• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2215-2221
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• 2023

In order to further meet the requirements of weight, volume, and dose minimization for new nuclear energy devices, the bare-bones multi-objective particle swarm optimization algorithm is used to automatically and iteratively optimize the design parameters of radiation shielding system material, thickness, and structure. The radiation shielding optimization program based on the bare-bones particle swarm optimization algorithm is developed and coupled into the reactor radiation shielding multi-objective intelligent optimization platform, and the code is verified by using the Savannah benchmark model. The material type and thickness of Savannah model were optimized by using the BBMOPSO algorithm to call the dose calculation code, the integrated optimized data showed that the weight decreased by 78.77%, the volume decreased by 23.10% and the dose rate decreased by 72.41% compared with the initial solution. The results show that the method can get the best radiation shielding solution that meets a lot of different goals. This shows that the method is both effective and feasible, and it makes up for the lack of manual optimization.

• Journal of Mechanical Science and Technology
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• v.14 no.1
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• pp.57-64
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• 2000

Recent advances in the field of control theory have enabled us to design active vibration control systems for various structures. In many studies, the controller used to suppress vibration has been synthesized for the given mathematical model of structure. In these cases, the designer has not been able to utilize the degree of freedom to adjust the structural parameters of the control object. To overcome this problem, so called 'Structure/Control Simultaneous Optimization Method' is used. In this context of view, this paper is concerned with the active vibration control of bridge towers, platforms and ocean vehicles etc. Simultaneous design method is used to achieve optimal system performance. Here, a general framework for the simultaneous design problem of output feedback case is introduced based on LMI (Linear Matrix Inequality). The simulation results show that the proposed design method achieves desirable control performance.

• Journal of Power Electronics
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• v.6 no.4
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• pp.322-329
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• 2006

In the last decade, the increasing restrictions imposed on the exhaust emissions from internal combustion engines and traffic limitations have increased the development of electrical propulsion systems for automotive applications. The goal of electrical and hybrid vehicles is the reduction of global emissions, which in turn leads to a decrease in fuel resource exploitation. This paper presents a novel approach for control of Induction Motors (IM) using the Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the Proportional Integral Controller (PI-Controller). The overall system is simulated under various operating conditions. The use of PSO as an optimization algorithm makes the drive robust and insensitive to load variation with faster dynamic response and higher accuracy. The system is tested under variable operating conditions. The simulation results show a positive dynamic response with fast recovery time.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.595-597
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• 2002

This paper presents an optimum design, which is able to determine optimal geometric and electric parameters of Switched Reluctance Motor so as to fit respective operating conditions specified in various industrial fields. Those works describe an approach to maximize the average torque while keeping the torque ripple within 10${\sim}$100(%) of respective limited values. This optimum design is obtained by uniting an optimization algorithm of PQRSM to a time stepping finite element method.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2476-2481
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• 2007

An inverse radiation analysis is presented for the estimation of the wall emissivities for an absorbing, emitting, and scattering media with diffusely emitting and reflecting opaque boundaries. In this study, a repulsive particle swarm optimization(RPSO) algorithm which is a relatively recent heuristic search method is proposed as an effective method for improving the search efficiency for unknown parameters. To verify the performance of the proposed RPSO algorithm, it is compared with a basic particle swarm optimization(PSO) algorithm and a hybrid genetic algorithm(HGA) for the inverse radiation problem with estimating the wall emissivities in a two-dimensional irregular medium when the measured temperatures are given at only four data positions. A finite-volume method is applied to solve the radiative transfer equation of a direct problem to obtain measured temperatures.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.680-695
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• 1998

The objective of this study is to optimize the responses of the creep feed ceramic grinding process simultaneously by an off-1ine multicriteria optimization methodology. The responses considered as objectives are material removal rate, flexural strength, normal grinding force, workpiece surface roughness and grinder power. Alumina material was ground by the creep feed grinding mode using superabrasive grinding wheels. The process variables optimized for the above objectives include grinding wheel specification, such as bond type, mesh size, and grit concentration, and grinding process parameters, such as depth of cut and feed rate. A weighting method transforms the multi-objective problem into a single-objective programming format and then, by parametric variation of weights, the set of non-dominated optimum solutions are obtained. Finally, the multi-objective optimization methodology was tested by a sensitivity analysis to check the stability of the model.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.819-826
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• 2001

Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain because they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, hybridizing a genetic algorithm with other algorithms can produce better performance than using the genetic algorithm or other algorithms independently. In this paper, we propose an efficient hybrid optimization algorithm named the adaptive random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural networks. This paper describes the application of genetic algorithms and simulated annealing to a random signal-based learning in order to generate the parameters and reinforcement signal of the random signal-based learning, respectively. The validity of the proposed algorithm is confirmed by applying it to two different examples.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1334-1337
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• 2004

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and expands the basic particle swarm optimization (PSO) to solve it. PSO works with a population of particles, each representing a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. To achieve better performance, the influence of PSO parameter settings on the search performance was investigated. Experiments show that with fine-adjusted parameters, the proposed method leads to a speedup of 94 on 320${\times}$240 images compared to the traditional exhaustive search method.

• International Journal of Naval Architecture and Ocean Engineering
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• v.9 no.4
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• pp.460-472
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• 2017

The design of harbours, as with any other system design, must be an optimization process. In this study, a global examination of the different constraints in coastal engineering was performed and an optimization problem was defined. The problem has multiple objectives, and the criteria to be minimized are the structure cost and wave height disturbance inside a harbour. As concluded in this survey, the constraints are predefined parameters, mandatory constraints or optional constraints. All of these constraints are categorized into four categories: environmental, fluid mechanical, structural and manoeuvring.

• Transactions of Materials Processing
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• v.7 no.3
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• pp.207-214
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• 1998

Influence of final thickness distribution in superplastic forming processes on mechanical properties of the product becomes very crucial. We should improve the thickness distribution of products by combining process parameters adequately In this paper we adopt a non-linear optimization technique for optimal process design of superplastic forming. And optimum design variable which makes the most adequate thickness distribution in combined stretc/blow forming and blow forming is predicted by this optimization scheme and rigid-viscoplastic finite element method.