• Journal of Information Processing Systems
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• 제14권5호
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• pp.1237-1253
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• 2018

Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the music improvisation process and can solve different optimization problems. In order to further improve the performance of the algorithm, this paper proposes an improved harmony search algorithm. Key parameters including harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) are optimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improved method to generate a new crossover solutions rather than the traditional mechanism of improvisation. Four complex function optimization and pressure vessel optimization problems were simulated using the optimization algorithm of standard harmony search algorithm, improved harmony search algorithm and exploratory harmony search algorithm. The simulation results show that the algorithm improves the ability to find global search and evolutionary speed. Optimization effect simulation results are satisfactory.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1993년도 제3회 정기총회 및 추계학술발표회
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• pp.16-16
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• 1993

The efficient use of capital intensive FMS requires determination of effective dispatching priority with which the parts of the selected part types are to be inputed into the system. This paper presents a simulation-optimization approach to find an appropriate dispatching priority. The study is based on a detailed simulator for a module-type commercial FMS, Specifically, after presenting the basic configuration and fundamental control logic of the system together with its main characteristics as a special type of a job shop, an algorithm is presented which combines simulated annealing and simulation to explore a dispatching priority of operations that minimizes the total tardiness, Computational performance of the algorithm shows that good solutions can be obtained within a reasonable amount of computations. The paper also compares the performance of the "optimal" or near optimal dispatching priority generated by the proposed algorithm with those generated by standard dispatching rules such as SPT, EDD and SLACK.

• 한국CDE학회논문집
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• 제17권5호
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• pp.375-386
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• 2012

Most engineering design problems require analyses or simulations to evaluate objective functions. However, a single simulation can take many hours or even days to finish for many real world problems. As a result, design optimization becomes impossible since they require hundreds or thousands of simulation evaluations. The surrogate-based optimization (SBO) strategy became a remedy for such computationally expensive analyses and simulations. A surrogate-based optimization strategy has been developed in this study in order to improve global optimization performance. The strategy is a heuristic algorithm and it exploits not only multiple surrogates, but also multiple optimizers. Multiple optimizations of multiple surrogate models yield multiple candidate design points of optima. During the sequential sampling process, the algorithm ranks candidate design points, selects the points as many as specified, and builds the improved surrogate model. Various mathematical functions with different numbers of design variables are chosen to compare the proposed method with the other most recent algorithm, MSEGO. The proposed method shows superior performance to the other method.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.115-125
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• 2021

Simulation-based hull form optimization is a typical HEB (high-dimensional, expensive computationally, black-box) problem. Conventional optimization algorithms easily fall into the "curse of dimensionality" when dealing with HEB problems. A recently proposed Cross-Entropy (CE) optimization algorithm is an advanced stochastic optimization algorithm based on a probability model, which has the potential to deal with high-dimensional optimization problems. Currently, the CE algorithm is still in the theoretical research stage and rarely applied to actual engineering optimization. One reason is that the Monte Carlo (MC) method is used to estimate the high-dimensional integrals in parameter update, leading to a large sample size. This paper proposes an improved CE algorithm based on quasi-Monte Carlo (QMC) estimation using high-dimensional truncated Sobol subsequence, referred to as the QMC-CE algorithm. The optimization performance of the proposed algorithm is better than that of the original CE algorithm. With a set of identical control parameters, the tests on six standard test functions and a hull form optimization problem show that the proposed algorithm not only has faster convergence but can also apply to complex simulation optimization problems.

• Computers and Concrete
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• 제9권2호
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• pp.119-131
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• 2012

Optimum packing of aggregate is an important aspect of mixture design, since porosity may be reduced and strength improved. It may also cause a reduction in paste content and is thus of economic relevance too. Several mathematic packing models have been developed in the literature for optimization of mixture design. However in this study, numerical simulation will be used as the main tool for this purpose. A basic, simple theoretical model is used for approximate assessment of mixture optimization. Calculation and simulation will start from a bimodal mixture that is based on the mono-sized packing experiences. Tri-modal and multi-sized particle packing will then be discussed to find the optimum mixture. This study will demonstrate that computer simulation is a good alternative for mixture design and optimization when appropriate particle shapes are selected. Although primarily focusing on aggregate, optimization of blends of Portland cement and mineral admixtures could basically be approached in a similar way.

