• Journal of Communications and Networks
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• v.9 no.4
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• pp.419-427
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• 2007

In wavelength-division-multiplexing(WDM) networks a link failure may cause the failure of several high-bit-rate optical channels, thereby leading to large data loss. Recently, various protection and restoration mechanisms have been proposed to efficiently deal with this problem in mesh networks. Among them, dedicated path protection(DPP) is a promising candidate because of its ultra-fast restoration time and robustness. In this work we investigate the issue of planning and optimization of WDM networks with DPP. Integer linear programming(ILP), in particular, is one of the most common exact method to solve the design optimization problem for protected WDM networks. Traditional ILP formalizations to solve this problem rely on the classical flow or route formulation approaches, but both these approaches suffer from a excessively high computational burden. In this paper, we present a variable-aggregation method that has the ability of significantly reducing the complexity of the traditional flow formulation. We compare also the computational burden of flow formulation with variable aggregation both with the classical flow and route formulations. The comparison is carried out by applying the three alternative methods to the optimization of two case-study networks.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.37-48
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• 2022

In this study, a hybrid meta-heuristic approach is proposed for solving engineering design optimization problems. Various approaches in many literatures have been proposed to solve engineering optimization problems with various types of decision variables and complex constraints. Unfortunately, however, their efficiencies for locating optimal solution do not be highly improved. Therefore, we propose a hybrid meta-heuristic approach for improving their weaknesses. the proposed GA-VNS-HC approach is combining genetic algorithm (GA) for global search with variable neighborhood search (VNS) and hill climbing (HC) for local search. In case study, various types of engineering design optimization problems are used for proving the efficiency of the proposed GA-VNS-HC approach

• Steel and Composite Structures
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• v.51 no.4
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• pp.363-375
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• 2024

Within the optimization field, addressing the intricate posed by fluidic pressure loads on functionally graded structures with frequency-related designs is a kind of complex design challenges. This paper thus introduces an innovative density-based topology optimization strategy for frequency-constraint functionally graded structures incorporating Darcy's law and a drainage term. It ensures consistent treatment of design-dependent fluidic pressure loads to frequency-related structures that dynamically adjust their direction and location throughout the design evolution. The porosity of each finite element, coupled with its drainage term, is intricately linked to its density variable through a Heaviside function, ensuring a seamless transition between solid and void phases. A design-specific pressure field is established by employing Darcy's law, and the associated partial differential equation is solved using finite element analysis. Subsequently, this pressure field is utilized to ascertain consistent nodal loads, enabling an efficient evaluation of load sensitivities through the adjoint-variable method. Moreover, this novel approach incorporates load-dependent structures, frequency constraints, functionally graded material models, and polygonal meshes, expanding its applicability and flexibility to a broader range of engineering scenarios. The proposed methodology's effectiveness and robustness are demonstrated through numerical examples, including fluidic pressure-loaded frequency-constraint structures undergoing small deformations, where compliance is minimized for structures optimized within specified resource constraints.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.10a
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• pp.141-148
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• 2000

In system design, it is not always possible that all decision makers can cooperate fully and thus avoid conflict. They each control a specified subset of design variables and seek to minimize their own cost functions subject to their individual constraints. However, a system management team makes every effort to coordinate multiple disciplines and overcome such noncooperative environment. Although full cooperation is difficult to achieve, noncooperation also should be avoided as possible. Our approach is to predict the results of their cooperation and generate approximate Pareto set for their multiple objectives. The Pareto set can be obtained according to the degree of one's conceding coupling variables in the other's favor. We employ approximation concept for modelling this coordination and the mutiobjective genetic algorithm for exploring the coupling variable space for obtaining an approximate Pareto set. The approximation management concept is also used for improving the accuracy of the Pareto set. The exploration for the coupling variable space is more efficient because of its smaller dimension than the design variable space. Also, our approach doesn't force the disciplines to change their own way of running analysis and synthesis tools. Since the decision making process is not sequential, the required time can be reduced comparing to the existing multidisciplinary optimization techniques. This approach is applied to some mathematical examples and structural optimization problems.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.45-48
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• 2009

In this paper, we use the optimization design theory based on the finite element method and implement the optimal design of electromagnetic devices using COMSOL interface. COMSOL is one of the commercial EM software. Shape information for the design optimization is extracted by CAD in EM software. To calculate the shape of optimal design, sensitive analysis is applied to the design processing in MATLAB. To achieve the design objective in this paper, objective function is defined. According to the sensitive analysis based on the finite element method, we change the design variable after the sensitivity of the objective function is computed. To verify the proposed method, the results are compared with the initial design.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.2
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• pp.809-818
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• 2019

