• Steel and Composite Structures
• /
• v.19 no.2
• /
• pp.503-519
• /
• 2015

This paper presents an optimization process using Genetic Algorithm (GA) for minimum weight by selecting suitable standard sections from a specified list taken from American Institute of Steel Construction (AISC). The stress constraints obeying AISC-LRFD (American Institute of Steel Construction-Load and Resistance Factor Design), lateral displacement constraints being the top and inter-storey drift, mid-span deflection constraints for the beams and geometric constraints are considered for optimum design by using GA that mimics biological processes. Optimum designs for three different space frames taken from the literature are carried out first without considering concrete slab effects in finite element analyses for the constraints above and the results are compared with the ones available in literature. The same optimization procedures are then repeated for the case of space frames with composite (steel and concrete) beams. A program is coded in MATLAB for the optimization processes. Results obtained in the study showed that consideration of the contribution of the concrete on the behavior of the floor beams results with less steel weight and ends up with more economical designs.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.29 no.6 s.237
• /
• pp.842-851
• /
• 2005

In this paper, a new method for constrained optimization of noisy functions is proposed. In approximate optimization using response surface methods, if constraints have severe noise, the approximate feasible region defined by approximate constraints is apt to include some of the infeasible region defined by actual constraints. This can cause the approximate optimum to converge into the infeasible region. In the proposed method, the approximate optimization is performed with the approximate constraints shifted by their deviations, which are calculated using a diagonal quadratic response surface method. This can prevent the approximate optimum from converging into the infeasible region. To fit the objective and constraints into diagonal quadratic models, we select the center and 4 additional points along each axis of design variables as experimental points. The deviation of each function is calculated using the differences between the real and approximate function values at the experimental points. A sequential approximate optimization technique based on the trust region algorithm is adopted to manage approximate models. The proposed approach is validated by solving some design problems. The results of the problems show the effectiveness of the proposed method.

• Structural Engineering and Mechanics
• /
• v.45 no.6
• /
• pp.759-777
• /
• 2013

In this paper, a topology optimization method based on the element independent nodal density (EIND) is developed for continuum solids with multiple load cases and multiple constraints. The optimization problem is formulated ad minimizing the volume subject to displacement constraints. Nodal densities of the finite element mesh are used a the design variable. The nodal densities are interpolated into any point in the design domain by the Shepard interpolation scheme and the Heaviside function. Without using additional constraints (such ad the filtering technique), mesh-independent, checkerboard-free, distinct optimal topology can be obtained. Adopting the rational approximation for material properties (RAMP), the topology optimization procedure is implemented using a solid isotropic material with penalization (SIMP) method and a dual programming optimization algorithm. The computational efficiency is greatly improved by multithread parallel computing with OpenMP to run parallel programs for the shared-memory model of parallel computation. Finally, several examples are presented to demonstrate the effectiveness of the developed techniques.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.4
• /
• pp.109-116
• /
• 2002

The solution of the aircraft wing spar cross-section area optimization problem is obtained by the response surface method. The object function of the problem is wing total weight, design variables are spar cross-section areas, constraints are the conditions that the stresses at the each spar is less than the allowable stress. D-Optimal condition is utilized to obtain the experimental points to construct the response surfaces. D-Optimal experimental points are obtained by the commercial software "Deign-Expert". Response values for the object function and constraints for each experimental point are calculated by the NASTRAN. Response surfaces for object function and constraints are approximated from the response values by the least square method. The optimization solution is obtained by the DOT for the response surfaces of object function and constraints. The optimization results obtained from the response surface are compared with the results obtained by the NASTRAN SOL200.

