• Structural Engineering and Mechanics
• /
• v.63 no.6
• /
• pp.825-835
• /
• 2017

Significant improvements to methodologies on structural damage detection (SDD) have emerged in recent years. However, many methods are related to inversion computation which is prone to be ill-posed or ill-conditioning, leading to low-computing efficiency or inaccurate results. To explore a more accurate solution with satisfactory efficiency, a PSO-INM algorithm, combining particle swarm optimization (PSO) algorithm and an improved Nelder-Mead method (INM), is proposed to solve multi-sample objective function defined based on Bayesian inference in this study. The PSO-based algorithm, as a heuristic algorithm, is reliable to explore solution to SDD problem converted into a constrained optimization problem in mathematics. And the multi-sample objective function provides a stable pattern under different level of noise. Advantages of multi-sample objective function and its superior over traditional objective function are studied. Numerical simulation results of a two-storey frame structure show that the proposed method is sensitive to multi-damage cases. For further confirming accuracy of the proposed method, the ASCE 4-storey benchmark frame structure subjected to single and multiple damage cases is employed. Different kinds of modal identification methods are utilized to extract structural modal data from noise-contaminating acceleration responses. The illustrated results show that the proposed method is efficient to exact locations and extents of induced damages in structures.

• International Journal of Aeronautical and Space Sciences
• /
• v.7 no.1
• /
• pp.1-12
• /
• 2006

In this study, a surrogate model is applied to multi-objective aerodynamic optimization design. For the balanced exploration and exploitation, each objective function is converted into the Expected Improvement (EI) and this value is used as fitness value in the multi-objective optimization instead of the objective function itself. Among the non-dominated solutions about EIs, additional sample points for the update of the Kriging model are selected. The present method was applied to a transonic airfoil design. Design results showed the validity of the present method. In order to obtain the information about design space, two data mining techniques are applied to design results: Analysis of Variance (ANOVA) and the Self-Organizing Map (SOM).

• Journal of Computational Design and Engineering
• /
• v.3 no.3
• /
• pp.179-190
• /
• 2016

A kind of modified epoxy resin sheet molding compounds of the impeller has been designed. Through the test, the non-metal impeller has a better environmental aging performance, but must do the waterproof processing design. In order to improve the stability of the impeller vibration design, the influence of uncertainty factors is considered, and a multi-objective robust optimization method is proposed to reduce the weight of the impeller. Firstly, based on the fluid-structure interaction, the analysis model of the impeller vibration is constructed. Secondly, the optimal approximate model of the impeller is constructed by using the Latin hypercube and radial basis function, and the fitting and optimization accuracy of the approximate model is improved by increasing the sample points. Finally, the micro multi-objective genetic algorithm is applied to the robust optimization of approximate model, and the Monte Carlo simulation and Sobol sampling techniques are used for reliability analysis. By comparing the results of the deterministic, different sigma levels and different materials, the multi-objective optimization of the SMC molding impeller can meet the requirements of engineering stability and lightweight. And the effectiveness of the proposed multi-objective robust optimization method is verified by the error analysis. After the SMC molding and the robust optimization of the impeller, the optimized rate reached 42.5%, which greatly improved the economic benefit, and greatly reduce the vibration of the ventilation system.

• Journal of Korean Society of Steel Construction
• /
• v.12 no.5 s.48
• /
• pp.503-513
• /
• 2000

This study treats the criteria, considering the fuzziness occurred by optimization design. And we applied two weighting methods to show the relative importance of criteria. This study develops multi-objective optimization programs implementing plain stress analysis by FEM and discrete optimization design uniformaly. The developed program performs a sample design of 10-member steel truss. This study can carry over the multi-objective optimization based on total system fuzzy-genetic algorithms while performing the stress analysis and optimization design. Especially, when general optimization with unreliable constraints is cannot be solve this study can make optimization design closed to realistic with fuzzy theory.

