• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제16권11호
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• pp.1106-1110
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• 2010

본 논문은 자동화 컨테이너 터미널의 장치장에서 장치 위치 결정 전략을 다목적 진화 알고리즘(MOEA: Multi-Objective Evolutionary Algorithm)을 이용해 최적화하는 방안을 제안한다. 장치장의 해측과 육측 생산성은 서로 상충하기 때문에, 이 둘을 동시에 최대화하는 것은 불가능하다. 대신 본 논문에서는 MOEA를 이용해 파레토 최적해 집합(Pareto optimal set)을 구하였다. 초기 실험 결과 장치장의 컨테이너 점유율이 높은 어려운 문제의 경우, MOEA의 집단이 지역 해에 쉽게 빠지는 것을 확인하였다. 이에 본 논문에서는 난이도가 다른 두 개의 문제를 동시에 최적화함으로써 집단의 다양성을 유지하는 방안을 제안하였으며, 실험 결과 제안 방안이 단일 문제만 해결하는 방안에 비해 동일한 비용으로 더 좋은 전략을 얻을 수 있음을 확인하였다.

• 전자공학회논문지C
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• 제35C권6호
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• pp.56-66
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• 1998

본 논문은 비최소 위상 시스템을 보다 효율적으로 제어하기 위하여 기존의 PID 타입의 선형 제어기와 병렬적으로 구성된 신경망 제어기의 구성에 대하여 다룬다. 제안된 제어기의 제어 목표는 비최소 위상 시스템의 제어의 경우 흔히 나타나는 언더슛 현상을 최소화하면서 설정된 시스템 응답과의 응답 오차가 최소화하는 것이다. 전체 비용 함수는 고려된 두 가지의 개별 목적 함수간의 선형 합으로 이루어 진다. 신경망 제어기는 주어진 전체 제어 시간 동안의 제어 성능을 광역 평가를 통하여 주어진 전체 비용 함수를 최소화하는 최적 제어기를 구성하도록 진화 프로그래밍을 이용하여 off-line으로 학습된다. 일반적인 컴퓨터 모의 실험으로 계단 신호 응답에서 나타나는 빠른 settling 시간, 작은 언더슛과 오버슛과 같은 제어 성능 향상의 관점에서 기존의 선형 제어 시스템의 성능에 비해 훨씬 효과적이라는 것을 보인다. 또한, 파렛토(pareto) 다중 최적화 개념을 도입하여 선형 합으로 이루어진 비용 함수 최적화의 한계성과 문제점을 극복한다.

• 한국전산구조공학회논문집
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• 제15권4호
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• pp.591-598
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• 2002

공학적 설계에 있어 많은 문제들은 몇 가지 목적함수들을 동시에 최소화하여야 할 필요가 있을 경우가 있다. 선박설계에 있어, 종래에는 자재비 경감과 재화중량 증가를 위해 최소중량설계가 구조 설계의 주된 목적이었으나, 값싼 노동력을 내세운 후발 조선국과의 치열한 국제 경쟁을 극복하기 위해서는 보다 경제성 있는 선박 건조 기술 개발이 선행되어야 할 것이다. 이에 따라 본 연구에서는 다목적함수 최적화기법을 이용한 선체 구조의 보다 합리적인 설계 방안에 대한 연구를 수행하여 실제 건조된 유조선을 대상으로 중량, 건조비 등의 경제성을 비교 평가하였다. 다목적 함수로는 유조선의 중량과 건조비로 하였으며 최적화 기법으로는 확률론적 탐색법인 ES(Evolution Strategies)를 이용하였다. 건조비 모델은 상대 건조비 개념을 도입하였고, 종강도 부재는 선급규정에 의해, 횡강도 및 횡격벽 부재는 직접해석법인 일반화된 경사처짐법을 사용하여 설계에 적용하였다. 다목적함수 최적화 결과로부터 도출된 Pareto 최적 설계점들에 대하여, 요구운임률을 각각 산정함으로써 이들 최적 설계점들 중에서 가장 경제성이 뛰어난 선박 설계 방안을 제시하였다.

