• Proceedings of the Korean Information Science Society Conference
• /
• 2011.06b
• /
• pp.470-473
• /
• 2011

본 논문에서는 첫째, 기존 HW/SW partitioning문제의 접근 방식 모델에서 다루지 못하였던 시간 의존적인 개발 기간, 판매 가격, 판매량, time-to-market 등의 요소들을 반영하는 multi-objective 최적화문제 형태의 새로운 "Sequential HW/SW Partitioning Optimization Framework(SPOF)"를 제시하고 둘째, 그 모델로 형식화된 NP-hard 문제를 일반적으로 해결하기위한 해법으로 SPOF의 형태에 맞게끔 변형한 chromosome과 genetic operation을 사용하는 메타휴리스틱 "Fast and Elitist Multi-objective Genetic Algorithm(NSGA-II)"을 제시한다. 실험을 통하여 NSGA-II의 최적 솔루션에의 수렴성을 보인다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.5
• /
• pp.621-626
• /
• 2003

In this paper, we modeled a virtual life (VL) that reacts to the user s action according to its own behavioral characteristics and grows itself. We established some conditions with which such a VL is designed. Genetic Algorithm is used for the growth process that changes the VL s properties. In this process, the parameter values of the VL s properties are encoded as one chromosome, and the GA operations change this chromosome. The VL s reaction to the user s action is determined by these properties as well as the general expectation of each reaction. These properties are evaluated through 5 fitness measures so as to deal with multi-objective criteria. Here, we present the simulation of the growth Process, and show some experimental results.

• Journal of Communications and Networks
• /
• v.8 no.1
• /
• pp.116-122
• /
• 2006

Most of the multimedia applications require strict quality of service (QoS) guarantee during the communication between a single source and multiple destinations. This gives rise to the need for an efficient QoS multicast routing strategy. Determination of such QoS-based optimal multicast routes basically leads to a multi-objective optimization problem, which is computationally intractable in polynomial time due to the uncertainty of resources in Internet. This paper describes a network model for researching the routing problem and proposes a new multicast tree selection algorithm based on genetic algorithms to simultaneously optimize multiple QoS parameters. The paper mainly presents a QoS multicast routing algorithm based on genetic algorithm (QMRGA). The QMRGA can also optimize the network resources such as bandwidth and delay, and can converge to the optimal or near-optimal solution within few iterations, even for the networks environment with uncertain parameters. The incremental rate of computational cost can close to polynomial and is less than exponential rate. The performance measures of the QMRGA are evaluated using simulations. The simulation results show that this approach has fast convergence speed and high reliability. It can meet the real-time requirement in multimedia communication networks.

• 유체기계공업학회:학술대회논문집
• /
• 1999.12a
• /
• pp.324-332
• /
• 1999

This paper presents the artificial life algorithm which is remarkable in the area of engineering for optimum design. As artificial life organisms have a sensing system, they can find the resource which they want to find and metabolize it. And the characteristics of artificial life are emergence and dynamical interacting with environment. In other words, the micro interaction with each other in the artificial life's group results in emergent colonization in the whole system. In this paper, therefore, artificial life algorithm by using above characteristics is employed into functions optimization. The effectiveness of this proposed algorithm is verified through the numerical test of single and multi objective functions. The numerical tests also show that the proposed algorithm is superior to genetic algorithm and immune algorithm for the Multi-peak function. And artificial life algorithm is also applied to optimum design of high-speed, short journal bearings and verified through the numerical test.

• Smart Structures and Systems
• /
• v.17 no.5
• /
• pp.709-724
• /
• 2016

This study investigates the optimum design parameters of a superelastic friction base isolator (S-FBI) system through a multi-objective genetic algorithm to improve the performance of isolated buildings against near-fault earthquakes. The S-FBI system consists of a flat steel-PTFE sliding bearing and superelastic NiTi shape memory alloy (SMA) cables. Sliding bearing limits the transfer of shear across the isolation interface and provides damping from sliding friction. SMA cables provide restoring force capability to the isolation system together with additional damping characteristics. A three-story building is modeled with S-FBI isolation system. Multiple-objective numerical optimization that simultaneously minimizes isolation-level displacements and superstructure response is carried out with a genetic algorithm in order to optimize S-FBI system. Nonlinear time history analyses of the building with optimal S-FBI system are performed. A set of 20 near-fault ground motion records are used in numerical simulations. Results show that S-FBI system successfully control response of the buildings against near-fault earthquakes without sacrificing in isolation efficacy and producing large isolation-level deformations.

• Journal of Korean Institute of Industrial Engineers
• /
• v.26 no.4
• /
• pp.421-430
• /
• 2000

This paper presents a new approach which uses $A^*$ search and genetic algorithms for solving large scale multi-objective shortest path problem. The focus of this paper is motivated by the problem of finding Pareto optimal paths for an advanced traveler information system(ATIS) in the context of intelligent transportation system(ITS) application. The individual description, the decoding rule, the selection strategy and the operations of crossover and mutation are proposed for this problem. The keynote points of the algorithm are how to represent individuals and how to calculate the fitness of each individual. The high performance of the proposed algorithm is demonstrated by computer simulations.

• Earthquakes and Structures
• /
• v.16 no.6
• /
• pp.679-690
• /
• 2019

This paper presents machine learning methods using Support Vector Machine (SVM) and Multilayer Perceptron (MLP) to analyze optimal damper distribution for structural vibration control. Regarding different building structures, a genetic algorithm based optimization method is used to determine optimal damper distributions that are further used as training samples. The structural features, the objective function, the number of dampers, etc. are used as input features, and the distribution of dampers is taken as an output result. In the case of a few number of damper distributions, multi-class prediction can be performed using SVM and MLP respectively. Moreover, MLP can be used for regression prediction in the case where the distribution scheme is uncountable. After suitable post-processing, good results can be obtained. Numerical results show that the proposed method can obtain the optimized damper distributions for different structures under different objective functions, which achieves better control effect than the traditional uniform distribution and greatly improves the optimization efficiency.

• ETRI Journal
• /
• v.43 no.1
• /
• pp.53-63
• /
• 2021

Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource-management approach. In this paper, we present a novel load-balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics-based VM placement approaches.

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2005.11a
• /
• pp.306-309
• /
• 2005

We consider the multi-objective optimization of a multi-service arrayed-waveguide grating-based single-hop metro WDM network with the two conflicting objectives of maximizing throughput while minimizing delay. We develop and evaluate a genetic algorithm based methodology for finding the optimal throughput-delay tradeoff curve, the so-called Pareto-optimal frontier. Our methodology provides the network architecture and the Medium Access Control protocol parameters that achieve the Pareto-optima in a computationally efficient manner. The numerical results obtained with our methodology provide the Pareto-optimal network planning and operation solution for a wide range of traffic scenarios. The presented methodology is applicable to other networks with a similar throughput-delay tradeoff.

• Journal of Korea Water Resources Association
• /
• v.37 no.2
• /
• pp.121-131
• /
• 2004

SIMHYD and TANK model are used to predict time series of daily rainfall-runoff of Soyang Dam and Youngcheon Dam watershed. The performances of SIMHYD model with 7 parameters and TANK model with17 parameters are compared. Three optimization methods (Genetic algorithm, Pattern search multi-start and Shuffled Complex Evolution algorithm) were applied to study-areas with 3 different types of objective functions. Efficiency of TANK model is higher than that of SIMHYD. Among different types of objective function, Nash-sutcliffe coefficient is found to be the most appropriateobjective function to evaluate applicability of model.