• 산업경영시스템학회지
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• 제33권4호
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• pp.58-68
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• 2010

Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such as traveling salesman, vehicle routing, and flow shop scheduling problems. They are different in representation of position and velocity vectors, operation mechanisms for updating vectors. In this paper, the performance of 5 DPSOs is analyzed by applying to traditional Traveling Salesman Problems. The experiment shows that DPSOs are comparable or superior to a genetic algorithm (GA). Also, hybrid PSO combined with local optimization (i.e., 2-OPT) provides much improved solutions. Since DPSO requires more computation time compared with GA, however, the performance of hybrid DPSO is not better than hybrid GA.

• 한국해양공학회지
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• 제24권6호
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• pp.109-113
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• 2010

We present the minimum weight optimum design of cross sectional for frame structures subject to natural frequency. The optimum design in this paper employ discrete and continuous design variables and Genetic Algorithms. In this paper, Genetic Algorithms is used in optimization process, and be used the method of Elitism and penalty parameters in order to improved fitness in the reproduction process. For 1-Bay 2-Story frame structure, in examples, continuous and discrete design variables are used, and W-section (No.1~No.64), from AISC, discrete data are used in discrete optimization. In this case, Exhaustive search are used for finding global optimum. Continuous variables are used for 1-Bay 7-Story frame structure. Two typical frame structure optimization examples are employed to demonstrate the availability of Genetic Algorithms for solving minimum weight optimum of frame structures with fundamental and multi frequency.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 춘계학술대회
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• pp.1005-1010
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• 2004

The structural optimization has been carried out in the continuous design space or in the discrete design space. Generally, available designs are discrete in design practice. But methods for discrete variables are extremely expensive in computational cost. In order to overcome this weakness, an iterative optimization algorithm was proposed for design in the discrete space, which is called as a sequential algorithm using orthogonal arrays (SOA). We focus to verify the fact that the local solution can be obtained throughout the optimization with this algorithm. The local solution is defined in discrete design space. Then the search space, which is the set of candidate values of each design variables formed by the neighborhood of current design point, is defined. It is verified that a local solution can be founded by moving sequentially the search space. The SOA algorithm has been applied to problems such as truss type structures. Then it is confirmed that a local solution can be obtained using the SOA algorithm

• 전산구조공학
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• 제10권2호
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• pp.255-263
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• 1997

실제로 구조시스템들의 최적설계는 설계변수가 연속값이 아닌 이산값을 요하는 경우가 대부분이다. 본 논문은 이산형 설계변수를 갖는 비대칭 복합 적층평판에 대해 선형 근사화방법을 이용한 이산최적설계를 수행하였으며, 이 방법이 매우 효율적임을 보였다. 대상 문제는 축력, 전단력, 그리고 휨과 비틀림 모멘트의 평면 내하중들(in-plane loads)의 다중하중조건을 받는 것으로 고려하였으며, 복합 적층평판을 구성하는 플라이들에 대한 최대변형률 규준을 설계 제약조건으로 부과하였다. 이산 최적화를 위한 초기 접근방법으로 단 한번의 연속변수 최적화 과정이 FDM(Feasible Direction Method)을 이용하여 수행되었으며, 차후 이산 및 연속변수를 포함하는 비선형 이산최적화문제를 SLDP(Sequential Linear Discrete Programming)방법에 의해 선형 근사화된 혼합정수계획문제로 형성하여 풀었다. 수치예에서 6개의 플라이로 구성된 비대칭 복합 적층평판을 대상으로 회전식 적층배열([(90-.theta.)/-(60+.theta.)/-.theta./-(45+.theta.)/(45-.theta.)]/sub s/)에 따른 이산최적해를 구하였다. 효율성 입증을 위해 똑같은 문제를 비선형 분기한계법을 이용하여 풀었으며, 그 결과를 비교 분석하였다.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2001년도 제34회 추계학술대회 개최
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• pp.166-171
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• 2001

In order to improve the efficiency of the design process and the quality of the resulting design for the application-based exclusive rolling element bearings, this study propose design methodologies by using a genetic-based combinatorial optimization. By the presence of discrete variables such as the number of rolling element (standard component) and by the engineering point of views, the design problem of the rolling element bearing can be characterized by the combinatorial optimization problem as a fully discrete optimization. A genetic algorithm is used to efficiently find a set of the optimum discrete design values from the pre-defined variable sets. To effectively deal with the design constraints and the multi-objective problem, a ranking penalty method is suggested for constructing a fitness function in the genetic-based combinatorial optimization. To evaluate the proposed design method, a robust performance analyzer of ball bearing based on quasi-static analysis is developed and the computer program is applied to some design problems, 1) maximize fatigue life, 2) maximize stiffness, 3) maximize fatigue life and stiffness, of a angular contact ball bearing. Optimum design results are demonstrate the effectiveness of the design method suggested in this study. It believed that the proposed methodologies can be effectively applied to other multi-objective discrete optimization problems.

