• 대한기계학회논문집A
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• 제24권5호
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• pp.1193-1202
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• 2000

The paper describes the use of genetic algorithms (GA's) to the minimum weight design of stiffened composite panels for buckling constraints. The proposed design problem is characterized by mixture of continuous and discrete design variables corresponding to panel elements and stacking sequence of laminates, respectively. Design space is multimodal and non-convex, thereby introducing the need for global search strategies. Advanced strategies in GA's such as directed crossover, multistage search and separated crossover are adopted to improve search ability and to save computational resource requirements. The paper explores the effectiveness of genetic algorithms and their advanced strategies in designing stiffened composite panels under various uniaxial compressive load conditions and the linrlit on stacking sequence of laminates.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.698-702
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• 1992

In this paper, Micro-Genetic algorithms(.mu.-GAs) is proposed on a minimum-time path planning for robot manipulator, which is a kind of optimization algorithm. The minimum-time path planning, which can allow the robot system to perform the demanded tasks with a minimum execution time, may be of consequence to improve the productivity. But most of the methods proposed till now suffers from a significant computation burden and can't often find the optimal values. One way to overcome such difficulties is to apply the Micro-Genetic Algorithms, which can allow to find the optimal values, to the minimum-time problem. This paper propose an approach for solving the minimum-time path planning by using Micro-Genetic Algorithms. The effectiveness of the proposed method is demonstrated using the 2 d.o.f plannar Robot manipulator.

• 전자공학회논문지B
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• 제31B권4호
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• pp.40-47
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• 1994

In this paper, Micro-Genetic algorithms($\mu$-GAs) is proposed on a minimum-time path planning for robot manipulator. which is a kind of optimization algorithm. The minimum-time path planning, which can allow the robot system to perform the demanded tasks with a minimum execution time, may be of consequence to improve the productivity. But most of the methods proposed till now suffers from a significant computation burden and can`t often find the optimaul values. One way to overcome such difficulties is to apply the Micro-Genetic Algorithms, which can allow to find the optimul values, to the minimum-time problem. This paper propose an approach for solving the minimum-time path planning by using Micro-Genetic Algorithms. The effectiveness of the proposed method is demonstrated using the 2 d.o.f plannar Robot manipulator.

• 자원환경지질
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• 제28권4호
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• pp.425-431
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• 1995

Genetic algorithms are so named because they are analogous to biological processes. The model parameters are coded in binary form. The algorithm then starts with a randomly chosen population of models called chromosomes. The second step is to evaluate the fitness values of these models, measured by a correlation between data and synthetic for a particular model. Then, the three genetic processes of selection, crossover, and mutation are performed upon the model in sequence. Genetic algorithms share the favorable characteristics of random Monte Carlo over local optimization methods in that they do not require linearizing assumptions nor the calculation of partial derivatives, are independent of the misfit criterion, and avoid numerical instabilities associated with matrix inversion. An additional advantage over converntional methods such as iterative least squares is that the sampling is global, rather than local, thereby reducing the tendency to become entrapped in local minima and avoiding the dependency on an assumed starting model.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.252-254
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• 2006

The recent improvement in the performance of digital processor, the application of control technology, which used in the HVDC(High Voltage Direct Current) system with the digital processors, has increased. Having this research development as the basis, this paper presents an achievement of progression by tuning the parameter of PI controller based on Genetic Algorithms(GAs) and by controlling with PI controller with a developed simulator by applying the Matrix operating function, voltage source switching element, modified nodal analysis which can include transformer and the backward Euler which does not create the problem of numerical oscillation. As a result, I expect this development in the simulator HVDC System to bring more application in the field of control technology research with an expanded practicality.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.104-106
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• 2007

In this paper, we propose an approach to design of optimized Cascade controller for Rotary Inverted Pendulum system using Hierarchical Fair Competition-based Genetic Algorithm(HFCGA). GAs may get trapped in a sub-optimal region of the search space thus becoming unable to find better quality solutions, especially for very large search space. The Parallel Genetic Algorithms(PGA) are developed with the aid of global search and retard premature convergence. HFCGA is a kind of multi-populations of PGA. In this paper, we design optimized Cascade controller by HFCGA for Rotary Inverted Pendulum system that is nonlinear and unstable. Cascade controller comprise two feedback loop, parameters of controller optimize using HFCGA. Then designed controller evaluate by apply to the real plant.

• 한국경영과학회지
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• 제14권2호
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• pp.47-47
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• 1989

This paper considers the parallel machines scheduling problem characterized as a multi-objective combinatorial problem. As this problem belongs to the NP-complete problem, genetic algorithms are applied instead of the traditional analytical approach. The purpose of this study is to show how the problem can be effectively solved by using genetic algorithms with a permutation approach. First, a permutation representation which can effectively represent the chromosome is introduced for this problem . Next, a schedule builder which employs the combination of scheduling theories and a simple heuristic approach is suggested. Finally, through the computer experiments of genetic algorithm to test problems, we show that the niche formation method does not contribute to getting better solutions and that the PMX crossover operator is the best among the selected four recombination operators at least for our problem in terms of both the performance of the solution and the operational convenience.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
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• pp.81-84
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• 1998

Genetic Algorithms (GAs) are stochastic algorithms whose search methods model some natural phenomena. The procedure of GAs may be divided into two sub-procedures : Operation and Selection. Chromosomes can produce new offspring by means of operation, and the fitter chromosomes can produce more offspring than the less fit ones by means of selection. However, operation which is executed randomly and has some limits to its execution can not guarantee to produce fitter chromosomes. Thus, we propose a method which gives a directional information to the genetic operator by reinforcement learning. It can be achived by using neural networks to apply reinforcement learning to the genetic operator. We use the amount of fitness change which can be considered as reinforcement signal to calcualte the error terms for the output units. Then the weights are updated using backpropagtion algorithm. The performance improvement of GAs using reinforcement learning can be measured by applying the pr posed method to GA-hard problem.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권3호
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• pp.253-258
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informal speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.131-138
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• 2022

A Mobile Ad-hoc Network (MANET) is a collection of moving nodes that communicate and collaborate without relying on a pre-existing infrastructure. In this type of network, nodes can freely move in any direction. Routing in this sort of network has always been problematic because of the mobility of nodes. Most existing protocols use simple routing algorithms and criteria, while another important criterion is path selection. The existing protocols should be optimized to resolve these deficiencies. 'Particle Swarm Optimization (PSO)' is an influenced method as it resembles the social behavior of a flock of birds. Genetic algorithms (GA) are search algorithms that use natural selection and genetic principles. This paper applies these optimization models to the OLSR routing protocol and compares their performances across different metrics and varying node sizes. The experimental analysis shows that the Genetic Algorithm is better compared to PSO. The comparison was carried out with the help of the simulation tool NS2, NAM (Network Animator), and xgraph, which was used to create the graphs from the trace files.