• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.1
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• pp.142-156
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• 2015

A new method for ship Inner Shell optimization, which is called Parametric Inner Shell Optimization Method (PISOM), is presented in this paper in order to improve both hull performance and design efficiency of transport ship. The foundation of PISOM is the parametric Inner Shell Plate (ISP) model, which is a fully-associative model driven by dimensions. A method to create parametric ISP model is proposed, including geometric primitives, geometric constraints, geometric constraint solving etc. The standard optimization procedure of ship ISP optimization based on parametric ISP model is put forward, and an efficient optimization approach for typical transport ship is developed based on this procedure. This approach takes the section area of ISP and the other dominant parameters as variables, while all the design requirements such as propeller immersion, fore bottom wave slap, bridge visibility, longitudinal strength etc, are made constraints. The optimization objective is maximum volume of cargo oil tanker/cargo hold, and the genetic algorithm is used to solve this optimization model. This method is applied to the optimization of a product oil tanker and a bulk carrier, and it is proved to be effective, highly efficient, and engineering practical.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.5
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• pp.589-595
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• 2010

This paper presents a reliability-based shape optimization (RBSO) using the evolutionary structural optimization (ESO). An actual design involves uncertain conditions such as material property, operational load, poisson's ratio and dimensional variation. The deterministic optimization (DO) is obtained without considering of uncertainties related to the uncertainty parameters. However, the RBSO can consider the uncertainty variables because it has the probabilistic constraints. In order to determine whether the probabilistic constraint is satisfied or not, simulation techniques and approximation methods are developed. In this paper, the reliability-based shape design optimization method is proposed by utilization the reliability index approach (RIA), performance measure approach (PMA), single-loop single-vector (SLSV), adaptive-loop (ADL) are adopted to evaluate the probabilistic constraint. In order to apply the ESO method to the RBSO, a sensitivity number is defined as the change of strain energy in the displacement constraint. Numerical examples are presented to compare the DO with the RBSO. The results of design example show that the RBSO model is more reliable than deterministic optimization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.564-582
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• 2015

This paper proposes a novel swarm intelligence optimization method which integrates bacterial foraging optimization (BFO) with quantum computing, called quantum bacterial foraging optimization (QBFO) algorithm. In QBFO, a multi-qubit which can represent a linear superposition of states in search space probabilistically is used to represent a bacterium, so that the quantum bacteria representation has a better characteristic of population diversity. A quantum rotation gate is designed to simulate the chemotactic step for the sake of driving the bacteria toward better solutions. Several tests are conducted based on benchmark functions including multi-peak function to evaluate optimization performance of the proposed algorithm. Numerical results show that the proposed QBFO has more powerful properties in terms of convergence rate, stability and the ability of searching for the global optimal solution than the original BFO and quantum genetic algorithm. Furthermore, we examine the employment of our proposed QBFO for cognitive radio spectrum allocation. The results indicate that the proposed QBFO based spectrum allocation scheme achieves high efficiency of spectrum usage and improves the transmission performance of secondary users, as compared to color sensitive graph coloring algorithm and quantum genetic algorithm.

• Steel and Composite Structures
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• v.21 no.3
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• pp.501-515
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• 2016

Seismic analysis for steel frame structure with brace configuration using topology optimization based on truss-like material model is studied. The initial design domain for topology optimization is determined according to original steel frame structure and filled with truss-like members. Hence the initial truss-like continuum is established. The densities and orientation of truss-like members at any point are taken as design variables in finite element analysis. The topology optimization problem of least-weight truss-like continuum with stress constraints is solved. The orientations and densities of members in truss-like continuum are optimized and updated by fully-stressed criterion in every iteration. The optimized truss-like continuum is founded after finite element analysis is finished. The optimal bracing system is established based on optimized truss-like continuum without numerical instability. Seismic performance for steel frame structures is derived using dynamic time-history analysis. A numerical example shows the advantage for frame structures with brace configuration using topology optimization in seismic performance.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.455-467
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• 2020

Shape design optimization for Blended-wing-body Underwater Gliders (BWBUGs) is usually computationally expensive. In our previous work, a simplified shape optimization (SSO) strategy is proposed to alleviate the computational burden, which optimizes some of the Sectional Airfoils (SAs) instead of optimizing the 3-D shape of the BWBUG directly. Test results show that SSO can obtain a good result at a much smaller computational cost when three SAs are adopted. In this paper, the performance of SSO is investigated with a different number of SAs selected from the BWBUG, and the results are compared with that of the Direct Shape Optimization (DSO) strategy. Results indicate that SSO tends to perform better with more SAs or even outperforms the DSO strategy in some cases, and the amount of saved computational cost also increases when more SAs are adopted, which provides some reference significance and enlarges the applicability range of SSO.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.208-215
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• 2021

Cosmic-ray muon scattering tomography (MST) technology is a new radiation imaging technology with unique advantages. As the performance of its image reconstruction algorithm has a crucial influence on the imaging quality, researches on this algorithm are of great significance to the development and application of this technology. In this paper, a fast inspection algorithm based on clustering analysis for the identification of the existence of nuclear materials is studied and optimized. Firstly, the principles of MST technology and a binned clustering algorithm were introduced, and then several simulation experiments were carried out using Geant4 toolkit to test the effects of exposure time, algorithm parameter, the size and structure of object on the performance of the algorithm. Based on these, we proposed two optimization methods for the clustering algorithm: the optimization of vertical distance coefficient and the displacement of sub-volumes. Finally, several sets of experiments were designed to validate the optimization effect, and the results showed that these two optimization methods could significantly enhance the distinguishing ability of the algorithm for different materials, help to obtain more details in practical applications, and was therefore of great importance to the development and application of the MST technology.

• Smart Structures and Systems
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• v.22 no.5
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• pp.561-574
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• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

• IE interfaces
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• v.20 no.4
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• pp.458-468
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• 2007

Artificial immune systems (AIS) are one of natural computing inspired by the natural immune system. The fault detection, the pattern recognition, the system control and the optimization are major application area of artificial immune systems. This paper gives a concept of artificial immune systems and useful techniques as like the clonal selection, the immune network theory and the negative selection. A concise survey on the optimization problem based on artificial immune systems is generated. The overall performance of artificial immune systems for the optimization problem is discussed.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.155-157
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• 2005

We propose a new category of hybrid multi-layer neural networks with hetero nodes such as Fuzzy Set based Polynomial Neurons (FSPNs) and Polynomial Neurons (PNs). These networks are based on a genetically optimized multi-layer perceptron. We develop a comprehensive design methodology involving mechanisms of genetic optimization and genetic algorithms, in particular. The augmented genetically optimized HFPNN (namely gHFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of HFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HFPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFPNNs quantified through experimentation where we use a number of modeling benchmarks-synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1347-1355
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• 2015

In this study, we compare the recognition performance using PCA and RBFNNs for introducing robust face recognition system to pose variations based on pose estimation. proposed face recognition system uses Honda/UCSD database for comparing recognition performance. Honda/UCSD database consists of 20 people, with 5 poses per person for a total of 500 face images. Extracted image consists of 5 poses using Multiple-Space PCA and each pose is performed by using (2D)²PCA for performing pose classification. Linear polynomial function is used as connection weight of RBFNNs Pattern Classifier and parameter coefficient is set by using Particle Swarm Optimization for model optimization. Proposed (2D)²PCA-based face pose classification performs recognition performance with PCA, (2D)²PCA and RBFNNs.