• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2479-2481
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• 2000

It is the time-varying dissolved oxygen(DO) dynamics that requires controlling for maintaining the DO concentration in the aeration tank. Many linear controllers have thus been applied. Because of the nonlinearity of the oxygen transfer function together with the time-varying respiration rate, however, the linear controllers are found to poorly perform in many cases. To overcome this limitation, a number of advanced controlling techniques have been developed and applied. In this study, designed GA-PI Controller using genetic algorithm(GA). Genetic algorithms(GAs) are search algorithms based on the mechanics of natural selection and natural genetics. As result of computer simulation, GA-PI controller shows the better control performance especially under the condition of the continuously changing DO set-point. This result represents that GA-PI controller can be a good measure to control the DO concentration in the SBR process which requires the sequential DO set-point change to accomplish the nitrification and denitrification in a single reactor.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2002년도 봄 학술발표회 논문집
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• pp.225-232
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• 2002

Simple genetic algorithm(SGA) has been used to optimize a lot of structural optimization problems because it can optimize non-linear problems and obtain the global solution. But, because of large evolving populations during many generations, it takes a long time to calculate fitness. Therefore this paper applied micro-genetic algorithm(μ -GA) to structural optimization and compared results of μ -GA with results of SGA. Additionally, the Paper applied μ -GA to gate optimization problem for injection molds by using simulation program CAPA.

• 한국멀티미디어학회논문지
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• 제9권1호
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• pp.119-126
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• 2006

멀티 프로세서 시스템에서 스케쥴링은 매우 중요한 부분이지만, 최적의 해를 구하는 것이 복잡하여 최근 다양한 휴리스틱 방법들에 의한 스케쥴링 알고리즘들이 제안되고 있다. 본 논문에서는 유전 알고리즘을 이용한 새로운 스케쥴링 알고리즘을 제시한다. 또한, 해를 구하는 과정에서 시뮬레이티드 어닐링 (simulated annealing)의 확률을 이용하여 유전 알고리즘의 성능을 개선시킨다. 제시된 알고리즘은 태스크들의 최종 수행 완료 시간 (makespan)을 최소화하는 것을 목표로 한다. 모의 실험을 통하여 제시된 알고리즘이 다른 알고리즘보다 최종 수행 완료 시간이 작음을 확인할 수 있었다.

• 대한전기학회논문지:전력기술부문A
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• 제48권4호
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• pp.388-394
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• 1999

This paper is concerned with an application of Parallel Genetic Algorithms(PGA) to optimal econmic load dispatch(ELD) in power systems. The ELD problem is to minimize the total generation fuel cost of power outputs for all generating units while satisfying load balancing constraints. Genetic Algorithms(GA) is a good candidate for effective parallelization because of their inherent principle of evolving in parallel a population of individuals. Each individual of a population evaluates the fitness function without data exchanges between individuals. In application of the parallel processing to GA, it is possible to use Single Instruction stream, Multiple Data stream(SIMD), a kind of parallel system. The architecture of SIMD system need not data communications between processors assigned. The proposed ELD problem with C code is implemented by SIMSCRIPT language for parallel processing which is a powerfrul, free-from and versatile computer simulation programming language. The proposed algorithms has been tested for 38 units system and has been compared with Sequential Quadratic programming(SQP).

• 소음진동
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• 제11권1호
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• pp.147-155
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• 2001

This paper is concerned with the real-time automatic tuning of the multi-input multi-output positive position feedback controllers for smart structures by the genetic algorithms. The genetic algorithms have proven its effectiveness in searching optimal design parameters without falling into local minimums thus rendering globally optimal solutions. The previous real-time algorithm that tunes a single control parameter is extended to tune more parameters of the MIMO PPF controller. We employ the MIMO PPF controller since it can enhance the damping value of a target mode without affecting other modes if tuned properly. Hence, the traditional positive position feedback controller can be used in adaptive fashion in real time. The final form of the MIMO PPF controller results in the centralized control, thus it involves many parameters. The bounds of the control Parameters are estimated from the theoretical model to guarantee the stability. As in the previous research, the digital MIMO PPF control law is downloaded to the DSP chip and a main program, which runs genetic algorithms in real time, updates the parameters of the controller in real time. The experimental frequency response results show that the MIMO PPF controller tuned by GA gives better performance than the theoretically designed PPF. The time response also shows that the GA tuned MIMO PPF controller can suppress vibrations very well.

• 한국공간정보시스템학회:학술대회논문집
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• 한국공간정보시스템학회 2004년도 춘계 학술발표회 논문집 제1권1호(통권1호)
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• pp.43-52
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• 2004

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algerian was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1995년도 봄 학술발표회 논문집
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• pp.187-194
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• 1995

Traditional genetic algorithms(GA) have mostly used binary code for representing design variable. The binary code GA has many difficulties to solve optimization problems with continuous design variables because of its targe computer core memory size, inefficiency of its computing time, and its bad performance on local search. In this paper, a real code GA is proposed for dealing with the above problems. So, new crossover and mutation processes of read code GA are developed to use continuous design variables directly. The results of real code GA are compared with those of binary code GA for several single and multiple objective optimization problems. As results of comparisons, it is found that the performance of the real code GA is better than that of the binary code GA, and concluded that the rent code GA developed here can be used for the general optimization problem.

• 대한전기학회논문지:전력기술부문A
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• 제48권9호
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• pp.1088-1095
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• 1999

This paper presents short-term hydro scheduling method for hydrothermal coordination by genetic algorithms. Hydro scheduling problem has many constraints with fixed final reservoir volume. In this paper, the difficult water balance constraints caused by hydraulic coupling satisfied throughout dynamic decoding method. Adaptive penalizing method was also proposed to handle the infeasible solutions that violate various constraints. In this paper, we proposed GA to solve hydrothermal scheduling with appropriate decoding method and dynamic penalty method. The effectiveness of the proposed method is demonstrated in the case study.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2004년도 추계학술대회
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• pp.179-188
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• 2004

Bankruptcy prediction is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. Recently, support vector machine (SVM) has been applied to the problem of bankruptcy prediction. The SVM-based method has been compared with other methods such as neural network, logistic regression and has shown good results. Genetic algorithm (GA) has been increasingly applied in conjunction with other AI techniques such as neural network, CBR. However, few studies have dealt with integration of GA and SVM, though there is a great potential for useful applications in this area. This study proposes the methods for improving SVM performance in two aspects: feature subset selection and parameter optimization. GA is used to optimize both feature subset and parameters of SVM simultaneously for bankruptcy prediction.

• 설비공학논문집
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• 제16권2호
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• pp.111-120
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• 2004

The optimal design method of indoor thermal environment using CFD coupled simulation and genetic algorithms (GA) is developed in this study. CFD could analyze the thermal environment considering the distribution of temperature, velocity, etc. in a room. Therefore, It would be appropriate to use CFD for the optimal design method considering their distribution. In this paper, the optimal design means the most appropriate boundary conditions of the room among the conditions where the design target of indoor therm environment is achieved. Two step optimal indoor thermal environment design method is proposed. It includes the GA for searching the optimal indoor thermal environment design. To examine the performance of this method, the optimal design of hybrid ventilation system, which uses the natural cross ventilation and the radiation-cooling panel is conducted. The optimal design which satisfies the design target (thermal comfort, minimum cooling load, minimum vertical temperature difference) is found using two step optimal design method.