• Journal of Mechanical Science and Technology
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• 제20권12호
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• pp.2115-2123
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• 2006

The paper deals with the identification of Pareto optimal solutions using GA based coevolution in the context of multiobjective optimization. Coevolution is a genetic process by which several species work with different types of individuals in parallel. The concept of cooperative coevolution is adopted to compensate for each of single objective optimal solutions during genetic evolution. The present study explores the GA based coevolution, and develops prescribed and adaptive scheduling schemes to reflect design characteristics among single objective optimization. In the paper, non-dominated Pareto optimal solutions are obtained by controlling scheduling schemes and comparing each of single objective optimal solutions. The proposed strategies are subsequently applied to a three-bar planar truss design and an energy preserving flywheel design to support proposed strategies.

• 대한조선학회지
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• 제31권1호
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• pp.34-38
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• 1994

본 연구에서는 Genetic Algorithm을 사용하여 상기의 문제를 해결하고자 한다. 특히 다목적 함수 최적화에는 한 번의 최적화 계산으로 Pareto최적해 집합이 동시에 구해지는 새로운 방법인 MOGA(Multicriteria Optimization by Genetic Algorithm)을 개발하였다. 먼저 Genetic Alorithm의 기본 특성에 대해 살펴보고, 다양한 종류의 문제를 통해 Genetic Algorithm의 유용 성을 검토하였다.

• Smart Structures and Systems
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• 제22권4호
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• pp.469-479
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• 2018

The present research study utilizes a multi-objective optimization method for Pareto optimization of an eight-degree of freedom full vehicle vibration model, adopting a non-dominated sorting genetic algorithm II (NSGA-II). In this research, a full set of ride comfort as well as ride safety parameters are considered as objective functions. These objective functions are divided in to two groups (ride comfort group and ride safety group) where the ones in one group are in conflict with those in the other. Also, in this research, a special optimizing technique and combinational method consisting of weighted sum method and Pareto optimization are applied to transform Pareto double-objective optimization to Pareto full-objective optimization which can simultaneously minimize all objectives. Using this technique, the full set of ride parameters of three dimensional vehicle model are minimizing simultaneously. In derived Pareto front, unique trade-off design points can selected which are non-dominated solutions of optimizing the weighted sum comfort parameters versus weighted sum safety parameters. The comparison of the obtained results with those reported in the literature, demonstrates the distinction and comprehensiveness of the results arrived in the present study.

• Structural Engineering and Mechanics
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• 제70권3호
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• pp.367-380
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• 2019

The paper concerns topology and geometry optimization of statically determinate beams with arbitrary number of supports. The optimization problem is treated as a bi-criteria one, with the objectives of minimizing the absolute maximum bending moment and the maximum deflection for a uniform gravity load. The problem is formulated and solved using the Pareto optimality concept and the lexicographic ordering of the objectives. The non-dominated sorting genetic algorithm NSGA-II and the local search method are used for the optimization in the Pareto sense, whereas the genetic algorithm and the exhaustive search method for the lexicographic optimization. Trade-offs between objectives are examined and sets of Pareto-optimal solutions are provided for different topologies. Lexicographically optimal beams are found assuming that the maximum moment is a more important criterion. Exact formulas for locations and values of the maximum deflection are given for all lexicographically optimal beams of any topology and any number of supports. Topologies with lexicographically optimal geometries are classified into equivalence classes, and specific features of these classes are discussed. A qualitative principle of the division of topologies equivalent in terms of the maximum moment into topologies better and worse in terms of the maximum deflection is found.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2001년도 추계학술대회(한국공작기계학회)
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• pp.231-236
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• 2001

The goal of multiphase optimization of machine structure is to obtain 1) light weight, 2) statically and dynamically rigid structure. The entire optimization process is carried out in two phases. In the first phase, multiple optimization problem with two objective functions is treated using pareto genetic algorithm. Two objective functions are weight of the structure, and static compliance. In the second phase, maximum receptance is minimized using genetic algorithm. The method is applied to design of quill type machine structure with back column.