• International Journal of Precision Engineering and Manufacturing
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• 제7권4호
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• pp.57-62
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• 2006

The quality of precision spindle units (S/Us) running on rolling bearings depends strongly on their structural parameters, such as the configuration and geometry of the S/U elements and bearing preloads. When S/Us are designed, their parameters should be optimized to improve the performance characteristics. However, it is practically impossible to state perfectly a general criterion function for S/U quality. Therefore, we propose to use a multicriteria optimization based on the parameter space investigation (PSI) method We demonstrate the efficiency of the proposed method using the optimization results of high-speed S/Us.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권5호
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• pp.325-331
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• 2004

The multiobjective optimization (MO) problem usually includes the conflicting objectives and the use of conventional optimization algorithms for MO problem does not so good approach to obtain an effective optimal solution. In this paper, genetic algorithm (GA) as an effective method is used to solve such MO problem of brushless DC motor (BLDCM). 3D equivalent magnetic circuit network (EMCN) method which enables us to reduce the computational burden is also used to consider the 3D structure of BLDCM. In order to effectively obtain a set of Pareto optimal solutions in MO problem, ranking method proposed by Fonseca is applied. The objective functions are decrease of cogging torque and increase of torque respectively. The airgap length, teeth width and magnetization angle of PM are selected for the design variables. The experimental results are also shown to confirm the validity of the optimization results.

• 대한조선학회논문집
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• 제44권2호
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• pp.139-147
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• 2007

The ultimate goal of structural design is to find the optimal design results which satisfies both safety and economy at the same time. Optimum design has been studied for the last several decades and is being studied. in this study, an optimum algorithm which is based on the genetic algorithm has been applied to the multi-object problem to obtain the optimum solutions which minimizes structural weight and construction cost of panel blocks in ship structures at the same time. Mathematical problems are dealt at first to justify the reliability of the present optimum algorithm. And then the present method has been applied to the panel block model which can be found in ship structures. From the present findings it has been seen that the present optimum algorithm can reasonably give the optimum design results.

• 대한산업공학회지
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• 제31권3호
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• pp.180-193
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• 2005

This paper deals with a multiobjective process planning problem of flexible assembly systems(FASs). The FAS planning problem addressed in this paper is an integrated one of the assignment of assembly tasks to stations and the determination of assembly routing, while satisfying precedence relations among the tasks and flexibility capacity for each station. In this research, we consider two objectives: minimizing transfer time of the products among stations and absolute deviation of workstation workload(ADWW). We place emphasis on finding a set of diverse near Pareto or true Pareto optimal solutions. To achieve this, we present a new multiobjective coevolutionary algorithm for the integrated problem here, named a multiobjective symbiotic evolutionary algorithm(MOSEA). The structure of the algorithm and the strategies of evolution are devised in this paper to enhance the search ability. Extensive computational experiments are carried out to demonstrate the performance of the proposed algorithm. The experimental results show that the proposed algorithm is a promising method for the integrated and multiobjective problem.

• Journal of Magnetics
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• 제21권4호
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• pp.660-665
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• 2016

The electromagnetic apparatus plays an important role in high power electrical systems. It is of great importance to provide an effective approach for the optimization of the high power electromagnetic apparatus. However, premature convergence and few Pareto solution set of the optimization for electromagnetic apparatus always happen. This paper proposed a modified multi-objective particle swarm optimization algorithm based on the niche sorting strategy. Applying to the modified algorithm, this paper guarantee the better Pareto optimal front with an enhanced distribution. Aiming at shortcomings in the closing bounce and slow breaking velocity of electromagnetic apparatus, the multi-objective optimization model was established on the basis of the traditional optimization. Besides, by means of the improved multi-objective particle swarm optimization algorithm, this paper processed the model and obtained a series of optimized parameters (decision variables). Compared with other different classical algorithms, the modified algorithm has a satisfactory performance in the multi-objective optimization problems in the electromagnetic apparatus.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.997-1000
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• 2005

Dynamic job shop environment requires not only more flexible capabilities of a CAPP system but higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations to be performed by predicting the machine loads. The developed algorithm is based on the multiple objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as Pareto-optimal solutions). The objective shows a combination of the minimization of part movement and the maximization of machine utility balance. The algorithm is characterized by a new and efficient method for nondominated sorting, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II.

• 한국정밀공학회지
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• 제24권4호
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• pp.84-92
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• 2007

Dynamic process planning requires not only more flexible capabilities of a CAPP system but also higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations by calculating the machine loads. The developed algorithm is based on the multi-objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as the Pareto-optimal solutions). The objective is to satisfy both the minimization number of part movements and the maximization of machine utilization. The algorithm is characterized by a new and efficient method for nondominated sorting through K-means algorithm, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II and branch and bound algorithm.