• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1994년도 추계학술발표회 및 정기총회
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• pp.1-2
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• 1994

With the prevalence of computers in modern organizations, simulation is receiving more atention as an effectvie decision -making tool. Simualtion is a computer-based numerical technique which uses mathmatical and logical models to approximate the behaviror of a real-world system. However, iptimization of synamic stochastic systems often defy analytical and algorithmic soluions. Although a simulation approach is often free fo the liminting assumption s of mathematical modeling, cost and time consiceration s make simulation the henayst's last resort. Therefore, whenever possible, analytical and algorithmica solutions are favored over simulation. This paper discussed the issues and procedrues for using simulation as a tool for optimization of stochastic complex systems that are dmodeled by computer simulation . Its emphasis is mostly on issues that are speicific to simulation optimization instead of consentrating on the general optimizationand mathematical programming techniques . A simulation optimization problem is an optimization problem where the objective function. constraints, or both are response that can only be evauated by computer simulation. As such, these functions are only implicit functions of decision parameters of the system, and often stochastic in nature as well. Most of optimization techniqes can be classified as single or multiple-resoneses techniques . The optimization of single response functins has been researched extensively and consists of many techniques. In the single response category, these strategies are gradient based search techniques, stochastic approximate techniques, response surface techniques, and heuristic search techniques. In the multiple response categroy, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphica techniqes, direct search techniques, constrained optimization techniques, unconstrained optimization techniques, and goal programming techniques. The choice of theprocedreu to employ in simulation optimization depends on the analyst and the problem to be solved. For many practival and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computersimulation is one of the most effective means of studying such complex systems. In this paper, after discussion of simulation optmization techniques, the applications of above techniques will be presented in the modeling process of many flexible manufacturing systems.

• Journal of Electrical Engineering and Technology
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• 제6권2호
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• pp.182-191
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• 2011

Genetic algorithms (GA) and particle swarm optimization (PSO) are the most famous optimization techniques among various modern heuristic optimization techniques. These two approaches identify the solution to a given objective function, but they employ different strategies and computational effort; therefore, a comparison of their performance is needed. This paper presents the application and performance comparison of the PSO and GA optimization techniques for a static synchronous series compensator-based controller design. The design objective is to enhance power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem, and both PSO and GA optimization techniques are employed to search for the optimal controller parameters.

• Journal of Power Electronics
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• 제16권3호
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• pp.1097-1109
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• 2016

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

• Structural Engineering and Mechanics
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• 제55권6호
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• pp.1121-1137
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• 2015

High powered computers and engineering computer systems allow designers to routinely simulate complex physical phenomena. The presented work deals with the analysis of two finite element method optimization techniques (First Order Method-FOM and Subproblem Approximation Method-SAM) implemented in the individual Design Optimization module in the Ansys software to analyze the behavior of real problems. A design optimization is a difficult mathematical process, intended to find the minimum or maximum of an objective function, which is mostly based on iterative procedure. Using optimization techniques in engineering designs requires detailed knowledge of the analyzed problem but also an ability to select the appropriate optimization method. The methods embedded in advanced computer software are based on different optimization techniques and their efficiency is significantly influenced by the specific character of a problem. The efficiency, robustness and accuracy of the methods are studied through strictly convex two-dimensional optimization problem, which is represented by volume minimization of two bars' plane frame structure subjected to maximal vertical displacement limit. Advantages and disadvantages of the methods are described and some practical tips provided which could be beneficial in any efficient engineering design by using an optimization method.

• International Journal of Fluid Machinery and Systems
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• 제2권2호
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• pp.179-188
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• 2009

Recent development of high speed computers and use of optimization techniques have given a big momentum of turbomachinery design replacing expensive experimental cost as well as trial and error approaches. The surrogate based optimization techniques being used for aerodynamic turbomachinery designs coupled with Reynolds-averaged Navier-Stokes equations analysis involve single- and multi-objective optimization methods. The objectives commonly tried to improve were adiabatic efficiency, pressure ratio, weight etc. Presently coupling the fluid flow and structural analysis is being tried to find better design in terms of weight, flutter and vibration, and turbine life. The present article reviews the surrogate based optimization techniques used recently in turbomachinery shape optimizations.

