• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.837-839
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• 2003

Low spatial resolution remote sensing (RS) data (LSRD) are promising in agricultural monitoring activities due to their high temporal resolution, but under such a spatial resolution, mixing in a pixel is a common problem. In this study, a numerical experiment was conducted to explore a mixed pixel problem in agriculture using a combined RSsimulation model SWAP (Soil-Water-Atmosphere -Plant) and a Genetic Algorithm (GA) approach. Results of the experiments showed that it is highly possible to address the mixed pixel problem with LSRD.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.11a
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• pp.131-142
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• 2000

Facility layout is the early stage of system design that requires a mid-term or long-term plan. Since improper facility layout might incur substantial logistics cost including material handling and re-installment costs, due consideration must be given to decisions on facility layout. Facility layout is concerned with low to arrange equipment necessary for production in a given space. Its objective is to minimize the sum of all the products of each equipment's amount of flow multiplied by distance. Facility layout also is related to the issue of NP-complete, i.e., calculated amounts exponentially increase with the increase of the number of equipment. This study discusses Hybrid GA developed, as an algorithm for facility layout, to solve the above-mentioned problems. The algorithm, which is designed to efficiently place equipment, automatically produces a horizontal passageway by the block, if a designer provides the width and length of the space to be handled. In addition, this study demonstrates the validity of the Algorithm by comparing with existing algorithms that have been developed. We present a Hybrid GA approach to the facility layout problem that improves on existing work in terms of solution quality and method. Experimental results show that the proposed algorithm is able to produce better solution quality and more practical layouts than the ones obtained by applying existing algorithms.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.21-23
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• 2003

Developing a public transportation guidance system requires the following aspects: (i) people may change transportation means not only within the same type but also among different modes such as between buses and subways, and (ii) the system should take into account the time taken in transfer from one mode to the other. This study suggest the framework for developing a public transportation guidance system that generates optimized paths in the transportation network of mixed means including buses, subways and other modes. For this study, the Genetic Algorithms are used to find the best routes that take into account transfer time and other servicetime constraints.

• Korean Journal of Optics and Photonics
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• v.33 no.1
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• pp.11-21
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• 2022

We have investigated the optimal design of an aperiodic optical phased array (OPA) for use in light detection and ranging applications. Three optimization algorithms - particle-swarm optimization (PSO), a genetic algorithm (GA), and a pattern-search algorithm (PSA) - were employed to obtain the optimal arrangement of optical antennas comprising an OPA. The optimization was performed to obtain the minimal side-lobe level (SLL) of an aperiodic OPA at each steering angle, using the three optimization algorithms. It was found that PSO and GA exhibited similar results for the SLL of the optimized OPA, while the SLL obtained by PSA showed somewhat different features from those obtained by PSO and GA. For an OPA optimized at a steering angle <45°, the SLL value averaged over all steering angles increased as the angle of optimization decreased. However, when the angle of optimization was larger than 45°, low average SLL values of <13 dB were obtained for all three optimization algorithms. This implies that an OPA with high signal quality can be obtained when the arrangement of the optical antennas is optimized at a large steering angle.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.6
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• pp.316-324
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• 2003

This paper applied Elite-based Simplex-GA hybrid approach combined with Muptipop-GA (ESGA) to determining the location, size and number of capacitors to improve voltage profile and minimize power losses in unbalanced distribution systems. One of the main obstacles in applying GA to complex problems has been the high computational cost due to their slow convergence rate. To alleviate this difficulty, ESGA approach was developed that combines Elite-based Simplex-GA hybrid approach with Muptipop-GA. The objective function formulated consists of two terms: cost for energy losses and cost related to capacitor purchase and capacitor installation. The cost function associated with capacitor placement is considered as a step function due to banks of standard discrete capacities. Its efficiency was proved through the application in IEEE 13 bus and 34 bus test systems and was compared with several methods using GA.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2269-2275
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• 2010

The performance of Genetic Algorithms (GA) is affected by various factors such as parameters, genetic operators and strategies. The traditional approach with random initial population is efficient however the whole initial population may contain many infeasible solutions. Thus it would take a long time for GA to produce a good solution. The GA have been modified in various ways to achieve faster convergence and it was particularly recognized by researchers that initial population greatly affects the performance of GA. This study proposes modified GA with sorted initial population and applies it to solving Travelling Salesman Problem (TSP). Normally, the bigger the initial the population is the more computationally expensive the calculation becomes with each generation. New approach allows reducing the size of the initial problem and thus achieve faster convergence. The proposed approach is tested on a simulator built using object-oriented approach and the test results prove the validity of the proposed method.

• Computers and Concrete
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• v.6 no.4
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• pp.343-356
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• 2009

Experiments were designed to investigate the flow behavior of portland cement paste and concrete incorporating superplasticizers. The paste and concrete mixtures were subjected to prolonged mixing for up to 110 min at high temperature. The yield stress values of concrete and that of the corresponding cement paste were measured using a rotating rheometer and viscometer, respectively. The results reveal a weak linear correlation between the yield stress of concrete mixtures and that of the corresponding cement pastes. Results also indicate that the yield stress of concrete varies in a linear fashion with the elapsed time, while its variations with the temperature and superplasticizer dosage follow power and inverse power functions, respectively. In this study, the genetic algorithms (GA) technique was used to predict the yield stress of concrete considering various parameters, such as the mixing time, ambient temperature, and superplasticizer dosage. A sensitivity study was conducted to evaluate the ability of the GA equations thus developed to capture the effects of test parameters on the yield stress of concrete. It was found that the GA equations were sensitive to the effects of test parameters and provided yield stress predictions that compared well with corresponding experimental data.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.862-864
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• 1996

This paper presents a new approach to evaluate reliability indices of electric distribution systems using genetic algorithm(GA). The use of reliability evaluation is an important aspect of distribution system planning and operation to adjust the reliability level of each area. In this paper, the reliability model is based on the optimal load transferring problem to minimize over load generated load point outage in each sub-section. This kind of the approach is one of the most difficult procedure which becomes a combination problems. A new approach using GA Was developed for this problem. We proposed a tree search algorithm which satisfied the tree constraint. GA is general purpose optimization techniques based on principles inspired from the biological evolution such as natural selection, genetic recombination and survival of the fittest Test results for the model system with 24 nodes and 29 branches are reported in the paper.

• Transactions of Materials Processing
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• v.30 no.3
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• pp.125-133
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• 2021

In this study, response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goal of optimization is to determine the maximum forming angle and minimum surface roughness, while varying the production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model and BPNN model to model the variations in the forming angle and surface roughness based on variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process the GA. The results showed that RSM and BPNN can be effectively used to control the forming angle and surface roughness. The optimized Pareto front produced by the GA can be utilized as a rational design guide for practical applications of AA5052 in the ISF process

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.634-639
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• 1998

A methodology based on the concept of variable string length GA(VGA) is developed for determining automatically the number of hyperplanes and their appropriate arrangement for modeling the class boundaries of a given training data set in RN. The genetic operators and fitness functionare newly defined to take care of the variability in chromosome length. Experimental results on different artificial and real life data sets are provided.