• Journal of Power System Engineering
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• v.2 no.1
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• pp.52-58
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• 1998

An immune system has powerful abilities such as memory, recognition and learning to respond to invading antigens, and is applied to many engineering algorithm recently. In this paper, the combined optimization algorithm is proposed for multi-objective problem by introducing the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The optimizing ability of the proposed algorithm is identified by using multi-peak function which have many local optimums and identification of the flexural rigidity for wire rope model.

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

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.792-797
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• 2011

This paper discusses the application of a hybrid discretiziation method for the discretization procedure that needs to be included in discrete particle swarm optimization (DPSO) for the problem of allocating PV (photovoltaic) systems onto distribution power systems. For this purpose, this paper proposes a rule-based expert system considering the objective function value and its optimizing speed as the input parameters and applied it to the PV allocation problem including discrete decision variables. For multi-level discretization, this paper adopts a hybrid method combined with a simple rounding and sigmoid funtion based 3-step and 5-step quantization methods, and the application of the rule based expert system proposing the adequate discretization method at each PSO iteration so that the DPSO with the hybrid discretization can provide better performance than the previous DPSO.

• Journal of Korea Water Resources Association
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• v.41 no.1
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• pp.1-15
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• 2008

The purpose of this study is to propose a procedure that will be able to find the most efficient sets of weight coefficients for the Geum-River basin in Korea. The result obtained from multi-objective optimization model is inherently sensitive to the weight coefficient on each objective. In multi-objective reservoir operation problems, the coefficient setting may be more complicated because of the natural variation of inflow. Therefore, for multi-objective reservoir operation problems, it may be important for modelers to provide reservoir operators with appropriate sets of weight coefficients considering the inflow variation. This study presents a procedure to find an appropriate set of weight coefficients under the situation that has inflow variation. The proposed procedure uses GA-CoMOM to provide a set of weight coefficient sets. A DEA-window analysis and a cross efficiency analysis are then performed in order to evaluate and rank the sets of weight coefficients for various inflow scenarios. This proposed procedure might be able to find the most efficient sets of weight coefficients for the Geum-River basin in Korea.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.355-361
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• 2010

This paper addresses a mathematical approach to decision making in a real-world material distribution situation. The problem is characterized by a low-volume and highly-varied mix of products, therefore there is a lot of material movement between the facilities. This study focuses especially on the transportation scheduler with a tool that can be used to quantitatively analyze the volume of material moved, the type of truck to be used, production schedules, and due dates. In this research, we have developed a mixed integer programming problem using the minimum cost, multiperiod, multi-commodity network flow approach that minimizes the overall material movement costs. The results suggest that the optimization approach provides a set of feasible solution routes with the objective of reducing the overall fleet cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3588-3607
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• 2021

Covid-19 pandemic required all the world to use internet more actively. As a result, individuals and businesses are more open to digital threats. In order to provide security within the network, firewalls should be used. Firewalls act as a gateway between the corporate and the external networks. Therefore, it is more important than ever to choose the right firewall for each network. In this study, a new linear decision making model is proposed in order to find out the most suitable firewall and the estimates are completed according to this new model. Also, this model is compared with multi-objective optimization on the basis of ratio analysis (MOORA) method. This study distinguishes from other studies by proposing a new solution which ranks the firewall alternatives using linear and MOORA approaches. These approaches are used in many fields before but not in information technologies. Thus, this study can be considered quite innovative in terms of the problem it handles and the approaches used. It offers up-to-date and practical suggestions related to a decision making problem that has not been previously studied in the literature.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.181-187
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• 2012

Various computer-based simulation tools such as 3D-CAD and CAE systems are widely used to design automotive body structure at the early phase of design. Designers must search the optimal solution that satisfies a number of performance requirements by using their tools and a trial-and-error approach. In the previous three reports, a set-based design approach has been proposed for achieving design flexibility and robustness while capturing designer's preference, and its effectiveness has been illustrated with a simple side-door impact beam design problem and real vehicle side-door structure design. This report presents the development of integrated 3D-CAD and CAE system, and the applicability of our proposal for obtaining the multi-objective satisfactory design solutions by applying to an automotive front-side frame.

• International Journal of High-Rise Buildings
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• v.7 no.4
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• pp.363-374
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• 2018

The façade is an important, complex, and costly part of a building, performing multiple objectives of value to the occupants, like protecting from wind, rain, sunlight, heat, cold, and sound. But the frequency of façade fires in large buildings is alarming, and has multiplied by seven times worldwide over the last three decades, to a current rate of 4.8 fires per year. High-performing polymer based materials allow for a significant improvement across several objectives of a facade (e.g., thermal insulation, weight, and construction time) thereby increasing the quality of a building. However, all polymers are flammable to some degree. If this safety problem is to be tackled effectively, then it is essential to understand how different materials, and the façade as a whole, perform in the event of a fire. This paper discusses the drivers for flammability in facades, the interaction of facade materials, and current gaps in knowledge. In doing so, it aims to provide an introduction to the field of façade fires, and to show that because of the drive for thermal efficiency and sustainability, façade systems have become more complex over time, and they have also become more flammable. We discuss the importance of quantifying the flammability of different façade systems, but highlight that it is currently impossible to do so, which hinders research progress. We finish by putting forward an integral framework of design that uses multi-objective optimization to ensure that flammability is minimized while considering other objectives, such as maximizing thermal performance or minimizing weight.

• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.681-695
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• 2019

A Transient Storage Model (TSM) is one of the most widely used model accounting for complex solute transport in natural river to understanding natural river properties with four TSM key parameters. The TSM parameters are estimated via inverse modeling. Parameter estimation of the TSM is carried out by solving optimization problem about finding best fitted simulation curve with measured curve obtained from tracer test. Several studies have reported uncertainty in parameter estimation from non-convexity of the problem. In this study, we assessed best combination of optimization method and objective function for TSM parameter estimation using Cheong-mi Creek tracer test data. In order to find best optimization setting guaranteeing convergence and speed, Evolutionary Algorithm (EA) based global optimization methods, such as CCE of SCE-UA and MCCE of SP-UCI, and error based objective functions were compared, using Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL). Overall results showed that multi-EA SC-SAHEL with Percent Mean Squared Error (PMSE) objective function is the best optimization setting which is fastest and stable method in convergence.

• Journal of Communications and Networks
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• v.10 no.3
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• pp.351-361
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• 2008

This paper presents a delay-margin based traffic engineering (TE) approach to provide end-to-end quality of service (QoS) in multi-protocol label switching (MPLS) networks using differentiated services (DiffServ) at the link level. The TE, including delay, class, and route assignments, is formulated as a nonlinear optimization problem reflecting the inter-class and inter-link dependency introduced by DiffServ and end-to-end QoS requirements. Three algorithms are used to provide a solution to the problem: The first two, centralized offline route configuration and link-class delay assignment, operate in the convex areas of the feasible region to consecutively reduce the objective function using a per-link per-class decomposition of the objective function gradient. The third one is a heuristic that promotes/demotes connections at different links in order to deal with concave areas that may be produced by a trunk route usage of more than one class on a given link. Approximations of the three algorithms suitable for on-line distributed TE operation are also derived. Simulation is used to show that proposed approach can increase the number of users while maintaining end-to-end QoS requirements.