• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4025-4041
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• 2020

Mobile edge computing (MEC) is capable of providing services to smart devices nearby through radio access networks and thus improving service experience of users. In this paper, an offloading strategy for the joint optimization of computing and communication resources in multi-user and multi-MEC overlapping scene was proposed. In addition, under the condition that wireless transmission resources and MEC computing resources were limited and task completion delay was within the maximum tolerance time, the optimization problem of minimizing energy consumption of all users was created, which was then further divided into two subproblems, i.e. offloading strategy and resource allocation. These two subproblems were then solved by the game theory and Lagrangian function to obtain the optimal task offloading strategy and resource allocation plan, and the Nash equilibrium of user offloading strategy games and convex optimization of resource allocation were proved. The simulation results showed that the proposed algorithm could effectively reduce the energy consumption of users.

• Journal of Aerospace System Engineering
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• v.15 no.2
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• pp.1-9
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• 2021

In this paper, a thickness compensation function is introduced to consider the shear deformation and warping effect resulting from increased thickness in the composite multi-cell wing box. The thickness compensation function is used to perform the structure optimization of the multi-cell. It is determined by minimizing the error of an analytical formula using solid mechanics and the Ritz method. It is used to define a structural performance prediction expression due to the increase in thickness. The parameter is defined by the number of spars and analyzed by the critical buckling load and the limited failure index as a response. Constraints in structural optimization are composed of displacements, torsional angles, the critical buckling load, and the failure index. The objective function is the mass, and its optimization is performed using a genetic algorithm.

• Steel and Composite Structures
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• v.44 no.2
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• pp.211-224
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• 2022

Multi-objective optimization was conducted to obtain the optimal configuration of a composite sandwich structure with honeycomb-foam hybrid core subjected to underwater shock waves, which can fulfill the demand for light weight and energy efficient design of structures against underwater blast. Effects of structural parameters on the dynamic response of the sandwich structures subjected to underwater shock waves were analyzed numerically, from which the correlations of different parameters to the dynamic response were determined. Multi-objective optimization of the structure subjected to underwater shock waves of which the initial pressure is 30 MPa was conducted based on surrogate modelling method and genetic algorithm. Moreover, optimization results of the sandwich structure subjected to underwater shock waves with different initial pressures were compared. The research can guide the optimal design of composite sandwich structures subjected to underwater shock waves.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1329-1339
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• 2017

This paper presents the power scheduling of a multi-microgrid (MMG) system using an optimization technique called particle swarm optimization (PSO). The PSO technique has been shown to be most effective at solving the various problems of the economic dispatch (ED) in a power system. In addition, a new MMG system configuration is proposed in this paper, through which the optimal power flow is achieved. Both optimization and power trading methods within an MMG are studied. The results of implementing PSO in an MMG system for optimal power flow and cost minimization are obtained and compared with another attractive and efficient optimization technique called the genetic algorithm (GA). The comparison between these two effective methods provides very competitive results, and their operating costs also appear to be comparable. Finally, in this study, power scheduling and a power trading method are obtained using the MATLAB program.

• Steel and Composite Structures
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• v.27 no.2
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• pp.177-184
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• 2018

This study contributes to evaluate multiphase topology optimization design of plate-like structures on elastic foundations by using classic plate theory. Multi-material optimal topology and shape are produced as an alternative to provide reasonable material assignments based on stress distributions. Multi-material topology optimization problem is solved through an alternative active-phase algorithm with Gauss-Seidel version as an optimization model of optimality criteria. Stiffness and adjoint sensitivity formulations linked to thin plate potential strain energy are derived in terms of multiphase design variables and Winkler-Pasternak parameters considering elastic foundation to apply to the current topology optimization. Numerical examples verify efficiency and diversity of the present topology optimization method of elastic thin plates depending on multiple materials and Winkler-Pasternak parameters with the same amount of volume fraction and total structural volume.