• 한국소음진동공학회논문집
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• 제12권7호
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• pp.542-549
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• 2002

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of given design variables and chance them to get a bettor design. Even though commercial simulation codes are used, the computational time and cost remains non-trivial. Therefore, malty researchers have used a mesa model made by sampling data through simulation. In this paper, four mesa-models for each index group such as ride comfort, derailment Quotient, unloading radio and stability index, are constructed by use of neural network. After these meta models are constructed, multi-objective optimization are achieved by using the differential evolution. This paper shows that the optimization of design variables using the neural network model is very efficient to solve the complex optimization Problem.

• 마이크로전자및패키징학회지
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• 제31권3호
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• pp.50-57
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• 2024

최근 대리 모델에 머신 러닝 기술을 접목하여 복잡한 설계에 대한 최적화를 빠르게 달성하는 방법론이 활발히 연구되고 있다. 훈련된 머신 러닝 대리 모델은 복잡한 유한요소해석 시뮬레이션 대비 컴퓨팅 자원을 적게 소모하면서 동일한 해석 결과를 출력할 수 있다. 또한 훈련된 모델에 최적화를 결합하면 반복 시뮬레이션 대비 더 빠르게 최적의 설계 변수를 도출할 수 있다. 본 연구에서는 패키지 휨을 최소화하는 설계 변수 조합을 효과적으로 탐색하기 위하여 심층신경망과 베이지안 최적화를 적용하였다. 심층신경망 모델은 유한요소해석 시뮬레이션으로 획득한 설계 변수-휨 데이터셋을 바탕으로 훈련하였고, 해당 모델에 베이지안 최적화를 적용하여 휨을 최소화하는 최적의 설계 변수를 탐색하였다. 구축한 심층신경망 및 베이지안 최적화 모델은 실제 시뮬레이션 결과와 99% 이상 일치하는 동시에, 최적 설계 변수 탐색에 소요되는 시간은 15초에 불과하여, 1회의 시뮬레이션과 비교해도 57% 이상 최적화 시간을 단축할 수 있다.

• 한국시뮬레이션학회논문지
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• 제6권1호
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• pp.71-84
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• 1997

For many optimization problems where some of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computer simulation is one of the most effective means of studying such complex systems. Many, if not most, simulation optimization problems have multiple aspects. Historically, multiple aspects have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple aspects. In this paper we propose a MAGA (Multiple Aspects-based Genetic Algorithm) as an algorithm for finding the Pareto optimal set. We demonstrate its ability to find and maintain a diverse "Pareto optimal population" on two problems.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 하계학술대회 논문집 Vol.5 No.1
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• pp.353-356
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• 2004

This paper discribes the analysis of the I-V characteristics of 4H-SiC DiMOSFET with single epi-layer Silicon Carbide has been around for over a century. However, only in the past two to three decades has its semiconducting properties been sufficently studied and applied, especially for high-power and high frequency devices. We present a numerical simulation-based optimization of DiMOSFET using the general-purpose device simulator MINIMIS-NT. For simulation, a loin thick drift layer with doping concentration of $5{\times}10^{15}/cm^3$ was chosen for 1000V blocking voltage design. The simulation results were used to calculate Baliga's figure of Merit (BFOM) as the criterion structure optimization and comparison.

• 한국시뮬레이션학회논문지
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• 제25권1호
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• pp.1-9
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• 2016

본 연구는 공대공 미사일 조종날개의 공력 및 구조를 동시에 고려한 구동력 최소화에 대한 최적화를 수행하였다. 본 연구에서는 조종날개의 공력 및 구조적 특성을 동시에 고려하기 위하여 공력-구조 연계 시뮬레이션을 사용하였으며 공력 및 구조 시뮬레이션에 각각의 전용 소프트웨어를 사용하고자 비정상-약결합 방식 연계기법을 적용하였다. 전역 최적화에는 많은 반복 계산이 필요하므로 빠른 계산을 위하여 수학적 모델링을 이용하였으며 이를 위하여 면 중앙 합성 실험계획법으로 실험점을 선정하였다. 선정된 실험점 및 그에 대한 공력-구조 연계 시뮬레이션 결과를 토대로 2차 다항식 반응면을 생성하였으며 생성된 수학적 모델링을 이용, 유전자 알고리즘 기반 전역최적 설계를 수행하였다. 최적화 목적함수는 마하 0.7 및 마하 2.0 사이의 압력 중심점 이동거리 최소화로 설정하였으며 최적화 결과 압력 중심점 이동거리가 7.5% 감소된 최적형상을 도출하였다.