The current trend in shipyard industry is to reduce the weight of ships to support the reduction of CO₂ emissions. In this study, the stiffened plate was optimized that is used for building most of the ship-structure. Further, this study proposed the hybrid Genetic Algorithm (GA) technique, which combines a genetic algorithm and subsequent optimization methods. The design variables included the number and type of stiffeners, stiffener spacing, and plate thickness. The number and type of stiffeners are discrete design variables that were optimized using the genetic algorithm. The stiffener spacing and plate thickness are continuous design variables that were determined by subsequent optimization. The plate deformation was classified into global and local displacement, resulting in accurate estimations of the maximum displacement. The optimization result showed that the proposed hybrid GA is effective for obtaining optimal solutions, for all the design variables.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.1
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• pp.158-166
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• 2003

The paper describes the development and application of advanced evolutionary computing techniques referred to as micro genetic algorithms ($\mu$GA) in the context of engineering design optimization. The basic concept behind $\mu$GA draws from the use of small size of population irrespective of the bit string length in the representation of design variable. Such strategies also demonstrate the faster convergence capability and more savings in computational resource requirements than simple genetic algorithms (SGA). The paper first explores ten-bar truss design problems to see the optimization performance between $\mu$GA and SGA. Subsequently, $\mu$GA is applied to a realistic engineering design problem in the injection molding process optimization.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.10a
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• pp.277-280
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• 2005

A shape optimization is applied to achieve a design objective in three-dimensional forging processes. In multi-stage forging processes, among the important design aspects, the die shape fur preforming is regarded as the design variable since it influences the forged part relatively higher than the others. The rigid-plastic finite element method and the sensitivity method are employed and formulated to solve a formulated optimization problem. An approximation scheme is also used for the direction search during the optimization. The upset forging of a square box is selected as a test example in order to demonstrate and verify the optimization process of this study. After the optimization, the optimized shape of the die yields a finial product of desire shape.

• The KSFM Journal of Fluid Machinery
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• v.10 no.6
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• pp.7-16
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• 2007

An axial-type fan which operates at the relative total pressure of 671Pa and static pressure of 560Pa with the flow rate of $416.6m^3/min$ is developed with an optimization technique based on the gradient method. Prior to the optimization of fan blade, a three-dimensional axial-type fan blade is designed based on the free-vortex method along the radial direction. Twelve design variables are applied to the optimization of the rotor blade, and one design variable is selected for optimizing a stator which is located behind of the rotor and is used to support a fan driving motor. The total and static pressure are applied to the restriction condition with the operating flowrate on the design point, and the efficiency is chosen as the response variable to be maximized. Through these procedures, an initial axial-fan blade designed by the free vortex method is modified to increase the efficiency with the satisfaction of the operating condition. The optimized fan is tested to compare the aerodynamic performance with an imported same class fan. The test result shows that the optimized fan operates with the satisfaction of restriction conditions, but the imported fan cannot. From the experimental and numerical test, they show that this optimization method improves the fan efficiency and operating pressures of a fan designed by the classical fan design method.

• Journal of Mechanical Science and Technology
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• v.20 no.3
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• pp.366-375
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• 2006

In this paper, it is intended to introduce a method to solve multi-objective optimization problems and to evaluate its performance. In order to verify the performance of this method it is applied for a vertical roller mill for Portland cement. A design process is defined with the compromise decision support problem concept and a design process consists of two steps: the design of experiments and mathematical programming. In this process, a designer decides an object that the objective function is going to pursuit and a non-linear optimization is performed composing objective constraints with practical constraints. In this method, response surfaces are used to model objectives (stress, deflection and weight) and the optimization is performed for each of the objectives while handling the remaining ones as constraints. The response surfaces are constructed using orthogonal polynomials, and orthogonal array as design of experiment, with analysis of variance for variable selection. In addition, it establishes the relative influence of the design variables in the objectives variability. The constrained optimization problems are solved using sequential quadratic programming. From the results, it is found that the method in this paper is a very effective and powerful for the multi-objective optimization of various practical design problems. It provides, moreover, a reference of design to judge the amount of excess or shortage from the final object.