• Title/Summary/Keyword: Hybrid Genetic Algorithm

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Hybrid Optimization Techniques Using Genetec Algorithms for Auto-Tuning Fuzzy Logic Controllers (유전 알고리듬을 이용한 자동 동조 퍼지 제어기의 하이브리드 최적화 기법)

  • Ryoo, Dong-Wan;Lee, Young-Seog;Park, Youn-Ho;Seo, Bo-Hyeok
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.48 no.1
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    • pp.36-43
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    • 1999
  • This paper proposes a new hybrid genetic algorithm for auto-tuning fuzzy controllers improving the performance. In general, fuzzy controllers use pre-determined moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a hybrid genetic algorithm. The object of the proposed algorithm is to promote search efficiency by the hybrid optimization technique. The proposed hybrid genetic algorithm is based on both the standard genetic algorithm and a modified gradient method. If a maximum point is not be changed around an optimal value at the end of performance during given generation, the hybrid genetic algorithm searches for an optimal value using the the initial value which has maximum point by converting the genetic algorithms into the MGM(Modified Gradient Method) algorithms that reduced the number of variables. Using this algorithm is not only that the computing time is faster than genetic algorithm as reducing the number of variables, but also that can overcome the disadvantage of genetic algoritms. Simulation results verify the validity of the presented method.

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An Application of a Hybrid Genetic Algorithm on Missile Interceptor Allocation Problem (요격미사일 배치문제에 대한 하이브리드 유전알고리듬 적용방법 연구)

  • Han, Hyun-Jin
    • Journal of the military operations research society of Korea
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    • v.35 no.3
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    • pp.47-59
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    • 2009
  • A hybrid Genetic Algorithm is applied to military resource allocation problem. Since military uses many resources in order to maximize its ability, optimization technique has been widely used for analysing resource allocation problem. However, most of the military resource allocation problems are too complicate to solve through the traditional operations research solution tools. Recent innovation in computer technology from the academy makes it possible to apply heuristic approach such as Genetic Algorithm(GA), Simulated Annealing(SA) and Tabu Search(TS) to combinatorial problems which were not addressed by previous operations research tools. In this study, a hybrid Genetic Algorithm which reinforces GA by applying local search algorithm is introduced in order to address military optimization problem. The computational result of hybrid Genetic Algorithm on Missile Interceptor Allocation problem demonstrates its efficiency by comparing its result with that of a simple Genetic Algorithm.

A Hybridization of Adaptive Genetic Algorithm and Particle Swarm Optimization for Numerical Optimization Functions

  • Yun, Young-Su;Gen, Mitsuo
    • Proceedings of the Korea Society for Industrial Systems Conference
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    • 2008.10b
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    • pp.463-467
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    • 2008
  • Heuristic optimization using hybrid algorithms have provided a robust and efficient approach for solving many optimization problems. In this paper, a new hybrid algorithm using adaptive genetic algorithm (aGA) and particle swarm optimization (PSO) is proposed. The proposed hybrid algorithm is applied to solve numerical optimization functions. The results are compared with those of GA and other conventional PSOs. Finally, the proposed hybrid algorithm outperforms others.

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A Study on a Hybrid Genetic Algorithm for the Analysis of Inverse Radiation (역복사 해석을 위한 혼합형 유전 알고리듬에 관한 연구)

  • Kim, Ki-Wan;Baek, Seung-Wook;Kim, Man-Young;Ryou, Hong-Sun
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.27 no.10
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    • pp.1516-1523
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    • 2003
  • An inverse radiation analysis is presented for the estimation of the boundary emissivities for an absorbing, emitting, and scattering media with diffusely emitting and reflecting opaque boundaries. The finite-volume method is employed to solve the radiative transfer equation for a two-dimensional irregular geometry. A hybrid genetic algorithm is proposed for improving the efficiency of the genetic algorithm and reducing the effects of genetic parameters on the performance of the genetic algorithm. After verifying the performance of the proposed hybrid genetic algorithm, it is applied to inverse radiation analysis in estimating the wall emissivities in a two-dimensional irregular medium when the measured temperatures are given at only four data positions. The effect of measurement errors on the estimation accuracy is examined.

