• Structural Engineering and Mechanics
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• v.77 no.6
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• pp.779-795
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• 2021

In the recent decades, various optimization algorithms have been considered for the optimization of structures. In this research, a new enhanced algorithm is used for the size and topology optimization of truss structures. This algorithm, which is obtained from the combination of Crow Search Algorithm (CSA) and the Cellular Automata (CA) method, is called CA-CSA method. In the first iteration of the CA-CSA method, some of the best designs of the crow's memory are first selected and then located in the cells of CA. Then, a random cell is selected from CA, and the best design is chosen from the selected cell and its neighborhood; it is considered as a "local superior design" (LSD). In the optimization process, the LSD design is used to modify the CSA method. Numerical examples show that the CA-CSA method is more effective than CSA in the size and topology optimization of the truss structures.

• Structural Engineering and Mechanics
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• v.76 no.6
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• pp.799-821
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• 2020

The optimum design of truss structures is one of the significant categories in structural optimization that has widely been applied by researchers. In the present study, new mathematical programming called Consistent Approximation (CONAP) method is utilized for the simultaneous optimization of the size and shape of truss structures. The CONAP algorithm has already been introduced to optimize some structures and functions. In the CONAP algorithm, some important parameters are designed by employing design sensitivities to enhance the capability of the method and its consistency in various optimum design problems, especially structural optimization. The cross-sectional area of the bar elements and the nodal coordinates of the truss are assumed to be the size and shape design variables, respectively. The displacement, allowable stress and the Euler buckling stress are taken as the design constraints for the problem. In the proposed method, the primary optimization problem is replaced with a sequence of explicit sub-problems. Each sub-problem is efficiently solved using the sequential quadratic programming (SQP) algorithm. Several truss structures are designed by employing the CONAP method to illustrate the efficiency of the algorithm for simultaneous shape and size optimization. The optimal solutions are compared with some of the mathematical programming algorithms, the approximation methods and metaheuristic algorithms those reported in the literature. Results demonstrate that the accuracy of the optimization is improved and the convergence rate speeds up.

• Journal of Korean Association for Spatial Structures
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• v.10 no.4
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• pp.59-66
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• 2010

This study presents the topology optimization technique by density method to determine the initial shape of space truss structures. Most initial shape design is performed by designer's previous experiences and trial and error method instead of the application of reasonable optimization method. Thus, the reasonable and economical optimization methods are needed to be introduced for the initial shape design. Therefore, we set design domain for cantilever space truss structure as an example model. And topology optimization is used to obtain optimum layout for them, and then size optimization method is used to find the optimum member size. Therefore, the reasonable initial optimal shapes of spatial truss structures can be obtained through the topology and size optimization using density method.

• Journal of Korean Society of Steel Construction
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• v.12 no.5 s.48
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• pp.515-525
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• 2000

The purpose of this paper was to develop size & shape optimization programming algorithm of plane trusses. The optimum techniques applied in this study were extended penalty method of Sequential Unconstrained Minimization Techniques(SUMT) and direct search method with multi-variables proposed by Hooke & Jeeves. Upper mentioned two methods were used iteratively at each level of size and shape optimization routines. The design variables of size optimization were circular steel tube(structural member) diameter and thickness, those of shape optimization were joint coordinates, and the objective function was represented as total weight of truss. During the optimum design, two level procedures of size and shape optimization were interacted iteratively until the final optimum values were attained. At the previous studies about shape optimization of truss, the member sectional areas and coordinates were applied as design variables. So that they could not apply the buckling effect of compression member. In this paper, actual sizes of member and nodal coordinates are used as design variables to consider the buckling effect of compression member properly.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.2 s.21
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• pp.22-29
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• 2005

In this study we performed topology optimization and size optimization about support structure of pressure container which is installed in a Common Bed. The optimization study shows that structure weight optimization results can be applied to navy ship. The topology optimization is performed by static load, homogenization and optimality criteria method and size optimization is performed by SOL200 of NASTRAN.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.2
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• pp.123-130
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• 2004

The automobile suspension system is composed of parts that affect performances of a vehicle such as ride quality, handling characteristics, straight performance and steering effort, etc. Moreover, by using the finite element analysis the cost for the initial design step can be decreased. In the design of a suspension system, usually system vibration and structural rigidity must be considered simultaneously to satisfy dynamic and static requirements simultaneously. In this paper, we consider the weight reduction and the increase of the first eigen-frequency of a suspension part, the upper control arm, especially using topology optimization and size optimization. Firstly, we obtain the initial design to maximize the first eigen-frequency using topology optimization. Then, we apply the multi-objective parameter optimization method to satisfy both the weight reduction and the increase of the first eigen-frequency. The design variables are varying during the optimization process for the multi-objective. Therefore, we can obtain the deterministic values of the design variables not only to satisfy the terms of variation limits but also to optimize the two design objectives at the same time. Finally, we have executed reliability based optimal design on the upper control arm using the Monte-Carlo method with importance sampling method for the optimal design result with 98％ reliability.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.170-177
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• 2014

This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.8
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• pp.1218-1228
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• 1997

The move limit strategy is used to avoid the excessive approximation in the structural optimization. The size of move limit has been obtained by engineering experience. Recently, efforts based on analytic methods are performed by some researchers. These methods still have problems, such as prematurity or oscillation of the move limit size. The existing methods usually control the bound of design variables based on the magnitude. Thus, they can not properly handle the configuration variables based on the geometry in the configuration optimization. In this research, the size of move limit is calculated based on the accuracy of approximation. The method is coded and applied to the two-point reciprocal quadratic approximation method. The efficiency is evaluated through examples.

• Structural Engineering and Mechanics
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• v.66 no.4
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• pp.465-476
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• 2018

In this paper, a new meta-heuristic optimization method is presented. This new method is named "Numbers Cup Optimization" (NCO). The NCO algorithm is inspired by the sport competitions. In this method, the objective function and the design variables are defined as the team and the team members, respectively. Similar to all cups, teams are arranged in groups and the competitions are performed in each group, separately. The best team in each group is determined by the minimum or maximum value of the objective function. The best teams would be allowed to the next round of the cup, by accomplishing minor changes. These teams get grouped again. This process continues until two teams arrive the final and the champion of the Numbers Cup would be identified. In this algorithm, the next cups (same iterations) will be repeated by the improvement of players' performance. To illustrate the capabilities of the proposed method, some standard functions were selected to optimize. Also, size optimization of three benchmark trusses is performed to test the efficiency of the NCO approach. The results obtained from this study, well illustrate the ability of the NCO in solving the optimization problems.

• Journal of Mechanical Science and Technology
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• v.18 no.2
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• pp.253-261
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• 2004

A simple and effective filtering method to control the member size of an optimized structure is proposed for topology optimization. In the present approach, the original objective sensitivities are replaced with their relative values evaluated within a filtering area. By adjusting the size of the filtering area, the member size of an optimized structure or the level of its topological complexity can be controlled even within a given finite element mesh. In contrast to the checkerboard-free filter, the present filter focuses on high-frequency components of the sensitivities. Since the present filtering method does not add a penalty term to the objective function nor require additional constraints, it is not only efficient but also simple to implement. Mean compliance minimization and eigenfrequency maximization problems are considered to verify the effectiveness of the present approach.