• Journal of Mechanical Science and Technology
• /
• v.20 no.10
• /
• pp.1662-1669
• /
• 2006

Since randomness and uncertainties of design parameters are inherent, the robust design has gained an ever increasing importance in mechanical engineering. The robustness is assessed by the measure of performance variability around mean value, which is called as standard deviation. Hence, constraints in robust optimization problem can be approached as probability constraints in reliability based optimization. Then, the FOSM (first order second moment) method or the AFOSM (advanced first order second moment) method can be used to calculate the mean values and the standard deviations of functions describing constraints and object. Among two methods, AFOSM method has some advantage over FOSM method in evaluation of probability. Nevertheless, it is difficult to obtain the mean value and the standard deviation of objective function using AFOSM method, because it requires that the mean value of function is always positive. This paper presented a special technique to overcome this weakness of AFOSM method. The mean value and the standard deviation of objective function by the proposed method are reliable as shown in examples compared with results by FOSM method.

• Structural Engineering and Mechanics
• /
• v.54 no.3
• /
• pp.453-474
• /
• 2015

A hybrid approach of Particle Swarm Optimization (PSO) and Swallow Swarm Optimization algorithm (SSO) namely Hybrid Particle Swallow Swarm Optimization algorithm (HPSSO), is presented as a new variant of PSO algorithm for the highly nonlinear dynamic truss shape and size optimization with multiple natural frequency constraints. Experimentally validation of HPSSO on four benchmark trusses results in high performance in comparison to PSO variants and to those of different optimization techniques. The simulation results clearly show a good balance between global and local exploration abilities and consequently results in good optimum solution.

• Korean Journal of Optics and Photonics
• /
• v.12 no.2
• /
• pp.109-114
• /
• 2001

Optical system has some optical and mechanical constraints. The constraints should be preserved in optimization of optical system. For the purpose, the constraints are combined with the merit function by using Lagrange's undetermined multipliers. We propose an active optimization control by using the fact that the errors of constraints are corrected with higher priority than the other errors of the merit function. In this control, the errors which have large contribution to the merit function are converted into constraints. At that time, if the errors are corrected at once, the optimization will be unstable because of their non-linearity. Hence we introduce a target rate which represents a fraction of error to be corrected, and the errors are corrected progressively. An optimization program was developed on the bases of the proposed active control, and applied to design a photographic lens system. By using the active control, we could get better results compared with conventional damped least squares method. ethod.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.1
• /
• pp.1-6
• /
• 2002

Hopfield introduced the neural network for linear program with linear constraints. In this paper, Hopfield neural network has been generalized to solve the optimization problems including nonlinear constraints. Also, it has been discussed the methods hew to reconcile optimization problem with neural networks and how to implement the circuits.

• Steel and Composite Structures
• /
• v.30 no.1
• /
• pp.47-55
• /
• 2019

The aim of this paper is to present new and an efficient optimization algorithm called Jaya for the optimum mass of braced dome structures with natural frequency constraints. Design variables of the bar cross-section area and coordinates of the structure nodes were used for size and shape optimization, respectively. The effectiveness of Jaya algorithm is demonstrated through three benchmark braced domes (52-bar, 120-bar, and 600-bar). The algorithm applied is an effective tool for finding the optimum design of structures with frequency constraints. The Jaya algorithm has been programmed in MATLAB to optimize braced dome.

• Bulletin of the Korean Mathematical Society
• /
• v.30 no.1
• /
• pp.1-7
• /
• 1993

Vector optimization problems consist of two or more objective functions and constraints. Optimization entails obtaining efficient solutions. Geoffrion [3] introduced the definition of the properly efficient solution in order to eliminate efficient solutions causing unbounded trade-offs between objective functions. In 1974, Isermann [7] obtained a necessary and sufficient condition for an efficient solution of a linear vector optimization problem with linear constraints and showed that every efficient solution is a properly efficient solution. Since then, many authors [1, 2, 4, 5, 6] have extended the Isermann's results. In particular, Gulati and Islam [4] derived a necessary and sufficient condition for an efficient solution of a linear vector optimization problem with nonlinear constraints, under certain assumptions. In this paper, we consider the following nonlinear vector optimization problem (NVOP): (Fig.) where for each i, f$_{i}$ is a differentiable function from R$^{n}$ into R and g is a differentiable function from R$^{n}$ into R$^{m}$ .