• Structural Engineering and Mechanics
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• v.47 no.2
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• pp.209-225
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• 2013

A procedure employing a Teaching-Learning Based Optimization (TLBO) method is developed to design discrete pin jointed structures. TLBO process consists of two parts: the first part represents learning from teacher and the second part illustrates learning by interaction among the learners. The results are compared with those obtained using other various evolutionary optimization methods considering the best solution, average solution, and computational effort. Consequently, the TLBO algorithm works effectively and demonstrates remarkable performance for the optimization of engineering design applications.

• Structural Engineering and Mechanics
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• v.61 no.3
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• pp.359-370
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• 2017

This study presents a particle swarm optimization algorithm integrated with weighted particle concept and improved fly-back technique. The rationale behind this integration is to utilize the affirmative properties of these new terms to improve the search capability of the standard particle swarm optimizer. Improved fly-back technique introduced in this study can be a proper alternative for widely used penalty functions to handle existing constraints. This technique emphasizes the role of the weighted particle on escaping from trapping into local optimum(s) by utilizing a recursive procedure. On the other hand, it guaranties the feasibility of the final solution by rejecting infeasible solutions throughout the optimization process. Additionally, in contrast with penalty method, the improved fly-back technique does not contain any adjustable terms, thus it does not inflict any extra ad hoc parameters to the main optimizer algorithm. The improved fly-back approach, as independent unit, can easily be integrated with other optimizers to handle the constraints. Consequently, to evaluate the performance of the proposed method on solving the truss weight minimization problems with discrete variables, several benchmark examples taken from the technical literature are examined using the presented method. The results obtained are comparatively reported through proper graphs and tables. Based on the results acquired in this study, it can be stated that the proposed method (integrated particle swarm optimizer, iPSO) is competitive with other metaheuristic algorithms in solving this class of truss optimization problems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1993.04a
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• pp.125-133
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• 1993

Preflex composit girder is intended for a better use on both steel and concrete by introducting prestress into the lower flange concrete with preflection. In Korea, recently preflex bridges are widely used especially for urban construction but the design method depends on the conventional ASD(Allowable Stress Design). This paper suggests an optimization model for the design of preflex composite bridges based on LIFD(Load Resistance Factor Design). The optimization algorithm adopted for the NLP model proposed in the paper is the FTM(Flexible Tolerance Method) which is very efficient for the approximate continuous optimization. For the discrete optimum results, a pesudo discrete optimization is used for the economical round-up to the available dimensions. The economic effectiveness of optimum design based on the LRFD method is investigation by comparing the results with those of the ASD method. Based on applications to the actual design examples, it may be concluded that the optimization model suggested in the paper provides economical but reliable design. And the suggested in the paper provides economical but reliable design. And the computer code for the automatic optimum design of preflex bridges developed in the paper for a CAD system may be successfully used in practice.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.10
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• pp.2150-2158
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• 2002

A recent research and development has the requirement for the optimization to shorten design time of modified or new product model and to obtain more precise engineering solution. General optimization problem must consider many conflicted objective functions simultaneously. Multi-objective optimization treats the multiple objective functions and constraints with design change. But, real engineering problem doesn't describe accurate constraint and objective function owing to the limit of representation. Therefore this study applies variance analysis on the basis of structure analysis and DOE to the vertical roller mill fur portland cement and proposed statistical design model to evaluate the effect of structural modification with design change by performing practical multi-objective optimization considering mass, stress and deflection.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.12
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• pp.3804-3821
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• 1996

The configuration optimization is a structural optimization method which includes the coordinates of a structure as well as the sectional properties in the design variable set. Effective reduction of the weight of discrete structures can be obrained by changing the geometry while satisfying stress, Ei;er bickling, displacement, and frequency constraints, etc. However, the nonlinearity due to the configuration variables may cause the difficulties of the convergence and expensive computational cost. An efficient approximation method for the configuration optimization has been developed to overcome the difficulties. The method approximates the constraint functions based onthe second-order Taylor series expansion with reciprocal design variables. The Hessian matrix is approzimated from the information on previous design points. The developed algotithms are coded and the examples are solved.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.04a
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• pp.195-202
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• 1995

In this study, the optimization program is developed to provide preliminary designs of steel-box girder bridges with minimum cost. The advantages of steel-box girder deck, when comparing with other girder types, are higher torsional rigidity and better resistance against corrosion. To achieve more rational design, systematic design procedure is required, by which the design constraints on steel-box girder are satisfied and the design variables with minimum cost are obtained. In the Proposed optmum design Process, the design variables are forced to be selected from the available discrete value set. The efficiency of the developed program has been verified by companing with previous designed sections and the resulting optimum cost with discrete variables has been compared with those of continuous variables.

• Korean Journal of Computational Design and Engineering
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• v.13 no.1
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• pp.67-75
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• 2008

In spite of rapidly increasing interests in digital manufacturing, there still lacks of a systematic approach in manufacturing line flow analysis via modeling and simulation; currently, the parameters for designing manufacturing line are defined by being solely based on engineers experiences. The paper proposes an application of the genetic algorithm to a discrete event line simulation finding optimal set of parameters for manufacturing line balancing problem. The proposed method has been applied to two example problems-one is a simple manufacturing model and the other for shipyard industry-in order to demonstrate its validity and usefulness.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.516-523
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• 2006

This paper is concerned with the $H_{\infty}$ control problem of 2-D discrete state delay systems described by the Roesser model. The condition for the system to have a specified $H_{\infty}$ performance is derived via the linear matrix inequality (LMI) approach. Furthermore, a design procedure for $H_{\infty}$ state feedback controllers is given by solving a certain LMI. The design problem of optimal $H_{\infty}$ controllers is formulated as a convex optimization problem, which can be solved by existing convex optimization techniques. Simulation results are presented to illustrate the effectiveness of the proposed results.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.129-132
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• 1999

This paper deals with the optimal filtering problem constrained to input noise signal corrupting the measurement output for linear discrete-time systems. The transfer matrix H$_2$and/or H$_{\infty}$ norms are used as criteria in an estimation error sense. In this paper, the mixed $H_2/H_{\infty}$ filtering Problem in lineal discrete-time systems is solved using the LMI approach, yielding a compromise between the H$_2$and H$_{\infty}$ filter designs. This filter design problems we formulated in a convex optimization framework using linear matrix inequalities. A numerical example is presented.

• Advances in Computational Design
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• v.1 no.3
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• pp.275-296
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• 2016

This article presents an adaptive directional differential evolution (ADDE) algorithm and its application in solving discrete sizing truss optimization problems. The algorithm is featured by a new self-adaptation approach and a simple directional strategy. In the adaptation approach, the mutation operator is adjusted in accordance with the change of population diversity, which can well balance between global exploration and local exploitation as well as locate the promising solutions. The directional strategy is based on the order relation between two difference solutions chosen for mutation and can bias the search direction for increasing the possibility of finding improved solutions. In addition, a new scaling factor is introduced as a vector of uniform random variables to maintain the diversity without crossover operation. Numerical results show that the optimal solutions of ADDE are as good as or better than those from some modern metaheuristics in the literature, while ADDE often uses fewer structural analyses.