Parameter Identification Using Hybrid Neural-Genetic Algorithm in Electro-Hydraulic Servo System (신경망-유전자 알고리즘을 이용한 전기${\cdot}$유압 서보시스템의 파라미터 식별)

  • 곽동훈;정봉호;이춘태;이진걸
    • Journal of the Korean Society for Precision Engineering
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    • v.19 no.11
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    • pp.192-199
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    • 2002
  • This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are consist of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.

Parameter Identification of an Electro-Hydraulic Servo System Using a Modified Hybrid Neural-Genetic Algorithm (전기.유압 서보시스템의 수정된 신경망-유전자 알고리즘에 의한 파라미터 식별)

  • 곽동훈;이춘태;정봉호;이진걸
    • Journal of Institute of Control, Robotics and Systems
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    • v.9 no.6
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    • pp.442-447
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    • 2003
  • This paper demonstrates that a modified hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. The modified hybrid neural-genetic multimodel parameter estimation algorithm is applied to an electro-hydraulic servo system the task to find the parameter values such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimizes the total square error.

Parameter Identification of an Electro-Hydraulic Servo System Using an Improved Hybrid Neural-Genetic Multimodel Algorithm (개선된 신경망-유전자 다중모델에 의한 전기.유압 서보시스템의 파라미터 식별)

  • 곽동훈;정봉호;이춘태;이진걸
    • Journal of the Korean Society for Precision Engineering
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    • v.20 no.5
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    • pp.196-203
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    • 2003
  • This paper demonstrates that an improved hybrid neural-genetic multimodel parameter estimation algorithm can be applied to the structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm, The ICRA neural network evaluates each member of a generation of model and the genetic algorithm produces new generation of model. We manufactured an electro-hydraulic servo system and the improved hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values, such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimize total square error.

Nonlinear Blind Equalizer Using Hybrid Genetic Algorithm and RBF Networks

  • Han, Soo-Whan;Han, Chang-Wook
    • Journal of Korea Multimedia Society
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    • v.9 no.12
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    • pp.1689-1699
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    • 2006
  • A nonlinear channel blind equalizer by using a hybrid genetic algorithm, which merges a genetic algorithm with simulated annealing, and a RBF network is presented. In this study, a hybrid genetic algorithm is used to estimate the output states of a nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. From these estimated output states, the desired channel states of the nonlinear channel are derived and placed at the center of a RBF equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a conventional genetic algorithm(GA) and a simplex GA, and the relatively high accuracy and fast convergence of the method are achieved.

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An Optimization Method Based on Hybrid Genetic Algorithm for Scramjet Forebody/Inlet Design

  • Zhou, Jianxing;Piao, Ying;Cao, Zhisong;Qi, Xingming;Zhu, Jianhong
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • 2008.03a
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    • pp.469-475
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    • 2008
  • The design of a scramjet inlet is a process to search global optimization results among those factors influencing the geometry of scramjet in their ranges for some requirements. An optimization algorithm of hybrid genetic algorithm based on genetic algorithm and simplex algorithm was established for this purpose. With the sample provided by a uniform method, the compressive angles which also are wedge angles of the inlet were chosen as the inlet design variables, and the drag coefficient, total pressure recovery coefficient, pressure rising ratio and the combination of these three variables are designed specifically as different optimization objects. The contrasts of these four optimization results show that the hybrid genetic algorithm developed in this paper can capably implement the optimization process effectively for the inlet design and demonstrate some good adaptability.

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Design of Auto-Tuning Fuzzy Logic Controllers Using Hybrid Genetic Algorithms (하이브리드 유전 알고리듬을 이용한 자동 동조 퍼지 제어기의 설계)

  • Ryoo, Dong-Wan;Kwon, Jae-Cheol;Park, Seong-Wook;Seo, Bo-Hyeok
    • Proceedings of the KIEE Conference
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    • 1997.11a
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    • pp.126-129
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    • 1997
  • This paper propose a new hybrid genetic algorithm for auto-tunig auzzy controller improving the performance. In general, fuzzy controller used pre-determine d moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controller, using hybrid genetic algorithms. The object of the proposed algorithm is to promote search efficiency by overcoming a premature convergence of genetic algorithms. Hybrid genetic algorithm is based on genetic algorithm and modified gradient method. Simulation results verify the validity of the presented method.

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