• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.401-406
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• 2011

In order to solve a complex optimization problem more efficiently than traditional approaches, various meta-heuristic algorithms such as genetic algorithm, ant-colony algorithm, and harmony search algorithm have been extensively researched. Compared with other meta-heuristic algorithm, harmony search algorithm shows a better result to resolve the complex optimization issues. Harmony search algorithm is inspired by the improvision process of musician for most suitable harmony. In general, the performance of harmony search algorithm is determined by the value of harmony memory considering rate, and pitch adjust rate. In this paper, modified harmony search algorithm is proposed in order to derive best harmony. If the optimal solution of a specific problem can not be found for a certain period of time, a part of original harmony memory is updated as the selected suitable harmonies. Experimental results using test function demonstrate that the updated harmony memory can induce the approximation of reliable optimal solution in the short iteration, because of a few change of fitness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1679-1685
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• 2022

In this paper, we propose an optimal multi-hop transmission scheme to maximize the end-to-end data rate from the source to the destination node in a wireless powered communication network. The frame structure for multi-hop transmission is presented to transmit multi-hop data while harvesting energy. Then, the transmission time of each node that maximizes the end-to-end transmission rate is determined through mathematical analysis in consideration of different harvested energy and link quality among nodes. We derive an optimization problem through system modeling of the considered wireless powered multi-hop transmission, and prove that there is a global optimal solution by verifying the convexity of this optimization problem. This analysis facilitates to find the optimal solution of the considered optimization problem. The proposed optimal multi-hop transmission scheme maximizes the end-to-end rate by allocating the transmission time for each node that equalizes the transmission rates of all links.

• Journal of Korean Society of Transportation
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• v.18 no.1
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• pp.61-73
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• 2000

The equilibrium network design problem can be formulated as a mathematical Program with variational inequality constraints. We know this problem may have may multiple local solutions due to its inherent characteristics - Nonlinear Objective function and Nonlinear, Nonconvex constraints. Hence, it is difficult to solve for a globally optimal solution. In this paper, we propose a genetic algorithm to obtain a globa1 optimum among many local optima. A Proposed a1gorithm is compared with 4 different solution algorithms for 1 small test network and 1 real-size network. The results of some computational testing are reported.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.2
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• pp.122-129
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• 2002

An optimization of satellite honeycomb platforms under sever space environment is performed. There are many optimization constraints for space environment to be considered. A modified method of feasible direction and a genetic algorithm are used to optimize the satellite platform structures. The design constraints are concerned with bearing stresses at joints and natural frequencies. The results from the optimization methods are compared. The numerical results show that natural frequency constraints are dominant to reach the optimum design. This study verifies the design of satellite honeycomb platforms and suggests an optimal platform design.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.341-342
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• 2008

GA(Genetic Algorithm)는 NP-Complete 도메인이나 NP-Hard 도메인 내의 문제들에 대해서 최적의 해를 찾기 위해서 많이 사용되어 지는 진화 컴퓨팅 방법 중 하나이다. 모바일 로봇 기술 중 경로계획은 NP-Complete 도메인 영역의 문제 중 하나로 이를 해결하기 위해서 Dijkstra 등의 그래프 이론을 이용한 연구가 많이 연구되었고 최근에는 GA등 진화 컴퓨팅 기법을 이용하여 최적의 경로를 찾는 연구가 많이 수행되고 있다. 그러나 모바일 로봇이 처리해야 될 공간 정보 크기가 증가함에 따라 기존 GA의 개체의 크기가 증가되어 게산 복잡도가 높아져 시간 지연등의 문제가 발생할 수 있다. 이는 모바일 로봇의 잠재적 오류로 발생될 수 있다. 공간 정보에는 동적이 장애물들이 예측 불허하게 나타 날 수 있는데 이것은 전역 경로 계획을 수립할 때 또한 반영되어야 된다. 본 논문에서는 k-means 클러스터링 기법을 이용하여 장애물 밀집도 및 거리 정보를 기반으로 공간정보를 k개의 군집 공간으로 재분류하여 이를 기반으로 N*M개의 그리드 개체 집단을 생성하여 최적 경로계획을 수립하는 GA를 제시한다.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.5
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• pp.210-218
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• 2003

MDO (multidisciplinary design optimization) technology has been proposed and applied to solve large and complex optimization problems where multiple disciplinaries are involved. In this research. an MDO problem is defined for automobile design which has crashworthiness analyses. Crash model which are consisted of airbag, belt integrated seat (BIS), energy absorbing steering system .and safety belt is selected as a practical example for MDO application to vehicle system. Through disciplinary analysis, vehicle system is decomposed into structure subspace and occupant subspace, and coupling variables are identified. Before subspace optimization, values of coupling variables at given design point must be determined with system analysis. The system analysis in MDO is very important in that the coupling between disciplines can be temporary disconnected through the system analysis. As a result of system analysis, subspace optimizations are independently conducted. However, in vehicle crash, system analysis methods such as Newton method and fixed-point iteration can not be applied to one. Therefore, new system analysis algorithm is developed to apply to crashworthiness. It is conducted for system analysis to determine values of coupling variables. MDO algorithm which is applied to vehicle crash is MDOIS (Multidisciplinary Design Optimization Based on Independent Subspaces). Then, structure and occupant subspaces are independently optimized by using MDOIS.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.99-121
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• 2009

Recently, there have been many studies that predict corporate bankruptcy using data mining techniques. Although various data mining techniques have been investigated, some researchers have tried to combine the results of each data mining technique in order to improve classification performance. In this study, we classify 4 types of data mining techniques via their characteristics and select representative techniques of each type then combine them using a genetic algorithm. The genetic algorithm may find optimal or near-optimal solution because it is a global optimization technique. This study compares the results of single models, typical combination models, and the proposed integration model using the genetic algorithm.

• Journal of Korean Society of Steel Construction
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• v.12 no.6
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• pp.629-638
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• 2000

With the positive features of simulated annealing algorithms such as simplicity of the algorithm and the possibility of finding global optimum solution, SA algorithm has been widely applied to structural optimization problems. However, the algorithms are far from practical applications in structural design or optimization of building structures due to requirement of a large number of iterations and dependency on cooling schedule and stopping criteria. In this paper, with the modification of annealing process and stopping criteria, a MSA algorithm is presented in the form of two phase annealing process for optimal seismic design of braced structures. The performance of the proposed algorithm has been illustrated in detail.

• Journal of the Korean Magnetics Society
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• v.20 no.1
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• pp.24-27
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• 2010

This paper presents the design optimization of a linear actuator for fast response of electromagnetic engine valve. The optimization is performed using generic algorithm which is one of global search techniques and not highly dependent on either initial conditions or constraints in the solution domain to maximize the mechanical frequency of the armature mass and valve spring stiffness for fast response of the engine valve. In the results, the mechanical frequency is improved by 30 %.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.836-839
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• 2016

In this paper, we propose a motion estimation method that is important in performance of video encoding. Conventional motion estimation methods have serious problems of low prediction quality and problems of much computation increase. In the paper, we propose a method that reduces unnecessary computations only using optimal candidate points, while keeping prediction quality almost similar to that of the full search. The proposed method takes only 3~5% in computational amount and has decreased prediction quality about 0~0.01dB compared with the fast full search algorithm.