• Journal of Environmental Impact Assessment
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• v.15 no.6
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• pp.435-441
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• 2006

For the calibration of rainfall-runoff model, automatic calibration methods are used instead of manual calibration to obtain the reliable modeling results. When mathematical programming techniques such as linear programming and nonlinear programming are applied, there is a possibility to arrive at the local optimum. To solve this problem, genetic algorithm is introduced in this study. It is very simple and easy to understand but also applicable to any complicated mathematical problem, and it can find out the global optimum solution effectively. The objective of this study is to develope a parameter optimization program that integrate a genetic algorithm and a rainfall-runoff model. The program can calibrate the various parameters related to the runoff process automatically. As a rainfall-runoff model, SWMM is applied. The automatic calibration program developed in this study is applied to the Jangcheon watershed flowing into the Youngrang Lake that is in the eutrophic state. Runoff surveys were carried out for two storm events on the Jangcheon watershed. The peak flow and runoff volume estimated by the calibrated model with the survey data shows good agreement with the observed values.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.4
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• pp.113-128
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• 2016

This paper proposes a Bi-level Genetic Algorithm for the Fixed Charge Transportation Problem with Non-linear Unit Cost. The problem has the property of mixed integer program with non-linear objective function and linear constraints. The bi-level procedure consists of the upper-GA and the lower-GA. While the upper-GA optimize the connectivity between each supply and demand pair, the lower-GA optimize the amount of transportation between the pairs set to be connected by the upper-GA. In the upper-GA, the feasibility of the connectivity are verified, and if a connectivity is not feasible, it is modified so as to be feasible. In the lower-GA, a simple method is used to obtain a pivot feasible solution under the restriction of the connectivity determined by the upper-GA. The obtained pivot feasible solution is utilized to generate the initial generation of chromosomes. The computational experiment is performed on the selected problems with several non-linear objective functions. The performance of the proposed procedure is analyzed with the result of experiment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.397-406
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• 2000

Genetic algorithm is used as a means of search, optimization md machine learning, its structure is simple but it is applied to various areas. And it is about an active and effective controller which can flexibly prepare for changeable circumstances. For this study, research about an action base system evolving by itself is also being considered. There is to have a problem that depended entirely on heuristic knowledge of expert forming membership function and control rule for fuzzy controller design. In this paper, for forming the fuzzy control to perform self-organization, we tuned the membership function to the most optimal using a genetic algorithm(GA) and improved the control efficiency by the self-correction and generation of control rules.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.462-466
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• 2002

Genetic Algorithm (GA) which imitates the process of nature evolution is applied to various fields because it is simple to theory and easy to application. Recently applying GA to hardware, it is to proceed the research of Evolvable Hardware(EHW) developing the structure of hardware and reconstructing it. And it is growing a necessity of GAP that embodies the computation of GA to the hardware. Evolving by GA don't act in the software but in the hardware(GAP) will be necessary for the design of independent EHW. This paper shows the design GAP for fast reconfiguration of EHW.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.530-536
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• 2008

Localization is one of the fundamental problems in wireless sensor networks (WSNs) that forms the basis for many location-aware applications. Localization in WSNs is to determine the position of node based on the known positions of several nodes. Most of previous localization method use triangulation or multilateration based on the angle of arrival (AOA) or distance measurements. In this paper, we propose an enhanced centroid localization method based on edge weights of adjacent nodes using fuzzy modeling and genetic algorithm when node connectivities are known. The simulation results shows that our proposed centroid method is more accurate than the simple centroid method using connectivity only.

• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.65-74
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• 2020

Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.

• Journal of the Korea Society of Computer and Information
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• v.20 no.10
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• pp.61-68
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• 2015

This paper deals with the pre-marshalling problem that the containers of container yard at container terminal are relocated in consensus sequence of loading schedule of container vessel. This problem is essential to improvement of competitive power of terminal. This problem has to relocate the all of containers in a bay with minimum number of movement. There are various algorithms such as metaheuristic as genetic algorithm and heuristic algorithm in order to find the solution of this problem. Nevertheless, there is no unique general algorithm that is suitable for various many data. And the main drawback of metaheuristic methods are not the solution finding rule but can be find the approximated solution with many random trials and by coincidence. This paper can be obtain the solution with O(m) time complexity that this problem deals with bin packing problem for m stack bins with descending order of take out ranking. For various experimental data, the proposed algorithm can be obtain the optimal solutions for all of data. And to conclude, this algorithm can be show that most simple and general algorithm with simple optimal solution finding rule.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2C
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• pp.10-15
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• 2005

In this paper, a simple but fast and reliable technique for the complex dielectric constant measurement of non-magnetic materials is introduced using a measured transmission coefficient (S21) and a genetic algorithm as an inversion process at microwave frequencies. In this experiment, it has been found that the transmission method is less susceptible with the measurement errors than that of the reflection method and the genetic algorithm can be efficiently used as a search technique. The suggested technique is validated with known and unknown conductor-loaded lossy materials and the conductor-loaded PCB at X-band.

• Korean Management Science Review
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• v.14 no.2
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• pp.47-61
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• 1997

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.

• Journal of the Korean Operations Research and Management Science Society
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• v.14 no.2
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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.