• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1998.04a
• /
• pp.254-261
• /
• 1998

This study presents a discrete optimization of tall steel buildings under multiple drift constraints using a dual method. Dual method can replace the primary optimization problem with a sequence of approximate explicit subproblems. Since each subproblem is convex and separable, it can be efficiently solved by using a dual formulation. Specifically, this study considers the discrete-optimization problem due to the commercial standard steel sections to select member sizes. The results by the proposed method will be compared with those of the conventional optimality criteria method

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.5
• /
• pp.429-434
• /
• 2013

This paper proposes a CPSO (Cooperative Particle Swarm Optimization)-based MPC (Model Predictive Control) scheme to deal with formation control problem of multiple nonholonomic mobile robots. In a distributed MPC framework, each robot needs to optimize control input sequence over a finite prediction horizon considering control inputs of the other robots where their cost functions are coupled by the state variables of the neighboring robots. In order to optimize the control input sequence, a CPSO algorithm is adopted and modified to fit into the formation control problem. Experiments are performed on a group of nonholonomic mobile robots to demonstrate the effectiveness of the proposed CPSO-based MPC for multi-robot formation.

• International Journal of Control, Automation, and Systems
• /
• v.4 no.1
• /
• pp.96-104
• /
• 2006

We propose a path planning method to improve the productivity of AOI (automated optical inspection) machines in PCB (printed circuit board) assembly lines. The path-planning problem is the optimization problem of finding inspection clusters and the visiting sequence of cameras to minimize the overall working time. A unified method is newly proposed to determine the inspection clusters and visiting sequence simultaneously. We apply a hybrid genetic algorithm to solve the highly complicated optimization problem. Comparative simulation results are presented to verify the usefulness of the proposed method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2008.11a
• /
• pp.728-733
• /
• 2008

Though multi-panel structures lined with a poroelastic material have been widely used to reduce sound transmission in various fields, most of the previous works to design them were conducted by repeated analyses or experiments based on initially given configurations or sequences. Therefore, it was difficult to obtain the optimal sequence of multi-panel structures lined with a poroelastic material yielding superior sound isolation capability. In this work, we propose a new design method to sequence a multi-panel structure lined with a poroelastic material having maximized sound transmission loss. Being formulated as a one-dimensional topology optimization problem for a given target frequency, the optimal sequencing of panel-poroelastic layers is systematically carried out in an iterative manner. In this method, a panel layer is expressed as a limiting case of a poroelastic layer to facilitate the optimization process. This means that main material properties of a poroelastic material are treated as Interpolated functions of design variables. The designed sequences of panel-poroelastic layers were shown to be significantly affected by the target frequencies; more panel layers were used at higher target frequencies. The sound transmission loss of the system was calculated by the transfer matrix derived from Biot's theory.

• Journal of the Korean Mathematical Society
• /
• v.47 no.6
• /
• pp.1253-1267
• /
• 2010

This paper considers the unconstrained global optimization with the revised filled function methods. The minimization sequence could leave from a local minimizer to a better minimizer of the objective function through minimizing an auxiliary function constructed at the local minimizer. Some promising numerical results are also included.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.99.6-99
• /
• 2001

The parameter optimization for agile beam radar tracking is addressed to minimize the radar resources that are required to maintain a target under track. The parameters to be optimized include the track-revisit interval and the sequence of pairs of target signal strengths and detection thresholds associated with repeated illumination attempts in each track-revisit. The optimization problem is solved numerically for typical examples.

• Journal of the Optical Society of Korea
• /
• v.4 no.2
• /
• pp.86-88
• /
• 2000

A sequence of functions are examined for the gradient-index AR thin film between two dielectric media and are used as the starting profiles in optimization to improve AR performance. Sinusoidal functions were quite efficient to use as components of the index change in the optimization. It is shown that there exist a number of gradient-index profiles which exhibit excellent AR-performance after control of the gradient-index profiles.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.48 no.6
• /
• pp.411-418
• /
• 2020

In this paper, approximation and optimization methods are proposed for the structural performance of the composite fan blade. Using these methods, we perform the optimal design of the stacking sequence to maximize stiffnesses without changing the mass and the geometric shape of the composite fan blade. In this study, the lamination parameters are introduced to reduce the design variables and space. From the characteristics of lamination parameters, we generate response surface model having a high fitness value. Considering the requirements of the optimal stacking sequence, the multi-objective optimization problem is formulated. We apply the two-step optimization method that combines gradient-based method and genetic algorithm for efficient search of an optimal solution. Finally, the finite element analysis results of the initial and the optimized model are compared to validate the approximation and optimization methods based on the lamination parameters.

• Advances in Computational Design
• /
• v.5 no.3
• /
• pp.209-231
• /
• 2020

In order to evaluate the performance of three heuristic optimization algorithms, namely, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO) for optimal stacking sequence of laminated composite plates with respect to critical buckling load and non-dimensional natural frequencies, a multi-objective optimization procedure is developed using the weighted summation method. Classical lamination theory and first order shear deformation theory are employed for critical buckling load and natural frequency computations respectively. The analytical critical buckling load and finite element calculation schemes for natural frequencies are validated through the results obtained from literature. The comparative study takes into consideration solution and computational time parameters of the three algorithms in the statistical evaluation scheme. The results indicate that particle swarm optimization (PSO) considerably outperforms the remaining two methods for the special problem considered in the study.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.13 no.1
• /
• pp.115-125
• /
• 2021

Simulation-based hull form optimization is a typical HEB (high-dimensional, expensive computationally, black-box) problem. Conventional optimization algorithms easily fall into the "curse of dimensionality" when dealing with HEB problems. A recently proposed Cross-Entropy (CE) optimization algorithm is an advanced stochastic optimization algorithm based on a probability model, which has the potential to deal with high-dimensional optimization problems. Currently, the CE algorithm is still in the theoretical research stage and rarely applied to actual engineering optimization. One reason is that the Monte Carlo (MC) method is used to estimate the high-dimensional integrals in parameter update, leading to a large sample size. This paper proposes an improved CE algorithm based on quasi-Monte Carlo (QMC) estimation using high-dimensional truncated Sobol subsequence, referred to as the QMC-CE algorithm. The optimization performance of the proposed algorithm is better than that of the original CE algorithm. With a set of identical control parameters, the tests on six standard test functions and a hull form optimization problem show that the proposed algorithm not only has faster convergence but can also apply to complex simulation optimization problems.