• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.11A
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• pp.1079-1085
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• 2007

This Paper Proposes a new optimization algorithm named by GBNSGA-II(Goal-pareto Based Non-dominated Sorting Genetic Algorithm-II) which uses Goal Programming to find non-dominated solutions in NSGA-II. Although the conventional NSGA is very popular to solve multiobjective optimization problem, its high computational complexity, lack of elitism and difficulty of selecting sharing parameter have been considered as problems to be overcome. To overcome these problems, NSGA-II has been introduced as the alternative for multiobjective optimization algorithm preventing aforementioned defects arising in the conventional NSGA. Together with advantageous features of NSGA-II, this paper proposes rather effective optimization algorithm formulated by purposely combining NSGA-II algorithm with GP (Goal Programming) subject to satisfying multiple objectives as possible as it can. By conducting computer simulations, the superiority of the proposed GBNSGA-II algorithm will be verified in the aspects of the effectiveness on optimization process in presence of a priori constrained goals and its fast converging capability.

• Steel and Composite Structures
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• v.14 no.4
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• pp.397-408
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• 2013

This paper deals with multiobjective optimization of symmetrically laminated composite truncated circular conical shells subjected to external uniform pressure load and thermal load. The design objective is the maximization of the weighted sum of the critical buckling load and fundamental frequency. The design variable is the fibre orientations in the layers. The performance index is formulated as the weighted sum of individual objectives in order to obtain optimal solutions of the design problem. The first-order shear deformation theory (FSDT) is used in the mathematical formulation of laminated truncated conical shells. Finally, the effect of different weighting factors, length-to-radius ratio, semi-cone angle and boundary conditions on the optimal design is investigated and the results are compared.

• Communications of the Korean Mathematical Society
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• v.25 no.1
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• pp.139-147
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• 2010

A generalized nondifferentiable fractional optimization problem (GFP), which consists of a maximum objective function defined by finite fractional functions with differentiable functions and support functions, and a constraint set defined by differentiable functions, is considered. Recently, Kim et al. [Journal of Optimization Theory and Applications 129 (2006), no. 1, 131-146] proved optimality theorems and duality theorems for a nondifferentiable multiobjective fractional programming problem (MFP), which consists of a vector-valued function whose components are fractional functions with differentiable functions and support functions, and a constraint set defined by differentiable functions. In fact if $\overline{x}$ is a solution of (GFP), then $\overline{x}$ is a weakly efficient solution of (MFP), but the converse may not be true. So, it seems to be not trivial that we apply the approach of Kim et al. to (GFP). However, modifying their approach, we obtain optimality conditions and duality results for (GFP).

• Steel and Composite Structures
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• v.51 no.6
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• pp.647-659
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• 2024

Given the increased interest in enhancing structural sustainability, the current study sought to apply multiobjective optimization to a footbridge with a steel-concrete composite I-girder structure. It was considered as objectives minimizing the cost for building the structure, the environmental impact assessed by CO2 emissions, and the vertical accelerations created by human-induced vibrations, with the goal of ensuring pedestrian comfort. Spans ranging from 15 to 25 meters were investigated. The resistance of the slab's concrete, the thickness of the slab, the dimensions of the welded steel I-profile, and the composite beam interaction degree were all evaluated as design variables. The optimization problem was handled using the Multiobjective Harmony Search (MOHS) metaheuristic algorithm. The optimization results were used to generate a Pareto front for each span, allowing us to assess the correlations between different objectives. By evaluating the values of design variables in relation to different levels of pedestrian comfort, it was identified optimal values that can be employed as a starting point in predimensioning of the type of structure analyzed. Based on the findings analysis, it is possible to highlight the relationship between the structure's cost and CO2 emission objectives, indicating that cost-effective solutions are also environmentally efficient. Pedestrian comfort improvement is especially feasible in smaller spans and from a medium to a maximum level of comfort, but it becomes expensive for larger spans or for increasing comfort from minimum to medium level.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.3
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• pp.26-38
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• 2020

Mixture design of self-compacting concrete is a typical multi-criteria decision making problem and conventional mixture designs are based on the low level engineering method like trials and errors through iteration method to satisfy the various requirements. This study concerns with performing the straightforward multiobjective design optimization of economic SCC mixture considering relative importances of the various requirements and cost-effectives of SCC. Total five requirements of 28day compressive strength, filling ability, segregation stability, material cost and mass were taken into consideration to prepare the objective function to be formulated in form of the weighted-multiobjective mixture design optimization problem. Economic SCC mixture computational design can be given in a rational way which considering material costs and the relative importances of the requiremets and from the result of this study it is expected that the development of SCC mixtue computational design and the consequent univeral concrete material design optimization methodology can be advanced.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.247-255
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• 2004