• 한국해양공학회지
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• 제21권2호
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• pp.75-80
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• 2007

The design variables for structural systems, in most practical designs, are chosen from a list of discrete values, which are commercially available sizing. This paper presents the application of Genetic Algorithms for determining the optimum design for two-dimensional structures with discrete and pseudocontinuous design variables. Genetic Algorithms are heuristic search algorithms and are effective tools for finding global solutions for discrete optimization. In this paper, Genetic Algorithms are used as the method of Elitism and penalty parameters, in order to improve fitness in the reproduction process. Examples in this paper include: 10 bar planar truss and 1 bay 8-story frame. Truss with discrete and pseudoucontinuous design variables and steel frame with W-sections are used for the design of discrete optimization.

• 한국기계가공학회지
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• 제19권5호
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• pp.27-37
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• 2020

Structural optimization problems with discrete design variables require more function calculations (or finite element analyses) than those in the continuous design space. In this study, a method to find an optimal solution in the discrete design of the truss structure is presented, reducing the number of function calculations. Because a continuous optimal solution is the Karush-Kuhn-Tucker point that satisfies the optimality condition, it is assumed that the discrete optimal solution is around the continuous optimum. Then, response values such as weight, displacement, and stress are predicted using approximate models-referred to as hybrid metamodels-within specified design ranges. The discrete design method using the hybrid metamodels is used as a post-process of the continuous optimization process. Standard truss design problems of 10-bar, 25-bar, 15-bar, and 52-bar are solved to show the usefulness of this method. The results are compared with those of existing methods.

• Steel and Composite Structures
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• 제15권4호
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• pp.451-466
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• 2013

This paper deals with the multi-objective optimization of tire reinforcement structures such as the tread belt and the carcass path. The multi-objective functions are defined in terms of the discrete-type design variables and approximated by artificial neutral network, and the sensitivity analyses of these functions are replaced with the iterative genetic evolution. The multi-objective optimization algorithm introduced in this paper is not only highly CPU-time-efficient but it can also be applicable to other multi-objective optimization problems in which the objective function, the design variables and the constraints are not continuous but discrete. Through the illustrative numerical experiments, the fiber-reinforced tire belt structure is optimally tailored. The proposed multi-objective optimization algorithm is not limited to the tire reinforcement structure, but it can be applicable to the generalized multi-objective structural optimization problems in various engineering applications.

• 조명전기설비학회논문지
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• 제23권9호
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• pp.55-61
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• 2009

본 논문에서는 구역전기사업자를 구성하는데 적용하기 위해, 기존의 최적화 기법인 Discrete Particle Swarm Optimization (DPSO) 알고리즘을 개량한 Phasor DPSO (PDPSO) 알고리즘을 새롭게 제시한다. 구역전기사업자는 전력구입 뿐만 아니라 전력판매도 가능하고, 미리 계약한 수용가의 전력부하에게 전력을 공급할 의무가 있다. 하나의 배전계통에 다수의 구역전기사업자가 존재할 경우, 해당 배전계통 내의 모든 수용가에게 최소의 운영비용으로 전력을 공급하기 위해서는 다수 구역전기사업자 간에 구성형태를 조정할 필요가 있다. 이에 적용할 최적화 기법으로 본 논문은 PDPSO 알고리즘을 제안하며, 제안된 알고리즘의 각 개체는 기존의 다변수 벡터 대신 크기와 위상각으로 이루어진 다변수 페이저 값을 갖는다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1362-1366
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• 2003

Workload programming is allocating suitable workloads of production process according to the needs of products, which would minimize the total cost of both work and stock under some constraint conditions. In this paper, a production process flow chart of discrete manufacturing is presented by a Petri net, and the optimization model of workload-stock is established. An approach of the optimal workloads is provided by means of the integer matrix theory. An example is given to verify this method.