영과학회지
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• 제4권1호
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• pp.59-64
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• 1979

The application of optimization techniques to the planning of industrial, economic, administrative and military activities with random technological coefficients has been extensively studied in the literature. Stochastic (linear) programs with simple recourse essentially model the allocation of scarce resources under uncertainty with linear penalties associated with shortages or surplus. This work on a problem with a discrete random resource vector, "The allocation of aircraft under uncertain demand" given in (1), is easily and efficiently handled by the application of the recently developed Wets' algorithm (8) for solving stochastic programs with simple recourse, which approves that such class of stochastic problems can be solved with the same efficiency as solving linear programs of the same size. It is known that the algorithm is also applicable to stochastic programs with continuous random demands for their approximate solutions.

• Advances in Computational Design
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• 제1권1호
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• pp.79-86
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• 2016

In this study, the three different meta-heuristics namely the Grey Wolf Optimizer (GWO), Stochastic Fractal Search (SFS), and Adaptive Differential Evolution with Optional External Archive (JADE) algorithms are examined. This study considers optimization of the planer frame to minimize its weight subjected to the strength and displacement constraints as per the American Institute of Steel and Construction - Load and Resistance Factor Design (AISC-LRFD). The GWO algorithm is associated with grey wolves' activities in the social hierarchy. The SFS algorithm works on the natural phenomenon of growth. JADE on the other hand is a powerful self-adaptive version of a differential evolution algorithm. A one-bay ten-story planar steel frame problem is examined in the present work to investigate the design ability of the proposed algorithms. The frame design is produced by optimizing the W-shaped cross sections of beam and column members as per AISC-LRFD standard steel sections. The results of the algorithms are compared. In addition, these results are also mapped with other state-of-art algorithms.

• 한국지능시스템학회논문지
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• 제8권5호
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• pp.98-106
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• 1998

본 논문에서는 신경망의 학습성능을 개선하기 위해 확룰적 근사법과 공액기울기법에 기초를 둔 새로운 학습방법을 제안하였다. 제안된 방법에서는 확률적 근사법과 공액기울기법을 조합 사용한 전역 최적화 기법의 역전파 알고리즘을 적용함으로써 학습성능을 최대한 개선할 수 있도록 하였다. 확률적 근사법은 국소최소점을 벗어나 전역최적점에 치우친 근사점을 결정해 주는 기능을 하도록 하며, 이점을 초기값으로 하여 결정론적 기법의 공액기울기법을 적용함으로써 빠른 수렴속도로 전역최적점으로의 수렴확률을 놓였다. 제안된 방법을 패리티 검사와 패턴 분류에 각각 적용하여 그 타당성과 성능을 확인한 결과 제안된 방법은 초기값을 무작위로 설정하는 기울기하강법에 기초를 둔 기존의 역전파 알고리즘이나 확률적 근사법과 기울기하강법에 기초를 둔 역전파 알고리즘에 비해 최적해로의 수렴 확률과 그 수렴속도가 우수함을 확인할 수 있었다.

• 전기학회논문지P
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• 제66권3호
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• pp.123-128
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• 2017

Electric utilities has been considered the necessity to introduce AM(asset management) of electric power facilities in order to reduce maintenance cost of existing facilities and to maximize profit. In order to make decisions in terms of repairs and replacements for power transformers, not only measuring by counting parts and labor costs, but comprehensive comparison including reliability and cost is needed. Therefore, this study is modeling input cost for power transformer during its entire life and also the life cycle cost (LCC) technique is applied. In particular, this paper presents an application of heuristic harmony search(HS) optimization algorithm to the convergence and the validity of economic life assessment of power transformer from LCC technique. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. The effectiveness of the proposed identification method has been demonstrated through an economic life assessment simulation of power transformer using HS optimization algorithm.