The majority of real-world problems encountered by engineers involve simultaneous optimization of competing objectives. In this case instead of single optima, there is a set of alternative trade-offs, generally known as Pareto-optimal solutions. The use of evolutionary algorithms Pareto GA, which was first introduced by Goldberg in 1989, has now become a sort of standard in solving Multiobjective Optimization Problems (MOPs). Though this approach was further developed leading to numerous applications, these applications are based on Pareto ranking and employ the use of the fitness sharing function to maintain diversity. Another scheme for solving MOPs has been presented by J. Nash to solve MOPs originated from Game Theory and Economics. Sefrioui introduced the Nash Genetic Algorithm in 1998. This approach combines genetic algorithms with Nash's idea. Another central achievement of Game Theory is the introduction of an Evolutionary Stable Strategy, introduced by Maynard Smith in 1982. In this paper, we will try to find ESS as a solution of MOPs using our game model based co-evolutionary algorithm. First, we will investigate the validity of our co-evolutionary approach to solve MOPs. That is, we will demonstrate how the evolutionary game can be embodied using co-evolutionary algorithms and also confirm whether it can reach the optimal equilibrium point of a MOP. Second, we will evaluate the effectiveness of our approach, comparing it with other methods through rigorous experiments on several MOPs.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.1
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• pp.144-151
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• 2003

In this paper, an optimum design problem for endosseous implant in dentistry is studied to find best implant design. An optimum design problem is formulated to reduce stresses arising at the cortical as well as cancellous bones, in which sufficient design parameters are chosen fur design definition that encompasses major implants in popular use. Optimization at once (OAO) with the large number of design variables, however, causes too costly solution or even failure to converge. A concept of multilevel optimization (MLO) is employed to this end, which is to group the design variables of similar nature, solve the sub-problem of smaller size fur each group in sequence, and this is iterated until convergence. Each sub-problem is solved based on the response surface method (RSM) due to its efficiency for small sized problem. Favorable solution is obtained by the MLO, which is compared to both solutions made by RSM and sequential quadratic programming (SQP) in the OAO problem.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.1
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• pp.112-123
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• 1997

The optimization techniques have been applied to versatile engineering problems for reducing manufacturing cost and for automatic design. The deterministic approaches or op5imization neglect the effects on uncertainties of design variables. The uncertainties include variation or perturbation such as tolerance band. The optimum may be useless when the constraints considering worst cases of design variables can not be satisfied, which results from constraint variation. The variation of design variables can also give rise to drastic change of performances. The two issues are related to constraint feasibility and insensitive performance. Robust design suggested in the present study is developed to gain an optimum insensitive to variation on design variables within feasible region. The multiobjective function is composed to the mean and the standard deviation of original objective function, while the constraints are supplemented by adding penalty term to original constraints. This method has a advantage that the second derivatives of the constraints are not required. A mathematical problem and several standard problems for structural optimization are solved to check out the usefulness of the suggested method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.4
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• pp.10-18
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• 2002

Network topology design is a multiobjective problem with various design components. The components such as cost, message delay and reliability are important to gain the best performance. Recently, Genetic Algorithms(GAs) have been widely used as an optimization method for real-world problems such as combinatorial optimization, network topology design, and so on. This paper proposed a method of Multi-objective GA for Design of the network topology which is to minimize connection cost and message delay time. A common difficulty in multiobjective optimization is the existence of an objective conflict. We used the prufer number and cluster string for encoding, parato elimination method and niche-formation method for the fitness sharing method, and reformation elitism for the prevention of pre-convergence. From the simulation, the proposed method shows that the better candidates of network architecture can be found.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.7
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• pp.813-820
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• 2010

The vertical roller mill is important for machine grinding and mixing various crude materials in the process of producing Portland cement. A vertical roller mill is subjected to cyclic bending stress because of the roller load. Because of the cyclic bending stress, only $4{\times}10^6-8{\times}10^6$ cycles are achieved instead of $4{\times}10^7$ cycles. The stress also causes fractures at the edge of grinding path of the outer roller. The expenses incurred in repairing the grinding path amounts to 30% of the total maintenance cost. Therefore, it is desirable to redesign the vertical roller mill in order to reduce the expenses incurred in repairing the roller. In this study, artificial neural networks (ANNs) were applied in order to solve the multiobjective optimization problem for vertical roller mills by using the function approximation ability of ANNs. To learn and generalize ANNs, the maximum and minimum stresses were estimated from the results of the finite-element analysis of a vertical roller mill. Thus, ANNs could be applied to solve the multiobjective optimization problem.