• Title/Summary/Keyword: hybrid genetic algorithm

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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 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.

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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A Vehicle Routing Problem Which Considers Hard Time Window By Using Hybrid Genetic Algorithm (하이브리드 유전자알고리즘을 이용한 엄격한 시간제약 차량경로문제)

  • Baek, Jung-Gu;Jeon, Geon-Wook
    • Journal of the military operations research society of Korea
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    • v.33 no.2
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    • pp.31-47
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    • 2007
  • The main purpose of this study is to find out the best solution of the vehicle routing problem with hard time window by using both genetic algorithm and heuristic. A mathematical programming model was also suggested in the study. The suggested mathematical programming model gives an optimal solution by using ILOG-CPLEX. This study also suggests a hybrid genetic algorithm which considers the improvement of generation for an initial solution by savings heuristic and two heuristic processes. Two heuristic processes consists of 2-opt and Or-opt. Hybrid genetic algorithm is also compared with existing problems suggested by Solomon. We found better solutions rather than the existing genetic algorithm.

Hybrid Genetic Algorithm for Classifier Ensemble Selection (분류기 앙상블 선택을 위한 혼합 유전 알고리즘)

  • Kim, Young-Won;Oh, Il-Seok
    • The KIPS Transactions:PartB
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    • v.14B no.5
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    • pp.369-376
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    • 2007
  • This paper proposes a hybrid genetic algorithm(HGA) for the classifier ensemble selection. HGA is added a local search operation for increasing the fine-turning of local area. This paper apply hybrid and simple genetic algorithms(SGA) to the classifier ensemble selection problem in order to show the superiority of HGA. And this paper propose two methods(SSO: Sequential Search Operations, CSO: Combinational Search Operations) of local search operation of hybrid genetic algorithm. Experimental results show that the HGA has better searching capability than SGA. The experiments show that the CSO considering the correlation among classifiers is better than the SSO.

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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Adaptive Hybrid Genetic Algorithm Approach for Optimizing Closed-Loop Supply Chain Model (폐쇄루프 공급망 모델 최적화를 위한 적응형혼합유전알고리즘 접근법)

  • Yun, YoungSu;Chuluunsukh, Anudari;Chen, Xing
    • Journal of the Korea Industrial Information Systems Research
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    • v.22 no.2
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    • pp.79-89
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    • 2017
  • The Optimization of a Closed-Loop Supply Chain (CLSC) Model Using an Adaptive Hybrid Genetic Algorithm (AHGA) Approach is Considered in this Paper. With Forward and Reverse Logistics as an Integrated Logistics Concept, The CLSC Model is Consisted of Various Facilities Such as Part Supplier, Product Manufacturer, Collection Center, Recovery Center, etc. A Mathematical Model and the AHGA Approach are Used for Representing and Implementing the CLSC Model, Respectively. Several Conventional Approaches Including the AHGA Approach are Used for Comparing their Performances in Numerical Experiment.

Hybrid Genetic Algorithm Approach using Closed-Loop Supply Chain Model (폐쇄루프 공급망 모델을 이용한 혼합형유전알고리즘 접근법)

  • Yun, YoungSu;Anudari, Chuluunsukh;Chen, Xing
    • Journal of the Korea Industrial Information Systems Research
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    • v.21 no.4
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    • pp.31-41
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    • 2016
  • This paper is to evaluate the performance of a proposed hybrid genetic algorithm (pro-HGA) approach using closed-loop supply chain (CLSC) model. The proposed CLSC model is a integrated supply chain network model both with forward logistics and reverse logistics. In the proposed CLSC model, the reuse, resale and waste disposal using the returned products are taken into consideration. For implementing the proposed CLSC model, two conventional approaches and the pro-HGA are used in numerical experiment and their performances are compared with each other using various measures of performance. The experimental results show that the pro-HGA approach is more efficient in locating optimal solution than the other competing approaches.

An Algorithm for Generating an Optimal Laser-Torch Path to Cut Multiple Parts with Their Own Set of Sub-Parts Inside (2차부재가 포함된 다수의 1차부재를 가공하기 위한 레이저 토치의 절단경로 최적화 알고리즘)

  • Kwon Ki-Bum;Lee Moon-Kyu
    • Journal of Institute of Control, Robotics and Systems
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    • v.11 no.9
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    • pp.802-809
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    • 2005
  • A hybrid genetic algorithm is proposed for the problem of generating laser torch paths to cut a stock plate nested with free-formed parts each having a set of sub-parts. In the problem, the total unproductive travel distance of the torch is minimized. The problem is shown to be formulated as a special case of the standard travelling salesman problem. The hybrid genetic algorithm for solving the problem is hierarchically structured: First, it uses a genetic algorithm to find the cutting path f3r the parts and then, based on the obtained cutting path, sequence of sub-parts and their piercing locations are optimally determined by using a combined genetic and heuristic algorithms. This process is repeated until any progress in the total unproductive travel distance is not achieved. Computational results are provided to illustrate the validity of the proposed algorithm.