• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.621-624
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• 2002

The purpose of this paper is a study on getting proper gain set of PID controller which satisfies multi-performance specifications of the control system. The multi-objective optimization method is introduced to evaluate specifications, and the genetic algorithm is used as an optimal problem solver. To enhance the performance of genetic algorithm itself, adaptive technique is included. According to the proposed method in this paper, finding suitable gain set can be more easily accomplishable than manual gain seeking and tuning.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.656-657
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• 2015

Recently, Multi-Objective Optimization of design elements is an important issue in building design. Design variables that considering the specificities of the different environments should use the appropriate algorithm on optimization process. The purpose of this study is to compare and analyze the optimal solution using three evolutionary algorithms and energy modeling simulation. This paper consists of three steps: i)Developing three evolutionary algorithm model for optimization of design elements ; ii) Conducting Multi-Objective Optimization based on the developed model ; iii) Conducting comparative analysis of the optimal solution from each of the algorithms. Including Non-dominated Sorted Genetic Algorithm (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO) and Random Search were used for optimization. Each algorithm showed similar range of result data. However, the execution speed of the optimization using the algorithm was shown a difference. NSGA-II showed the fastest execution speed. Moreover, the most optimal solution distribution is derived from NSGA-II.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.467-475
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• 2009

Congestion management is one of the technical challenges in power system deregulation. This paper presents single objective and multi-objective optimization approaches for optimal choice, location and size of Static Var Compensators (SVC) and Thyristor Controlled Series Capacitors (TCSC) in deregulated power system to improve branch loading (minimize congestion), improve voltage stability and reduce line losses. Though FACTS controllers offer many advantages, their installation cost is very high. Hence Independent System Operator (ISO) has to locate them optimally to satisfy a desired objective. This paper presents optimal location of FACTS controllers considering branch loading (BL), voltage stability (VS) and loss minimization (LM) as objectives at once using GA. It is observed that the locations that are most favorable with respect to one objective are not suitable locations with respect to other two objectives. Later these competing objectives are optimized simultaneously considering two and three objectives at a time using multi-objective Strength Pareto Evolutionary Algorithms (SPEA). The developed algorithms are tested on IEEE 30 bus system. Various cases like i) uniform line loading ii) line outage iii) bilateral and multilateral transactions between source and sink nodes have been considered to create congestion in the system. The developed algorithms show effective locations for all the cases considered for both single and multiobjective optimization studies.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.2
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• pp.57-64
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• 2012

In this paper, the multi-objective genetic algorithm is proposed for the document clustering which is important in the text mining field. The most important function in the document clustering algorithm is to group the similar documents in a corpus. So far, the k-means clustering and genetic algorithms are much in progress in this field. However, the k-means clustering depends too much on the initial centroid, the genetic algorithm has the disadvantage of coming off in the local optimal value easily according to the fitness function. In this paper, the multi-objective genetic algorithm is applied to the document clustering in order to complement these disadvantages while its accuracy is analyzed and compared to the existing algorithms. In our experimental results, the multi-objective genetic algorithm introduced in this paper shows the accuracy improvement which is superior to the k-means clustering(about 20 %) and the general genetic algorithm (about 17 %) for the document clustering.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.511-513
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• 2003

멀티미디어 서비스의 증가로 다양한 QoS(Quality of Service) 파라미터를 보장하는 멀티캐스트 라우팅 알고리즘이 필요하게 되었다. 이러한 멀티캐스트 라우팅에서 고려해야 하는 각각의 QoS 파리미터와 비용과의 관계는 Trade-off 관계에 있으며, 이들을 동시에 최적화하는 멀티캐스트 라우팅 문제는 다목적 최적화 문제(Multi-Objective Optimization Problem: MOOP)에 속하는 어려운 문제이다. 다목적 최적화 문제의 목표는 다양한 파레토 최적해(Pareto Optimal Solution)를 찾는데 있으며, 이를 해결하기 위해서 본 논문에서는 다목적 유전자 알고리즘(Multiple Objective Genetic Algorithms： MOGA)을 적용하였다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.109-117
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• 2022

