• Journal of IKEEE
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• v.19 no.2
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• pp.225-231
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• 2015

The parameter K which decides how much to convert wind energy to electric energy in MPPT(maximum power point tracking) control of wind turbine system using torque controller is changed because blade shape and air density change. If the parameter K is not optimal value, power lose occur. The changed parameter K is important issue in wind turbine system. In this paper, to solve this problem, considering wind turbine system using back-to-back converter control and torque control, we propose the adaptive MPPT algorithm which performs fast control by using initial K, estimates mechanical power using Kalman filter method, uses the estimated mechanical power as input for MPPT algorithm again, and consequently performs optimal MPPT control.

• Journal of Korea Water Resources Association
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• v.32 no.6
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• pp.711-720
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• 1999

In this study, a various rainfall-runoff modelling approaches have been applied to the runoff response of flood hydrograph in three experimental watershed of the western part of korea. Mathematical models of runoff response also have been studied including linear system theory based on modeling techniques. Eight models were operated at the five water level gauging stations and the parameters of each model were computed by the Rosenbrock's hill climbing method to minimize the objective function. For the parameter verification of the models, a different complex rainfall-runoff event was selected in the same of the three river basins and derived IUH of the each model could be calibrated. Furthermore multiple regressions of the logarithmic transformation method between model parameters and catchment characteristics were studied in the selected five station.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.47-55
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• 2010

Linear constraint satisfaction optimization problem is a kind of combinatorial optimization problem involving linearly expressed objective function and complex constraints. Integer programming is known as a very effective technique for such problem but require very much time and memory until finding a suboptimal solution. In this paper, we propose a method to improve the search performance by integrating local search and integer programming. Basically, simple hill-climbing search, which is the simplest form of local search, is used to solve the given problem and integer programming is applied to generate a neighbor solution. In addition, constraint programming is used to generate an initial solution. Through the experimental results using N-Queens maximization problems, we confirmed that the proposed method can produce far better solutions than any other search methods.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.69-76
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• 2022

The traveling salesman problem(TSP) is one of the most famous combinatorial optimization problem. So far, many metaheuristic search algorithms have been proposed to solve the problem, and one of them is local search. One of the very important factors in local search is neighbor generation method, and random-based neighbor generation methods such as inversion have been mainly used. This paper proposes 4 new greedy-based neighbor generation methods. Three of them are based on greedy insertion heuristic which insert selected cities one by one into the current best position. The other one is based on greedy rotation. The proposed methods are applied to first-choice hill-climbing search and simulated annealing which are representative local search algorithms. Through the experiment, we confirmed that the proposed greedy-based methods outperform the existing random-based methods. In addition, we confirmed that some greedy-based methods are superior to the existing local search methods.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.6
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• pp.772-784
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• 2012

Marker-assisted gene pyramiding aims to produce individuals with superior economic traits according to the optimal breeding scheme which involves selecting a series of favorite target alleles after cross of base populations and pyramiding them into a single genotype. Inspired by the science of evolutionary computation, we used the metaphor of hill-climbing to model the dynamic behavior of gene pyramiding. In consideration of the traditional cross program of animals along with the features of animal segregating populations, four types of cross programs and two types of selection strategies for gene pyramiding are performed from a practical perspective. Two population cross for pyramiding two genes (denoted II), three population cascading cross for pyramiding three genes(denoted III), four population symmetry (denoted IIII-S) and cascading cross for pyramiding four genes (denoted IIII-C), and various schemes (denoted cross program-A-E) are designed for each cross program given different levels of initial favorite allele frequencies, base population sizes and trait heritabilities. The process of gene pyramiding breeding for various schemes are simulated and compared based on the population hamming distance, average superior genotype frequencies and average phenotypic values. By simulation, the results show that the larger base population size and the higher the initial favorite allele frequency the higher the efficiency of gene pyramiding. Parents cross order is shown to be the most important factor in a cascading cross, but has no significant influence on the symmetric cross. The results also show that genotypic selection strategy is superior to phenotypic selection in accelerating gene pyramiding. Moreover, the method and corresponding software was used to compare different cross schemes and selection strategies.

• Journal of Korean Society of Forest Science
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• v.104 no.1
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• pp.98-103
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• 2015

This study was conducted to analyze the running stability of a semi-crawler type mini-forwarder. The running stability analysis was performed by using a dynamic analysis program, RecurDyn. Physical properties of the semi-crawler type mini-forwarder was performed by using 3D CAD modeler, AutoCAD 3D. As a result from the computer simulation of stationary sideways overturning, it was found that the semi-crawler type mini-forwarder runs safely on a road with a slope not bigger than $20^{\circ}$ regardless whether it is empty or loaded, but in case of a road with a slope bigger than $20^{\circ}$, it is assumed that it is difficult for the car to run safely due to some dangers. In addition, it was found that the critical slope of its sideways overturning gets much smaller when empty since the location of its gravity center is elevated and much higher when it is loaded. As a result from the computer simulation of its hill-climbing ability, since the running speed is unstable in case of a road with a vertical slope not smaller than $28^{\circ}$, it is assumed that it is safe to drive it on a road with a slope not bigger than $28^{\circ}$. Taking a look at the result from an analysis of the running safety when it passes an obstacle, it was observed that a front tire comes off the ground when the running speed of the car is 5 and 4 km per hour respectively when it is empty and loaded while the gravity center of the front tire is watched. When taking a look at the changes in the location of the gravity center of the rear wheel crawler shaft, it was not found that the shaft comes off the ground at the test speeds both when it is empty and loaded.