• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1999년도 추계학술대회 논문집
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• pp.75-80
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• 1999

This paper presents a new method to find a optimal solution for stochastic system. This method uses Genetic Algorithm(GA) and simulation. GA is used to search for new alternative and simulation is used to evaluate alternative. The stochastic system has one or more random variables as inputs. Random inputs lead to random outputs. Since the outputs are random, they can be considered only as estimates of the true characteristics of they system. These estimates could greatly differ from the corresponding real characteristics for the system. We need multiple replications to get reliable information on the system. And we have to analyze output data to get a optimal solution. It requires too much computation to be practical. We address the problem of reducing computation. The procedure on this paper use GA character, an iterative process, to reduce the number of replications. The same chromosomes could exit in post and present generation. Computation can be reduced by using the information of the same chromosomes which exist in post and present current generation.

• 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.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.171-171
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• 2020

Decision making strategies should consider both adaptiveness and robustness in order to deal with two main characteristics of climate change: non-stationarity and deep uncertainty. Especially, robust strategies are different from traditional optimal strategies in the sense that they are satisfactory over a wider range of uncertainty and may act as a key when confronting climate change. In this study, a new framework named Robust Stochastic Dynamic Programming (R-SDP) is proposed, which couples previously developed robust optimization (RO) into the objective function and constraint of SDP. Two main approaches of RO, feasibility robustness and solution robustness, are considered in the optimization algorithm and consequently, three models to be tested are developed: conventional-SDP (CSDP), R-SDP-Feasibility (RSDP-F), and R-SDP-Solution (RSDP-S). The developed models were used to derive optimal monthly release rules in a single reservoir, and multiple simulations of the derived monthly policy under inflow scenarios with varying mean and standard deviations are undergone. Simulation results were then evaluated with a wide range of evaluation metrics from reliability, resiliency, vulnerability to additional robustness measures. Evaluation results were finally visualized with advanced visualization tools that are used in multi-objective robust decision making (MORDM) framework. As a result, RSDP-F and RSDP-S models yielded more risk averse, or conservative, results than the CSDP model, and a trade-off relationship between traditional and robustness metrics was discovered.

• 한국항공우주학회지
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• 제34권9호
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• pp.33-44
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• 2006

최근 공학적 설계문제들이 복잡해짐에 따라 크리깅을 이용한 근사최적화에 관한 연구가 활발하다. 따라서 본 논문에서는 개선된 확률론적 최적화 알고리즘을 제안함으로써 크리깅을 이용한 근사최적설계의 정확성과 효율성을 높이고자한다. 순차적 근사최적화 시 확률적인 설계영역으로의 이동을 위해 새로운 방법인 확률론적 국부화기법(SLM)을 제안하며, 고전적 계획법, 공간충진 계획법의 두 실험계획법을 사용함으로써 실험점 선정의 효율성을 높이고, 실험계획법의 종류에 따른 결과를 비교, 분석하였다. 또한 3부재 트러스, Sandgren의 압력용기 그리고 하니콤 인공위성 플랫폼 최적설계의 실제 공학적 문제에 적용함으로써 효율성을 검증하고자 한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.197-198
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• 2008

In this paper, we study an opportunistic scheduling scheme for the OFDMA system with streaming services. The service is modeled by using the appropriate utility function. We formulate a stochastic optimization problem that aims at maximizing network utility while satisfying the QoS requirement of each user. The problem is solved by using the dual approach and the stochastic sub-gradient algorithm.

• 한국항행학회논문지
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• 제19권6호
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• pp.636-642
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• 2015

본 논문에서는 계단응답으로부터 시 지연을 갖는 선형 연속시스템을 식별하기 위해 HS 최적화 알고리즘을 적용에 관하여 연구하였다. 인식 모델은 1차 시 지연 모델 (FOPDT)로써, FOPDT은 많은 화학 공정과 HAVC 공정에 실효성이 있으며 PID 튜닝에도 적합하다. 최근에 개발된 HS 알고리즘은 완벽한 하모니를 찾아가는 음악적 과정을 개념화 한 것이다. 수학을 기반으로 하는 전통적 기법과 달리 HS는 확률적인 방법을 사용하므로 미분과 같은 수학적 접근을 필요로 하지 않는다. 제시된 인식 방법의 효과를 입증하기 위해 많은 수치 예를 수행하여 결과를 제시하였다.

• International Journal of Control, Automation, and Systems
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• 제3권2호
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• pp.236-243
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• 2005

This paper presents a new stochastic approach for solving combinatorial optimization problems by using a new selection method, i.e. SA-selection, in genetic algorithm (GA). This approach combines GA with simulated annealing (SA) to improve the performance of GA. GA and SA have complementary strengths and weaknesses. While GA explores the search space by means of population of search points, it suffers from poor convergence properties. SA, by contrast, has good convergence properties, but it cannot explore the search space by means of population. However, SA does employ a completely local selection strategy where the current candidate and the new modification are evaluated and compared. To verify the effectiveness of the proposed method, the optimization of a fuzzy controller for balancing an inverted pendulum on a cart is considered.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2005년도 춘계 학술발표회 논문집
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• pp.351-358
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• 2005

Many methods have been developed and are in use for structural size optimization problems, In which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. This paper proposes an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) meta-heuristic algorithm. The recently developed HS algorithm was 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 In this paper, a discrete search strategy using the HS algorithm is presented in detail and its effectiveness and robustness, as compared to current discrete optimization methods, are demonstrated through a standard truss example. The numerical results reveal that the proposed method is a powerful search and design optimization tool for structures with discrete-sized members, and may yield better solutions than those obtained using current method.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.1322-1327
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• 2003

It is important to determine supporting locations for structural stability of a structure under non-uniform load in space interfered by other parts. In this case, There are many local optima with discontinuous design space. Therefore, The traditional optimization methods based on derivative are not suitable. Whereas, Genetic algorithm(GA) based on stochastic search technique is a very robust and general method. This paper has been presented to determine supporting locations of the vertical supports for reducing stress of the KSTAR(Korea super Superconducting Tokamak Advanced Research) IVCC(In-vessel control coil) under non-uniform electromagnetic load and space interfered by other parts using genetic algorithm. For this study, we develop a program combining finite element analysis with a genetic algorithm to perform structural analysis of IVCC. In addition, this paper presents a technique to perform optimization with FEM when design variables are trapped in an incongruent design space.

• Structural Engineering and Mechanics
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• 제42권3호
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• pp.335-352
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• 2012

Particle Swarm Optimization (PSO) is a stochastic population based optimization algorithm which has attracted attentions of many researchers. This method has great potentials to be applied to many optimization problems. Despite its robustness the standard version of PSO has some drawbacks that may reduce its performance in optimization of complex structures such as laminated composites. In this paper by suggesting a new variation scheme for acceleration parameters and inertial weight factors of PSO a novel optimization algorithm is developed to enhance the basic version's performance in optimization of laminated composite structures. To verify the performance of the new proposed method, it is applied in two multi-objective design optimization problems of laminated cylindrical. The numerical results from the proposed method are compared with those from two other conventional versions of PSO-based algorithms. The convergancy of the new algorithms is also compared with the other two versions. The results reveal that the new modifications inthe basic forms of particle swarm optimization method can increase its convergence speed and evade it from local optima traps. It is shown that the parameter variation scheme as presented in this paper is successful and can evenfind more preferable optimum results in design of laminated composite structures.