• 대한조선학회논문집
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• 제33권1호
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• pp.161-172
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• 1996

최적화기법을 포함한 종래의 전산 프로그램들은 수치적 계산과정과 그 결과에만 중점을 두고 개발되어 왔으며, 설계모델의 개발과 최적화기법의 선택 및 결과의 판단 등은 설계 전문가에 의하여 수행되어 왔다. 반면에 전문가의 경험적지식을 처리하는 지식기반시스템은 기호처리에 중점을 두고 있기 때문에 수치적 계산을 효과적으로 할 수 없다. 본 논문에서는 수치적인 계산결과만을 제공하는 최적화기법의 한계와 기호처리에 중점을 두고 있는 지식기반시스템의 한계를 극복하여, 보다 현실적인 최적설계안을 도출할 수 있는 지식기반 다목적함수 최적설계 시스템을, 최적화기법과 LISP 언어로 개발한 지식기반시스템을 통합하여 구현하고, 이를 LNG선의 최적설계 모델에 적용하여 개발된 시스템의 유용성을 보였다.

• 대한수학회논문집
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• 제28권2호
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• pp.419-423
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• 2013

In this paper we present a robust duality theory for generalized convex programming problems under data uncertainty. Recently, Jeyakumar, Li and Lee [Nonlinear Analysis 75 (2012), no. 3, 1362-1373] established a robust duality theory for generalized convex programming problems in the face of data uncertainty. Furthermore, we extend results of Jeyakumar, Li and Lee for an uncertain multiobjective robust optimization problem.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.651-652
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• 2008

In this paper, we suggest a new multiobjective optimization algorithm which is based on the simulated annealing(SA) method. The proposed algorithm uses population-based simulated annealing and adapts elitism in the process of selection.

• 대한수학회논문집
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• 제16권4호
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• pp.667-677
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• 2001

We consider a nonsmooth multiobjective opimization problem(PE) involving locally Lipschitz functions and define gen-eralized invexity for locally Lipschitz functions. Using Fritz John type optimality conditions, we establish Fritz John type sufficient optimality theorems for (PE) under generalized invexity.

• 한국정밀공학회지
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• 제17권5호
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• pp.93-99
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• 2000

In many dynamic structural optimization problems, the goal is to reduce the total weight of the structure without causing the resonance. Up to now, gradient informations(i.e., design sensitivity) have been used to achieve the goal. For some class of dynamic problems, especially coalescent eigenvalue Problems with multiobjective optimization, the design sensitivity analysis is too much complicated mathematically and numerically. Therefore, this article proposes a new technique fur structural dynamic modification using a mode modification method with Genetic Algorithm(GA). In GA formulation, fitness is defined based on penalty function approach. Design variables are iteratively improved by using genetic algorithm. Two numerical examples are shown, (ⅰ) a cantilevered plate, and (ⅱ) H-shaped structure. The results demonstrate that the proposed method is highly efficient.

• International Journal of Control, Automation, and Systems
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• 제1권3호
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• pp.271-281
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• 2003

In this paper, a new nonlinear programming approach is suggested to solve biaffine matrix inequality (BMI) problems in multiobjective and structured controls. It is shown that these BMI problems are reduced to nonlinear minimization problems. An algorithm that is easily implemented with existing convex optimization codes is presented for the nonlinear minimization problem. The efficiency of the proposed algorithm is illustrated by numerical examples.

• Industrial Engineering and Management Systems
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• 제11권4호
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• pp.310-330
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• 2012

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In manufacturing systems, the purpose of scheduling is to minimize the production time and costs, by assigning a production facility when to make, with which staff, and on which equipment. Production scheduling aims to maximize the efficiency of the operation and reduce the costs. In order to find an optimal solution to manufacturing scheduling problems, it attempts to solve complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. Genetic algorithm (GA) is one of the generic population-based metaheuristic optimization algorithms and the best one for finding a satisfactory solution in an acceptable time for the NP-hard scheduling problems. GA is the most popular type of evolutionary algorithm. In this survey paper, we address firstly multiobjective hybrid GA combined with adaptive fuzzy logic controller which gives fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and four crucial issues in the manufacturing scheduling including a mathematical model, GA-based solution method and case study in flexible job-shop scheduling problem (fJSP), automatic guided vehicle (AGV) dispatching models in flexible manufacturing system (FMS) combined with priority-based GA, recent advanced planning and scheduling (APS) models and integrated systems for manufacturing.

• 정보처리학회논문지B
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• 제15B권1호
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• pp.53-60
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• 2008

본 논문은 입술의 움직임을 통해 음성을 인식하는 자동 독순의 인식 성능 향상을 위해 인식기로 사용되는 은닉 마르코프 모델을 분별적으로 학습하는 기법을 제안한다. 기존에 많이 사용되는 Baum-Welch 알고리즘에서는 각 모델이 해당 클래스 데이터의 확률을 최대화하는 것을 목표로 학습시키는 반면, 제안하는 알고리즘에서는 클래스간의 분별력을 높이기 위해 두 가지의 최소화 목적함수로 이루어진 새로운 학습 목표를 정의하고 이를 달성하기 위해 모의 담금질 기법에 기반을 둔 다목적함수 전역 최적화 기법을 개발한다. 화자종속 인식 실험을 통해 제안하는 기법의 성능을 평가하며, 실험결과 기존의 학습 방법에 비해 오인식율을 상대적으로 약 8% 감소시킬 수 있음을 보인다.

• 전기학회논문지
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• 제61권1호
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• pp.135-142
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• 2012

In this paper, we proposed a new architecture called radial basis function-based polynomial neural networks classifier that consists of heterogeneous neural networks such as radial basis function neural networks and polynomial neural networks. The underlying architecture of the proposed model equals to polynomial neural networks(PNNs) while polynomial neurons in PNNs are composed of Fuzzy-c means-based radial basis function neural networks(FCM-based RBFNNs) instead of the conventional polynomial function. We consider PNNs to find the optimal local models and use RBFNNs to cover the high dimensionality problems. Also, in the hidden layer of RBFNNs, FCM algorithm is used to produce some clusters based on the similarity of given dataset. The proposed model depends on some parameters such as the number of input variables in PNNs, the number of clusters and fuzzification coefficient in FCM and polynomial type in RBFNNs. A multiobjective particle swarm optimization using crowding distance (MoPSO-CD) is exploited in order to carry out both structural and parametric optimization of the proposed networks. MoPSO is introduced for not only the performance of model but also complexity and interpretability. The usefulness of the proposed model as a classifier is evaluated with the aid of some benchmark datasets such as iris and liver.

• 한국수학교육학회지시리즈A:수학교육
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• 제30권1호
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• pp.35-42
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• 1991

Multiobjective Optimisation problem involving set functions is introduced. Then an iterative algorithm for these kinds of problems is suggested and its optimal process will be proved.