• Nuclear Engineering and Technology
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• 제42권4호
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• pp.414-425
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• 2010

In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into "families". On the basis of the decision maker's preferences, each family is then synthetically represented by a "head of the family" solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions.

• Industrial Engineering and Management Systems
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• 제10권1호
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• pp.54-64
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• 2011

The purpose of this study is to optimally allocate the human resources to tasks while minimizing the total daily human resource costs and smoothing the human resource usage. The human resource allocation problem (hRAP) under consideration contains two kinds of special constraints, i.e. operational precedence and skill constraints in addition to the ordinary constraints. To deal with the multiple objectives and the special constraints, first we designed this hRAP as a network problem and then proposed a Pareto multistage decisionbased genetic algorithm (P-mdGA). During the evolutionary process of P-mdGA, a Pareto evaluation procedure called generalized Pareto-based scale-independent fitness function approach is used to evaluate the solutions. Additionally, in order to improve the performance of P-mdGA, we use fuzzy logic controller for fine-tuning of genetic parameters. Finally, in order to demonstrate the applicability and to evaluate the performance of the proposed approach, P-mdGA is applied to solve a case study in a hotel, where the managers usually need helpful automatic support for effectively allocating hotel staff to hotel tasks.

• 융합신호처리학회논문지
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• 제9권1호
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• pp.39-45
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• 2008

WDM 네트워크는 높은 전송속도와 낮은 지연시간으로 메트로폴리탄 네트워크뿐만 아니라 최근 기가비트 이더넷 등을 이용하여 근거리 망에서도 많은 연구가 진행되어 왔다. 네트워크의 성능은 네트워크 구조의 파라미터 값들과 사용되는 Medium Access Control 프로토콜의 파라미터 값들에 많이 의존한다. 또한 네트워크 효율성과 지연시간은 주로 상반된 관계를 보여 한쪽의 희생이 불가피 하였다. 네트워크를 효율적으로 운용하기 위해서는 효율성과 지연시간이라는 성능의 최적값을 찾아야 상황에 맞게 운용할 수 있다. 본 논문에서는 Arrayed Waveguide Grating (AWG) 기반의 성형 WDM 네트워크상에서 효율성의 최대화와 지연시간의 최소화라는 두 개의 서로 상반된 목적 함수를 유전자 알고리즘 기반의 방법론을 이용하여 파레토 최적화 곡선이라는 최적의 값들을 찾아내었다. 이를 이용하여 구한 최적의 네트워크 구성을 위한 파라미터 값들과 MAC 프로토콜의 파라미터 값들을 이용하여 상황에 따른 최적의 네트워크 성능을 유추할 수 있게 되었다. 본 논문에 사용된 유전자 알고리즘을 이용한 최적화 방법은 이와 유사한 상반된 목적 함수를 갖는 네트워크의 성능을 최적화하는데 사용필 수 있을 것이다.

• 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.

• 한국전자파학회논문지
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• 제16권1호
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• pp.40-48
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• 2005

본 논문에서는 NEC 코드와 Pareto 유전자 알고리즘 최적화 기법을 이용하여 초소형 유도결합 안테나를 설계하였다. 최적화된 유도결합 안테나 중 몇 가지 표본을 제작하고 성능을 측정하였다. 일반적으로 안테나의 크기가 작아질수록 입력 저항, 대역폭 및 효율이 감소하는데 비하여 제안된 방법으로 설계된 유도결합 안테나는 다른 부가적인 정합회로 없이 우수한 성능을 보인다. 간단한 회로 모델을 도입하여 제안된 유도결합 안테나의 동작원리를 설명하였고, Duroid 기판 위에 평면 구조로 제작하여 RFID 태그 안테나로써 성능을 입증하였다.