• Korean Management Science Review
• /
• v.27 no.1
• /
• pp.91-105
• /
• 2010

We proposed and stratified a selection standard model on Open Source Functional Board which could be found in Web. So we could grasp the weight about Performance Evaluation from the viewpoints of planners, developers, and web disigner professional of views. We suggested applying diverse measurement types in case of item which could chart Evaluation Standards on chosen sample boards. In case of item which couldn't do that, we compared and analyzed it by using selective type of 9 point scaling method on professionalists in every sample board. As a result of weight on upper estimate section of evaluation model chart, the order of importance was convenience(0.334), performance(0.333), function(0.240) and design(0.093) respectively. It indicates that there is more weight on performance and convenience which are hard to be structurally modified than designs and functions that are directly shown to the users. Also, it was evident that opposite results came out when using 9-point scale survey and measurement with objective data such as function and performance. The reason is because the surveyed subject can have his or her own subjectivity and bias unlike objective data. However, objectivity of the administrator is also an important factor thus both two perspectives have to be all considered when selecting the bulletin board.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.1
• /
• pp.95-104
• /
• 2017

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions.

• Proceedings of the KIEE Conference
• /
• 1995.11a
• /
• pp.9-11
• /
• 1995

In this paper, tile squirrel case induction motors required multi-objective function are designed. As the objective function of the optimization program, we select the linear combination of loss and mass of motors by using weighting factors. Optimization process is performed by using the evolution strategy (ES). ES is the algorithm that can find the global minimum. To verify validity of the proposed method, a sample design is tried.

• Proceedings of the KIEE Conference
• /
• 2002.07a
• /
• pp.127-129
• /
• 2002

This paper presents a fuzzy-GA method to resolve dispersed generator placement for distribution systems. The problem formulation considers an objective to reduce power loss costs of distribution systems and the constraints with the number or size of dispersed generators and the deviation of the bus voltage. The main idea of solving fuzzy nonlinear goal programming is to transform the original objective function and constraints into the equivalent multi-objectives functions with fuzzy sets to evaluate their imprecise nature and solve the problem using the proposed genetic algorithm, without any transformation for this nonlinear problem to a linear model or other methods. The method proposed is applied to the sample systems to demonstrate its effectiveness.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.24 no.1
• /
• pp.69-78
• /
• 2011

In this study, an optimization method using multi-objective genetic algorithm(MOGA) has been proposed to develop a fuzzy control algorithm that can effectively control a smart tuned mass damper(TMD). A 76-story benchmark building subjected to wind load was selected as an example structure. The smart TMD consists of 100kN MR damper and the natural period of the smart TMD was tuned to the first mode natural period of the example structure. Damping force of MR damper is controlled to reduce the wind-induced responses of the example structure by a fuzzy logic controller. Two input variables of the fuzzy logic controller are the acceleration of 75th floor and the displacement of the smart TMD and the output variable is the command voltage sent to MR damper. Multi-objective genetic algorithm(NSGA-II) was used for optimization of the fuzzy logic controller and the acceleration of 75th story and the displacement of the smart TMD were used as objective function. After optimization, a series of fuzzy logic controllers which could appropriately reduce both wind responses of the building and smart TMD were obtained. Based on numerical results, it has been shown that the control performance of the smart TMD is much better than that of the passive TMD and it is even better than that of the sample active TMD in some cases.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.49 no.9
• /
• pp.433-445
• /
• 2000

In this paper, optimal tuning problem of power system stabilizer(PSS) using Immune Algorithm(IA) is investigated to improve power system dynamic stability. In proposed method, objective function is represented as antigens. An affinity calculation is embedded within the algorithm for determining the promotion or suppression of antibody. An antibody that most fits the antigen is considered as the solution to PSS tuning problem. The computaton performance by the proposed method is compared with Genetic Algorithm(GA). The porposed PSS using IA has been applied for two sample system, single-machine infinite bus system and multi-machine power system. The performance of the proposed PSS is compared with that of conventional PSS. It is shown that the proposed PSS tuned using immune algorithm is more robust than conventional PSS.