• Title/Summary/Keyword: Penalty type of genetic algorithm

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A Study on Real-Time Operation Method of Urban Drainage System using Data-Driven Estimation (실시간 자료지향형 예측을 활용한 내배수 시설 운영기법 연구)

  • Son, Ahlong;Kim, Byunghyun;Han, Kunyeun
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.37 no.6
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    • pp.949-963
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    • 2017
  • This study present an efficient way of operating drainage pump station as part of nonstructural measures for reducing urban flood damage. The water level in the drainage pump station was forecast using Neuro-Fuzzy and then operation rule of the drainage pump station was determined applying the genetic algorithm method based on the predicted inner water level. In order to reflect the topographical characteristics of the drainage area when constructing the Neuro-Fuzzy model, the model considering spatial parameters was developed. Also, the model was applied a penalty type of genetic algorithm so as to prevent repeated stops and operations while lowering my highest water level. The applicability of the development model for the five drainage pump stations in the Mapo drainage area was verified. It is considered to be able to effectively manage urban drainage facilities in the development of these operating rules.

Development of an Optimization Algorithm Using Orthogonal Arrays in Discrete Space (직교배열표를 이용한 이산공간에서의 최적화 알고리즘 개발)

  • Yi, Jeong-Wook;Park, Joon-Seong;Lee, Kwon-Hee;Park, Gyung-Jin
    • Proceedings of the KSME Conference
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    • 2001.06c
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    • pp.408-413
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    • 2001
  • The structural optimization is carried out in the continuous design space or discrete design space. Methods for discrete variables such as genetic algorithms are extremely expensive in computational cost. In this research, an iterative optimization algorithm using orthogonal arrays is developed for design in discrete space. An orthogonal array is selected on a discrete design space and levels are selected from candidate values. Matrix experiments with the orthogonal array are conducted. New results of matrix experiments are obtained with penalty functions for constraints. A new design is determined from analysis of means(ANOM). An orthogonal array is defined around the new values and matrix experiments are conducted. The final optimum design is found from iterative process. The suggested algorithm has been applied to various problems such as truss and frame type structures. The results are compared with those from a genetic algorithm and discussed.

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Minimum-weight design of stiffened shell under hydrostatic pressure by genetic algorithm

  • Ghasemi, A.R.;Hajmohammad, M.H.
    • Steel and Composite Structures
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    • v.19 no.1
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    • pp.75-92
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    • 2015
  • In this paper, optimization of cylindrical shells under external pressure to minimize its weight has been studied. Buckling equations are based on standard of ABS underwater vehicles. Dimension and type of circumferential stiffeners, and its distance from each other are assumed as variables of optimization problem. Considering the extent of these variables, genetic algorithms have been used for optimization. To study the effect of hydrostatic pressure on the shell and its fabrication according to the existing standards, geometrical and construction as well as stress and buckling constraints have been used in optimization algorithm and also penalty functions are applied to eliminate weak model. Finally, the best model which has the minimum weight considering the applied pressure has been presented.

Design Methodology of Automotive Wheel Bearing Unit with Discrete Design Variables (이산 설계변수를 포함하고 있는 자동차용 휠 베어링 유닛의 설계방법)

  • 윤기찬;최동훈
    • Transactions of the Korean Society of Automotive Engineers
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    • v.9 no.1
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    • pp.122-130
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    • 2001
  • In order to improve the efficiency of the design process and the quality of the resulting design, this study proposes a design method for determining design variables of an automotive wheel-bearing unit of double-row angular-contact ball bearing type by using a genetic algorithm. The desired performance of the wheel-bearing unit is to maximize system life while satisfying geometrical and operational constraints without enlarging mounting spae. The use of gradient-based optimization methods for the design of the unit is restricted because this design problem is characterized by the presence of discrete design variables such as the number of balls and standard ball diameter. Therefore, the design problem of rolling element bearings is a constrained discrete optimization problem. A genetic algorithm using real coding and dynamic mutation rate is used to efficiently find the optimum discrete design values. To effectively deal with the design constraints, a ranking method is suggested for constructing a fitness function in the genetic algorithm. A computer program is developed and applied to the design of a real wheel-bearing unit model to evaluate the proposed design method. Optimum design results demonstrate the effectiveness of the design method suggested in this study by showing that the system life of an optimally designed wheel-bearing unit is enhanced in comparison with that of the current design without any constraint violations.

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Formulation of Knowledge Base for Fuzzy Network Performance Manager with Genetic Algorithm (유전자 알고리즘을 이용한 퍼지네트워크 성능관리기의 지식베이스 생성)

  • Lee, Sang-Ho;Kim, In-Jun;Lee, Kyung-Chang;Lee, Seok
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1996.11a
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    • pp.514-518
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    • 1996
  • This paper focuses on automated generation of the knowledge base for a fuzzy network performance manager in order to satisfy delay constraints imposed on time-critical messages while maintaining as much network capacity as possible for non-time-critical messages. Therefore, the bowlegs base is formulated to minimize a certain penalty function by using a type of genetic algorithm. The efficacy of the formulation method has been demonstrated by a series of simulation experiments.

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Development of an Optimization Algorithm Using Orthogonal Arrays in Discrete Design Space (직교배열표를 이용한 이산공간에서의 최적화 알고리듬 개발)

  • Lee, Jeong-Uk;Park, Jun-Seong;Lee, Gwon-Hui;Park, Gyeong-Jin
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.25 no.10
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    • pp.1621-1626
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    • 2001
  • The structural optimization have been carried out in the continuous design space or in the discrete design space. Methods fur discrete variables such as genetic algorithms , are extremely expensive in computational cost. In this research, an iterative optimization algorithm using orthogonal arrays is developed for design in discrete space. An orthogonal array is selected on a discrete des inn space and levels are selected from candidate values. Matrix experiments with the orthogonal array are conducted. New results of matrix experiments are obtained with penalty functions leer constraints. A new design is determined from analysis of means(ANOM). An orthogonal array is defined around the new values and matrix experiments are conducted. The final optimum design is found from iterative process. The suggested algorithm has been applied to various problems such as truss and frame type structures. The results are compared with those from a genetic algorithm and discussed.