Most of real-world decision-making processes are used to optimize problems with many objectives of conflicting. Since the betterment of some objectives requires the sacrifice of other objectives, different objectives may not be optimized simultaneously. Consequently, Pareto solution can be considered as candidates of a solution with respect to a multi-objective optimization (MOP). Such problem involves two main procedures: finding Pareto solutions and choosing one solution among them. So-called multi-objective genetic algorithms have been proved to be effective for finding many Pareto solutions. In this study, we suggest a fitness evaluation method based on the achievement level up to the target value to improve the solution search performance by the multi-objective genetic algorithm. Using numerical examples and benchmark problems, we compare the proposed method, which considers the achievement level, with conventional Pareto ranking methods. Based on the comparison, it is verified that the proposed method can generate a highly convergent and diverse solution set. Most of the existing multi-objective genetic algorithms mainly focus on finding solutions, however the ultimate aim of MOP is not to find the entire set of Pareto solutions, but to choose one solution among many obtained solutions. We further propose an interactive decision-making process based on a visualized trade-off analysis that incorporates the satisfaction of the decision maker. The findings of the study will serve as a reference to build a multi-objective decision-making support system.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.93.3-93
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• 2002

$\textbullet$ Co-evolutionary algorithms $\textbullet$ Nash Genetic Algorithms $\textbullet$ Multi-objective Optimization $\textbullet$ Distance dependent mutation $\textbullet$ Pareto Optimality

• Journal of Korean Association for Spatial Structures
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• v.14 no.4
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• pp.97-104
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• 2014

Recently, a concept of damped outrigger system has been proposed for tall buildings. Structural characteristics and design method of this system were not sufficiently investigated to date. In this study, control performance of damped outrigger system for building structures subjected to seismic excitations has been investigated. And optimal design method of damped outrigger system has been proposed using multi-objective genetic algorithm. To this end, a simplified numerical model of damped outrigger system has been developed. State-space equation formulation proposed in previous research was used to make a numerical model. Multi-objective genetic algorithms has been employed for optimal design of the stiffness and damping parameters of the outrigger damper. Based on numerical analyses, it has been shown that the damped outrigger system control dynamic responses of the tall buildings subjected to earthquake excitations in comparison with a traditional outrigger system.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.713-724
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• 2013

In terms of waste load allocation, inequality of waste load discharge must be considered as well as economic aspects such as minimization of waste load abatement. The inequality of waste load discharge between areas was calculated with Gini coefficient and was included as one of the objective functions of the multi-objective waste load allocation. In the past, multi-objective functions were usually weighted and then transformed into a single objective optimization problem. Recently, however, due to the difficulties of applying weighting factors, multi-objective genetic algorithms (GA) that require only one execution for optimization is being developed. This study analyzes multi-objective waste load allocation using NSGA-II-aJG that applies Pareto-dominance theory and it's adaptation of jumping gene. A sensitivity analysis was conducted for the parameters that have significant influence on the solution of multi-objective GA such as population size, crossover probability, mutation probability, length of chromosome, jumping gene probability. Among the five aforementioned parameters, mutation probability turned out to be the most sensitive parameter towards the objective function of minimization of waste load abatement. Spacing and maximum spread are indexes that show the distribution and range of optimum solution, and these two values were the optimum or near optimal values for the selected parameter values to minimize waste load abatement.

• Journal of Communications and Networks
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• v.15 no.5
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• pp.486-495
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• 2013

In this article, we suggest a two-dimensional genetic algorithm (GA) method that applies a cognitive radio (CR) decision engine which determines the optimal transmission parameters for multicarrier communication systems. Because a CR is capable of sensing the previous environmental communication information, CR decision engine plays the role of optimizing the individual transmission parameters. In order to obtain the allowable transmission power of multicarrier based CR system demands interference analysis a priori, for the sake of efficient optimization, a two-dimensionalGA structure is proposed in this paper which enhances the computational complexity. Combined with the fitness objective evaluation standard, we focus on two multi-objective optimization methods: The conventional GA applied with the multi-objective fitness approach and the non-dominated sorting GA with Pareto-optimal sorting fronts. After comparing the convergence performance of these algorithms, the transmission power of each subcarrier is proposed as non-interference emission with its optimal values in multicarrier based CR system.