• Journal of Power Electronics
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• v.15 no.2
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• pp.479-486
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• 2015

The application of soft-switching techniques is increasing in the DC/DC converter area. It is important to design soft-switching parameters to ensure the converter operates properly and efficiently. An optimized design method is presented in this paper. The objective function is the total power loss of a converter, while the variables are soft-switching parameters and the constraints are the electrical requirements for soft-switching. Firstly, a response surface methodology (RSM) model with a high precision is built, and the rough optimized parameters can be obtained with the help of a genetic algorithm (GA) in the solution space determined by the constraints. Secondly, a re-optimization is conducted with a SPICE model and a GA, and accurate optimized parameters can be obtained. Simulation and experiment results show that the proposed method performs well in terms of a wide adaptability, efficiency, and global optimization.

• Transactions of Materials Processing
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• v.30 no.3
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• pp.125-133
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• 2021

In this study, response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goal of optimization is to determine the maximum forming angle and minimum surface roughness, while varying the production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model and BPNN model to model the variations in the forming angle and surface roughness based on variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process the GA. The results showed that RSM and BPNN can be effectively used to control the forming angle and surface roughness. The optimized Pareto front produced by the GA can be utilized as a rational design guide for practical applications of AA5052 in the ISF process

• Proceedings of the Materials Research Society of Korea Conference
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• 1995.11a
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• pp.16-16
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• 1995

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.820-822
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• 1996

This paper proposes a method for seeking the scheme of substation restoration by using genetic algorithm. Genetic algorithm (GA), first introduced by John Holland, is becoming an important tool in machine learning and function optimization. GA is a searching or optimization algorithm based on Darwinian biological evolution principle. As a test system, we assume a simple substation system and for the transformer fault, the result is obtained.

• The Korean Journal of Rheology
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• v.10 no.2
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• pp.113-120
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• 1998

사출성형공정은 대표적인 고분자 가공공정으로 그 복잡한 특성으로 인하여 공정변 수를 최적화하는 것을 주로 경험에 의존해 왔다. 본 연구에서는 사출성형공정의 보압과정 중에 보압의 이력을 최적화하여 제품각 부분의 부피수축율차이를 최소가 되게 하는 최적화 시스템을 개발하였다. 최적화 알고리즘으로는 GA방법을 사용하였으며 본 연구에서 제안한 최적화 시스템으로 보압과정의 최적화를 수행한 결과 부피수축율의 차이가 현저히 감소하는 것을 알수 있었다. 특히 SA방법을 사용하는 경우 초기의 최적화 속도가 GA를 사용하는 경 우에 비해서 뛰어남을 알수 있었다. 또한 충전과정과 보압과정을 함께 최적화하여 보압과정 만 최적화한 결과와 비교하여 보았다.

• Structural Engineering and Mechanics
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• v.66 no.2
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• pp.173-183
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• 2018

This paper proposes a developed optimization model for steel frames with semi-rigid beam-to-column connections and fixed bases using teaching-learning-based optimization (TLBO) and genetic algorithm (GA) techniques. This method uses rotational deformations of frame members ends as an optimization variable to simultaneously obtain the optimum cross-sections and the most suitable beam-to-column connection type. The total cost of members plus connections cost of the frame are minimized. Frye and Morris (1975) polynomial model is used for modeling nonlinearity of semi-rigid connections, and the $P-{\Delta}$ effect and geometric nonlinearity are considered through a stepped analysis process. The stress and displacement constraints of AISC-LRFD (2016) specifications, along with size fitting constraints, are considered in the design procedure. The developed model is applied to three benchmark steel frames, and the results are compared with previous literature results. The comparisons show that developed model using both LTBO and GA achieves better results than previous approaches in the literature.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2115-2123
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• 2006

The paper deals with the identification of Pareto optimal solutions using GA based coevolution in the context of multiobjective optimization. Coevolution is a genetic process by which several species work with different types of individuals in parallel. The concept of cooperative coevolution is adopted to compensate for each of single objective optimal solutions during genetic evolution. The present study explores the GA based coevolution, and develops prescribed and adaptive scheduling schemes to reflect design characteristics among single objective optimization. In the paper, non-dominated Pareto optimal solutions are obtained by controlling scheduling schemes and comparing each of single objective optimal solutions. The proposed strategies are subsequently applied to a three-bar planar truss design and an energy preserving flywheel design to support proposed strategies.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1007-1014
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• 2015

An optimally designed six-phase induction motor (6PIM) is compared with an initial design induction motor having the same ratings. The Genetic Algorithm (GA) method is used for optimization and multi objective function is considered. Comparison of the optimum design with the initial design reveals that better performance can be obtained by a simple optimization method. Also in this paper each design of 6PIM, is simulated by MAXWELL_2D. The obtained simulation results are compared in order to find the most suitable solution for the specified application, considering the influence of each design upon the motor performance. Construction a 6PIM based on the information obtained from GA method has been done. Quality parameters of the designed motors, such as: efficiency, power losses and power factor measured and optimal design has been evaluated. Laboratory tests have proven the correctness of optimal design.

• KSBB Journal
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• v.14 no.4
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• pp.495-502
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• 1999

The optimization of fed-batch yeast fermentation process has been performed using genetic algorithm(GA). Three strategies were designed and applied to obtain the optimal feed rate profiles. Genes in the chromosome (input variables for optimization) included feed rates on fixed time intervals (strategy I), or swiching times $t_s1\;and\;t_s2$, and feed rates on singular arc (strategy II), or feed rates and the length of time interval (strategy III). Strategy III showed the best results for all initial conditions due to efficient utilization of genetic information. Simulation results using GA showed similar or better performance compared with previous results by variational caculus and singular control approach.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.2
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• pp.61-66
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• 2005

This paper presents an unified method which solves real and reactive power dispatch problems for the economic operation of power systems using evolutionary computation such as genetic algorithms(GA), evolutionary programming(EP), and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most of these approaches have the common defect of being caught to a local minimum solution. The proposed methods, applied to the IEEE 30-bus system, were run for 10 other exogenous parameters and composed of P-optimization module and Q-optimization module. Each simulation result, by which evolutionary computations are compared and analyzed, shows the possibility of applications of evolutionary computation to large scale power systems.