• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.403-410
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• 1998

An immune system has powerful abilities such as memory recognition and learning how to respond to invading antigens, and has been applied to many engineering algorithms in recent year. In this paper, the combined optimization algorithm (Immune-Genetic Algorithm: IGA) is proposed for multi-optimization problems by introduction the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The new combined algorithm is applied to minimize the total weight of the rotor shaft and the transmitted forces at the bearings in order to demonstrate the merit of the combined algorithm. The inner diameter of the shaft and the bearing stiffness are chosen as the design variables. the results show that the combined algorithm can reduce both the weight of the shaft and the transmitted forces at the bearing with dynamic constraints.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.1-20
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• 2018

Service composition in the Inter-Cloud raises new challenges that are caused by the different Quality of Service (QoS) requirements of the users, which are served by different geo-distributed Cloud providers. This paper aims to explore how to select and compose such services while considering how to reach high efficiency on cost and response time, low network latency, and high reliability across multiple Cloud providers. A new hybrid multi-objective evolutionary algorithm to perform the above task called LS-NSGA-II-DE is proposed, in which the differential evolution (DE) algorithm uses the adaptive mutation operator and crossover operator to replace the those of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to get the better convergence and diversity. At the same time, a Local Search (LS) method is performed for the Non-dominated solution set F{1} in each generation to improve the distribution of the F{1}. The simulation results show that our proposed algorithm performs well in terms of the solution distribution and convergence, and in addition, the optimality ability and scalability are better compared with those of the other algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.39-45
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• 2008

This paper considers the multi-objective optimization of a multi-service arrayed waveguide grating-based single-hop WDM network with the two conflicting objectives of maximizing throughput while minimizing delay. This paper presents a genetic algorithm based methodology for finding the optimal throughput-delay tradeoff curve, the so-called Pareto-optimal frontier. Genetic algorithm based methodology provides the network architecture parameters and the Medium Access Control protocol parameters that achieve the Pareto-optima in a computationally efficient manner. The numerical results obtained with this 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 the Korean Society of Safety
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• v.33 no.2
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• pp.61-67
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• 2018

This study proposes a new vibration control approach for adjacent twin structures, which is termed as viscous damper asymmetric coupling system in this paper. The proposed system takes a concept that the diagonal bracing viscous dampers are asymmetrically distributed in two buildings to break the behavior symmetry of the twin buildings and then the coupling viscous damper is additionally installed at the top floor of the two buildings to couple both buildings and interactively transfer the asymmetric behavior-caused damping forces into both buildings. These asymmetric damping distributions and interacting damping forces of the connection damper efficiently suppress the overall vibration of the damper-coupled adjacent twin buildings efficiently. Genetic algorithm (GA) based multi-objective optimization technique is adopted for optimal design of the proposed system. In the numerical example of adjacent twin 10-story building structures, the conventional control approach, that is, uniform damping distribution system (UDS) is also taken into account for comparison purpose. The optimization results verify that the proposed system either can improve the control performance over the UDS with the same damping capacity, or can save the damping capacity significantly while maintaining the similar level of control performance to the UDS.

• Geomechanics and Engineering
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• v.10 no.6
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• pp.709-726
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• 2016

Corrugated-core sandwich panels are prevalent for many applications in industries. The researches performed with the aim of optimization of such structures in the literature have considered a deterministic approach. However, it is believed that deterministic optimum points may lead to high-risk designs instead of optimum ones. In this paper, an effort has been made to provide a reliable and robust design of corrugated-core sandwich structures through stochastic and probabilistic multi-objective optimization approach. The optimization is performed using a coupling between genetic algorithm (GA), Monte Carlo simulation (MCS) and finite element method (FEM). To this aim, Prob. Design module in ANSYS is employed and using a coupling between optimization codes in MATLAB and ANSYS, a connection has been made between numerical results and optimization process. Results in both cases of deterministic and probabilistic multi-objective optimizations are illustrated and compared together to gain a better understanding of the best sandwich panel design by taking into account reliability and robustness. Comparison of results with a similar deterministic optimization study demonstrated better reliability and robustness of optimum point of this study.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1224-1225
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• 2005

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1671-1689
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• 2013

The key issue of block-based multi-focus image fusion is to determine the size of the sub-block because different sizes of the sub-block will lead to different fusion effects. To solve this problem, this paper presents a novel genetic algorithm (GA) based multi-focus image fusion method, in which the block size can be automatically found. In our method, the Sum-modified-Laplacian (SML) is selected as an evaluation criterion to measure the clarity of the image sub-block, and the edge information retention is employed to calculate the fitness of each individual. Then, through the selection, crossover and mutation procedures of the GA, we can obtain the optimal solution for the sub-block, which is finally used to fuse the images. Experimental results show that the proposed method outperforms the traditional methods, including the average, gradient pyramid, discrete wavelet transform (DWT), shift invariant DWT (SIDWT) and two existing GA-based methods in terms of both the visual subjective evaluation and the objective evaluation.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.3
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• pp.293-303
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• 2015

Optimal design of the water supply pipe network aims to minimize construction cost while satisfying the required hydraulic constraints such as the minimum and maximum pressures, and velocity. Since considering one single design factor (i.e., cost) is very vulnerable for including future conditions and cannot satisfy operator's needs, various design factors should be considered. Hence, this study presents three kinds of design factors (i.e., minimizing construction cost, maximizing reliability, and surplus head) to perform multi-objective optimization design. Harmony Search (HS) Algorithm is used as an optimization technique. As well-known benchmark networks, Hanoi network and Gyeonggi-do P city real world network are used to verify the applicability of the proposed model. In addition, the proposed multi-objective model is also applied to a real water distribution networks and the optimization results were statistically analyzed. The results of the optimal design for the benchmark and real networks indicated much better performance compared to those of existing designs and the other approach (i.e., Genetic Algorithm) in terms of cost and reliability, cost, and surplus head. As a result, this study is expected to contribute for the efficient design of water distribution networks.

• Journal of Korea Water Resources Association
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• v.38 no.10 s.159
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• pp.861-869
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• 2005

A rainfall-runoff model should be calibrated so that the model simulates the hydrological behavior of the basin as accurately as possible. In this study, to calibrate the five parameters of the SSARR model, a multi-objective function and the genetic algorithm were used. The solution of the multi-objective function will not, in general, be a single unique set of parameters but will consist of the so-called Pareto solution according to various trade-offs between the different objectives. The calibration strategy using multi-objective function could decrease calibrating time and effort. From the Pareto solution, a single solution could be selected to simulate a specific flow condition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3C
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• pp.314-322
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• 2007

This paper proposes a new optimization algorithm named by GBNSGA(Goal-Pareto Based Non-dominated Sorting Genetic Algorithm) which determines the best configuration for CR(Cognitive Radio) communication systems. Conventionally, in order to select the proper radio configuration, genetic algorithm has been introduced so as to alleviate computational burden along the execution of the cognition cycle proposed by Mitola. This paper proposes a novel optimization algorithm designated as GBNSGA for cognitive engine which can be described as a hybrid algorithm combining well-known Pareto-based NSGA(Non-dominated Sorting Genetic Algorithm) as well as GP(Goal Programming). By conducting computer simulations, it will be verified that the proposed method not only satisfies the user's service requirements in the form of goals. It reveals the fast optimization capability and more various solutions rather than conventional NSGA or weighted-sum approach.