• 산업경영시스템학회지
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• 제28권3호
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• pp.149-155
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• 2005

In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.

• 제어로봇시스템학회논문지
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• 제8권12호
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• pp.1056-1060
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• 2002

Model-based tuning rules of the PID controller are proposed incorporating with real-coded genetic algorithms. The optimal parameter sets of the PID controller for step set-point tracking are obtained based on the first-order time delay model and a real-coded genetic algorithm as an optimization tool. As for assessing the performance of the controllers, performance indices(ISE, IAE and ITAE) are adopted. Then tuning rules are derived using the tuned parameter sets, potential rule models and another real-coded genetic algorithm A set of simulation works is carried out to verify the effectiveness of the proposed rules.

• Journal of Ship and Ocean Technology
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• 제9권4호
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• pp.35-46
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• 2005

The performance of optimisation methods, based on penalty functions, is highly problem- dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm (R$\mu$GA) is proposed to find the global optimum of continuous and/or discrete nonlinear constrained engineering problems without handling any of penalty functions. R$\mu$GA can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. The proposed R$\mu$GA approach has been demonstrated by solving three different engineering design problems. From the simulation results, it has been concluded that R$\mu$GA is an effective global optimisation tool for solving continuous and/or discrete nonlinear constrained real­world optimisation problems.

• Journal of Advanced Marine Engineering and Technology
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• 제31권7호
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• pp.863-871
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• 2007

This paper proposes a methodology to estimate the system coefficients for the infinite impulse response(IIR) digital filters using real code GA. In the traditional real coded GA, it adapts the general genetic operations, whereas in this paper the proposed real coded GA applies improved genetic operations in order to search the optimal solution in given problems. Each of unknown IIR digital coefficients collected as forms of a chromosome. Two illustrative examples including the band pass and band stop IIR digital filters are demonstrated to verify the proposed method.

• Journal of Advanced Marine Engineering and Technology
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• 제31권5호
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• pp.599-605
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• 2007

This paper presents a method for identifying various systems based on input-output data and a real-coded genetic algorithm(RCGA). The advantages of this technique are, first, it is not dependent on the deterministic or stochastic nature of the systems and, second, the globally optimized models for the original systems can be identified without the need of a differentiable measure function of linearly separable parameters. Under suitable hypotheses, the estimation error is shown to converge in probability to zero. The performance of the proposed algorithm is demonstrated through several simulations.

• 수산해양기술연구
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• 제39권4호
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• pp.284-290
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• 2003

It is very important to minimize the weight of shaft from the viewpoint of economics and manufacture. For minimizing effectively the diameter of shaft in torsional shafting, authors developed computer program using the real-coded genetic algorithm which is one of optimizing techniques and based on real coding representation of genetic algorithm. In order to confirm the accuracy and effectiveness of the developed computer program, the computational results by the developed program were compared with those of conventional strength, stiffness and vibration designs for a generator shafting.

• Journal of Advanced Marine Engineering and Technology
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• 제32권5호
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• pp.785-790
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• 2008

A technique for determining the feedback gain of the states feedback controller using a real-coded genetic algorithm(RCGA) is presented. It is concerned with the states error to the performance index of a RCGA. As for assessing the performance of the controller three performance criteria (ISE. IAE and ITAE) are adopted. And designing the controller involves a constrained optimization problem. Therefore a real-coded genetic algorithm incorporating the penalty strategy is used. The performance of the proposed method is demonstrated through a set of simulation about an inverted pendulum system.

• 제어로봇시스템학회논문지
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• 제6권4호
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• pp.268-275
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• 2000

The increasing technological demands of today call for complex systems, which in turn involve a series of optimization problems with some equality or inequality constraints. In this paper, we presents a real-coded genetic algorithm(RCGA) as an optimization tool which is implemented by three genetic operators based on real coding representation. Through a lot of simulation works, the optimum settings of its control parameters are obtained on the basis of global off-line robustness for use in off-line applications. Two optimization problems are Presented to illustrate the usefulness of the RCGA. In case of a constrained problem, a penalty strategy is incorporated to transform the constrained problem into an unconstrained problem by penalizing infeasible solutions.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1645-1650
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• 2005

FOPDT(First-Order Plus Dead-Time) and SOPDT(Second-Order Plus Dead-Time) process, which are used as the most useful process in industry, are difficult about process identification because of the long dead-time problem and the model mismatch problem. Thus, the accuracy of process identification is the most important problem in FOPDT and SOPDT process control. In this paper, we proposed the real-coded genetic algorithm for identification of FOPDT and SOPDT processes. The proposed method using real-coding genetic algorithm shows better performance characteristic comparing with the existing an area-based identification method and a directed identification method that use step-test responses. The proposed strategy obtained useful result through a number of simulation examples.

• 한국지반공학회논문집
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• 제19권6호
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• pp.99-106
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• 2003

본 연구에서는 장비하중이 고려된 폐기물 매립지 라이닝 시스템의 단면 설계를 위하여 제안된 개별요소법에 근거한 안정해석 기법과 실수형 유전자 알고리즘이 적용된 새로운 최적화 설계기법이 제안되었다. 개별요소법에 근거하여 제안된 해석기법을 장비하중이 적용된 폐기물 매립지의 라이닝 시스템 해석에 적용함으로써, 라이닝 시스템의 구성요소 중의 하나인 덮개 흙의 단면 변화에 따른 영향이 검토되었다. 또한 폐기물 매립 용량을 최대화할 수 있는 매립지라이닝 사면 형상을 결정하기 위하여, 실수형 유전자 알고리즘에 근거한 최적화 과정을 통한 최적설계 예제해석이 수행되었다.