• 한국시뮬레이션학회논문지
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• 제3권2호
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• pp.57-67
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• 1994

For many practical and industrial optimization problems where some or all 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. In this paper, with discussion of simulation optimization techniques, a case study in machining process for application of simulation optimization is presented. Most of optimization techniques can be classified as single-or multiple-response techniques. The optimization of single-response category, these strategies are gradient based search methods, stochastic approximate method, response surface method, and heuristic search methods. In the multiple-response category, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphical method, direct search method, constrained optimization, unconstrained optimization, and goal programming methods. The choice of the procedure to employ in simulation optimization depends on the analyst and the problem to be solved.

• 한국전산구조공학회논문집
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• 제24권5호
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• pp.543-551
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• 2011

본 논문에서는 해양작업 상태의 하중조건을 고려한 부유식 원유생산 저장 하역장치에 설치된 라이져 보강구조의 강도설계에 관련하여 다양한 근사화 기법 기반 설계최적화 및 그 성능을 비교하고자 한다. 설계최적화 문제는 하중조건별 구조강도의 제한조건 하에서 중량을 최소화하여 설계변수인 구조 부재치수가 결정되도록 정식화된다. 비교 연구를 위해 사용된 근사화 기법은 반응표면법 기반 순차적 근사최적화(RBSAO), 크리깅 기반 순차적 근사최적화(KBSAO), 그리고 개선된 이동최소자승법(MLSM) 기반 근사최적화 기법인 CF-MLSM와 Post-MLSM이다. RBSAO와 KBSAO의 적용을 위하여 상용프로세스 통합 설계최적화(PIDO) 코드를 사용하였다. 본 연구에 적용한 MLSM 기반 근사최적화 기법들은 제한조건의 가용성을 보장할 수 있도록 새롭게 개발되었다. 다양한 근사화 모델 기반 설계최적화 기법에 의한 결과는 설계 해의 개선 및 수렴속도 등의 수치적 성능을 기준으로 실제 비근사 설계최적화 결과와 비교 검토하였다.

• Journal of applied mathematics & informatics
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• 제5권3호
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• pp.701-716
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• 1998

In this paper we develop algorithms in programming lan-guage SCHEME for implementation of the main first order gradient techniques for unconstrained optimization. Implementation of the de-scent techniques which use non-optimal descent steps as well as imple-mentation of the optimal descent techniques are described. Also we investigate implementation of the global problem called optimization along a line. Developed programs are effective and simpler with re-spect to the corresponding in the procedural programming languages. Several numerical examples are reported.

• 대한기계학회논문집A
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• 제34권11호
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• pp.1603-1611
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• 2010

본 논문에서는 범프 및 브레이크 하중조건 하에서 자동차 넉클의 강도설계에 관한 다양한 회귀모델 기반 근사최적화 기법 및 그 성능을 비교하고자 한다. 최적설계문제는 응력, 변형 및 진동주파수의 제한조건 하에서 중량을 최소화하여 설계변수인 단면치수가 결정되도록 정식화 된다. 비교 연구를 위해 사용된 근사화 기법은 순차적 근사최적화(SAO), 순차적 이점대각이차 근사최적화(STDQAO), 그리고 개선된 이동최소 자승법(MLSM) 기반 근사최적화 기법인 CF-MLSM 와 Post-MLSM 이다. SAO 와 STDQAO 적용을 위하여 상용 프로세스통합 설계최적화(PIDO) 코드를 사용하였다. 본 연구에 적용한 MLSM 기반 근사최적화 기법들은 제한조건의 가용성을 보장할 수 있도록 새롭게 개발되었다. 다양한 근사최적화 기법에 의한 설계결과는 설계 해의 개선 및 수렴속도 등 수치적 성능을 기준으로 실제 비근사최적화 결과와 비교검토 되었다.

• International Journal of Computer Science & Network Security
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• 제24권8호
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• pp.72-84
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• 2024

Plant leaf classification is an imperative task when their use in real world is considered either for medicinal purposes or in agricultural sector. Accurate identification of plants is, therefore, quite important, since there are numerous poisonous plants which if by mistake consumed or used by humans can prove fatal to their lives. Furthermore, in agriculture, detection of certain kinds of weeds can prove to be quite significant for saving crops against such unwanted plants. In general, Artificial Neural Networks (ANN) are a suitable candidate for classification of images when small datasets are available. However, these suffer from local minima problems which can be effectively resolved using some global optimization techniques. Considering this issue, the present research paper presents an automated plant leaf classification system using optimized soft computing models in which ANNs are optimized using Grasshopper Optimization algorithm (GOA). In addition, the proposed model outperformed the state-of-the-art techniques when compared with simple ANN and particle swarm optimization based ANN. Results show that proposed GOA-ANN based plant leaf classification system is a promising technique for small image datasets.