• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.9
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• pp.365-375
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• 2006

This paper is presented by the application of parallel adaptive evolutionary algorithm(PAEA) to search an optimal solution of a thermal unit commitment problem. The adaptive evolutionary algorithm(AEA) takes the merits of both a genetic algorithm(GA) and an evolution strategy(ES) in an adaptive manner to use the global search capability of GA and the local search capability of ES. To reduce the execution time of AEA, the developed algorithm is implemented on an parallel computer which is composed of 16 processors. To handle the constraints efficiently and to apply to Parallel adaptive evolutionary algorithm(PAEA), the states of thermal unit are represented by means of real-valued strings that display continuous terms of on/off state of generating units and are involved in their minimum up and down time constraints. And the violation of other constraints are handled by repairing operator. The procedure is applied to the $10{\sim}100$ thermal unit systems, and the results show capabilities of the PAEA.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1624-1627
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• 1997

The 6-DOF parallel manipulator is a closed-kindmatic chain robot manipulator that is capable of providing high structural rigidity and positional accuracy. Because of its advantage, the parallel manipulator have been widely used in many engineering applications such as vehicle/flight driving simulators, rogot maniplators, attachment tool of machining centers, etc. However, the kinematic analysis for the implementation of a real-time controller has some problem because of the lack of an efficient lagorithm for solving its highly nonliner forward kinematic equation, which provides the translational and orientational attitudes of the moveable upper platform from the lenght of manipulator linkages. Generally, Newton-Raphson method has been widely sued to solve the forward kinematic problem but the effectiveness of this methodology depend on how to set initial values. This paper proposes a hybrid method using genetic algorithm(GA) and Newton-Raphson method to solve forward kinematics. That is, the initial values of forward kinematics solution are determined by adopting genetic algorithm which can search grobally optimal solutions. Since determining this values, the determined values are used in Newton-Raphson method for real time calcuation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.3
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• pp.24-29
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• 2009

In case of the optimization problems that have many design variables, the conventional genetic algorithms(GA) fall into a trap of local minima with high probability. This problem is called the premature convergence problem. To overcome it, the parallel genetic algorithms which adopt the migration mechanism have been suggested. But it is hard to determine the several parameters such as the migration size and the migration interval for the parallel GAs. Therefore, we propose a new method to determine the migration interval automatically in this paper. To verify its validity, it is applied to some traditional mathematical optimization problems and is compared with the conventional parallel GA. It is also applied to the optimal design of the brushless DC motor for an electric wheel chair which is a real world problem and has five design variables.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.125-139
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• 2004

Generally, parallel redundancy is used to improve reliability in many systems. However, redundancy increases system cost, weight, volume, power, etc. Due to limited availability of these resources, the system designer has to maximize reliability subject to various constraints or minimize resources while satisfying the minimum requirement of system reliability. This paper presents GAs (Genetic Algorithms) to solve redundancy allocation in series-parallel systems. To apply the GAs to this problem, we propose a genetic representation, the method for initial population construction, evaluation and genetic operators. Especially, to improve the performance of GAs, we develop heuristic operators (heuristic crossover, heuristic mutation) using the reliability-resource information of the chromosome. Experiments are carried out to evaluate the performance of the proposed algorithm. The performance comparison between the proposed algorithm and a pervious method shows that our approach is more efficient.

• Journal of the Korea Safety Management & Science
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• v.3 no.3
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• pp.191-200
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• 2001

Parallel Genetic Algorithms partition the whole population into several sub-populations and search the optimal solution by exchanging the information each others periodically. Distributed Genetic Algorithm, one of Parallel Genetic Algorithms, divides a large population into several sub-populations and executes the traditional Genetic Algorithm on each sub-population independently. And periodically promising individuals selected from sub-populations are migrated by following the migration interval and migration rate to different sub-populations. In this paper, for the Travelling Salesman Problems, we analyze and compare with Distributed Genetic Algorithms using different Genetic Algorithms and using same Genetic Algorithms on each separated sub-population The simulation result shows that using different Genetic Algorithms obtains better results than using same Genetic Algorithms in Distributed Genetic Algorithms. This results look like the property of rapidly searching the approximated optima and keeping the variety of solution make interaction in different Genetic Algorithms.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.29-32
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• 2001

In this paper, we applied the high-performance PGAP(Parallel Genetic Algorithm Processor) to recognizing rotated pattern. In order to perform this research efficiently, we used Multi-PGAP system consisted of four PGAP. In addition, we used mental rotation based on the rotated pattern recognition mechanism of human to reduce the number of operation. Also, we experimented with distinguishing specific pattern from similar coin patterns and determine rotated angle between patterns. The result showed that the development of future artificial recognition system is feasible by employing high performance PGAPS.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.161-164
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• 2000

We propose the genetic algorithm to apply three kinds of FFT data flows to be considered the overhead for the data exchange between processors that have the multi-scheduling problem on parallel computer In the design of genetic algorithm, we propose the chromosome representation which can simply encode and decode a solution without any heuristic information, the evaluation function to be considered an efficiency of processor, and the genetic operator to inherit a superior gene from their parents. And we saw that the simulation result can verify better performance than the existing algorithm(BEA : binary exchange algorithm)in the face of execution time.

• Journal of Mechanical Science and Technology
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• v.18 no.7
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• pp.1121-1130
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• 2004

The Genetic Algorithm (GA), an optimization technique based on the theory of natural selection, has proven to be a relatively robust means of searching for global optimum. It converges to the global optimum point without auxiliary information such as differentiation of function. In the case of a complex problem, the GA involves a large population number and requires a lot of computing time. To improve the process, this research used parallel processing with several personal computers. Parallel process technique is classified into two methods according to subpopulation's size and number. One is the fine-grained method (FGM), and the other is the coarse-grained method (CGM). This study selected the CGM as a parallel process technique because the load is equally divided among several computers. The given design domain should be reduced according to the degree of feasibility, because mechanical system problems have constraints. The reduced domain is used as an initial design domain. It is consistent with the feasible domain and the infeasible domain around feasible domain boundary. This parallel process used the Message Passing Interface library.

• Journal of the Korea Safety Management & Science
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• v.25 no.2
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• pp.1-8
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• 2023

This paper is proposing a novel machine scheduling model for the unrelated parallel machine scheduling problem without setup times to minimize the total completion time, also known as "makespan". This problem is a NP-complete problem, and to date, most approaches for real-life situations are based on the operator's experience or simple heuristics. The new model based on the Memetic Algorithm, which was proposed by P. Moscato in 1989, is a hybrid algorithm that includes genetic algorithm and local search optimization. The new model is tested on randomly generated datasets, and is compared to optimal solution, and four scheduling models; three rule-based heuristic algorithms, and a genetic algorithm based scheduling model from literature; the test results show that the new model performed better than scheduling models from literature.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.2
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• pp.183-192
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• 2000

This paper considers the problem of scheduling a set of n jobs on m parallel machines to minimize total weighted tardiness. For the problem a genetic algorithm is proposed, in which solutions are encoded using the random key method suggested by Bean and new crossover operators are employed to increase performance of the algorithm. The algorithm is compared with the Modified Due-Date (MDD) algorithm after series of tests to find appropriate values for genetic parameters. Results of computational tests on randomly generated test problems show that the suggested algorithm performs better than the MDD algorithm and gives good solutions in a reasonable amount of computation time.