• Structural Engineering and Mechanics
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• v.65 no.2
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• pp.173-181
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• 2018

In this paper, finite element (FE) model updating based on multi-objective optimization with the surrogate model for a steel plate girder bridge is investigated. Conventionally, FE model updating for bridge structures uses single-objective optimization with finite element analysis (FEA). In the case of the conventional method, computational burden occurs considerably because a lot of iteration are performed during the updating process. This issue can be addressed by replacing FEA with the surrogate model. The other problem is that the updating result from single-objective optimization depends on the condition of the weighting factors. Previous studies have used the trial-and-error strategy, genetic algorithm, or user's preference to obtain the most preferred model; but it needs considerable computation cost. In this study, the FE model updating method consisting of the surrogate model and multi-objective optimization, which can construct the Pareto-optimal front through a single run without considering the weighting factors, is proposed to overcome the limitations of the single-objective optimization. To verify the proposed method, the results of the proposed method are compared with those of the single-objective optimization. The comparison shows that the updated model from the multi-objective optimization is superior to the result of single-objective optimization in calculation time as well as the relative errors between the updated model and measurement.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.8
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• pp.1898-1909
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• 1993

In this paper, a convenient scheme for solving multi-objective optimization problems including fuzzy information in both objective functions and constraints is presented. At first, a multi-objective problem is converted into single objective problem based on the norm method, and a merbership function is constructed by selecting its type and providing the parameters defined by the norm method. Finally, this fuzzy programming problem is converted into an ordinary optimization problem which can be solved by usual nonlinear programming techniques. With this scheme, a designer can conveniently obtain pareto optimal solutions of a fuzzy system only by providing some parameters corresponding to the importance of the objectiv functions. Proposed scheme is simple and efficient in treating multi-objective fuzzy systems compared with and method by with membership function value is provided interactively. To show the validity of the scheme, a simple 3-bar truss example and optimal cutting problem are solved, and the results show that the scheme is very useful and easy to treat multi-objective fuzzy systems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.255-262
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• 2002

In this paper, an improved multi-objective optimmum design method is proposed. And it is applied to steel frames under seismic loads. The multi-objective optimization problem is formulated with three optimality criteria, namely, minimum structural weight and maximum strain energy and stability. The Pareto curve can be obtained by performing the multi-objective optimization for multistory shear buildings. In order to efficiently solve the multi-objective optimization problem the decomposition method that separates both system-level and element-level is used. In addition, various techniques such as effective reanalysis technique with respect to intermediate variables and sensitivity analysis using an automatic differentiation (AD) we incorporated. Moreover, the relationship function among section properties induced from the profile is used in order to link system-level and element level. From the results of numerical investigation, it may be stated that the proposed method will lead to the more rational design compared with the conventional one.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.203-211
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• 2022

Multi-objective optimization problems (MOPs) arise in many real-world applications. MOPs involve two or more objectives with the aim to be optimized. With these problems improvement of one objective may led to deterioration of another. The primary goal of most multi-objective evolutionary algorithms (MOEA) is to generate a set of solutions for approximating the whole or part of the Pareto optimal front, which could provide decision makers a good insight to the problem. Over the last decades or so, several different and remarkable multi-objective evolutionary algorithms, have been developed with successful applications. However, MOEAs are still in their infancy. The objective of this research is to study how to use and apply machine learning (ML) to improve evolutionary multi-objective optimization (EMO). The EMO method is the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D has become one of the most widely used algorithmic frameworks in the area of multi-objective evolutionary computation and won has won an international algorithm contest.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.108-113
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• 2002

This paper presents a multi-step structural design optimization method fur machine tool structures using a genetic algorithm with dynamic penalty. The first step is a sectional topology optimization, which is to determine the best sectional construction that minimize the structural weight and the compliance responses subjected to some constraints. The second step is a static design optimization, in which the weight and the static compliance response are minimized under some dimensional and safety constraints. The third step is a dynamic design optimization, where the weight static compliance, and dynamic compliance of the structure are minimized under the same constraints. The proposed design method was examined on the 10-bar truss problem of topology and sizing optimization. And the results showed that our solution is better than or just about the same as the best one of the previous researches. Furthermore, we applied this method to the topology and sizing optimization of a crossbeam slider for a high-speed machining center. The topology optimization result gives the best desirable cross-section shape whose weight was reduced by 38.8% than the original configuration. The subsequent static and dynamic design optimization reduced the weight, static and dynamic compliances by 5.7 %, 2.1% and 19.1% respectively from the topology-optimized model. The examples demonstrated the feasibility of the suggested design optimization method.