• Title/Summary/Keyword: 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.

Automatic generation of Fuzzy Parameters Using Genetic and gradient Optimization Techniques (유전과 기울기 최적화기법을 이용한 퍼지 파라메터의 자동 생성)

  • Ryoo, Dong-Wan;La, Kyung-Taek;Chun, Soon-Yong;Seo, Bo-Hyeok
    • Proceedings of the KIEE Conference
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    • 1998.07b
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    • pp.515-518
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    • 1998
  • This paper proposes a new hybrid algorithm for auto-tuning fuzzy controllers improving the performance. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a genetic-MGM algorithm. The object of the proposed algorithm is to promote search efficiency by a genetic and modified gradient optimization techniques. The proposed genetic and MGM algorithm is based on both the standard genetic algorithm and a gradient method. If a maximum point don't be changed around an optimal value at the end of performance during given generation, the genetic-MGM algorithm searches for an optimal value using 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 algorithms. Simulation results verify the validity of the presented method.

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Fast and Scalable Path Re-routing Algorithm Using A Genetic Algorithm (유전자 알고리즘을 이용한 확장성 있고 빠른 경로 재탐색 알고리즘)

  • Lee, Jung-Kyu;Kim, Seon-Ho;Yang, Ji-Hoon
    • The KIPS Transactions:PartB
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    • v.18B no.3
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    • pp.157-164
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    • 2011
  • This paper presents a fast and scalable re-routing algorithm that adapts to dynamically changing networks. The proposed algorithm integrates Dijkstra's shortest path algorithm with the genetic algorithm. Dijkstra's algorithm is used to define the predecessor array that facilitates the initialization process of the genetic algorithm. After that, the genetic algorithm re-searches the optimal path through appropriate genetic operators under dynamic traffic situations. Experimental results demonstrate that the proposed algorithm produces routes with less traveling time and computational overhead than pure genetic algorithm-based approaches as well as the standard Dijkstra's algorithm for large-scale networks.

Parameter Estimation of Three-Phase Induction Motor by Using Genetic Algorithm

  • Jangjit, Seesak;Laohachai, Panthep
    • Journal of Electrical Engineering and Technology
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    • v.4 no.3
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    • pp.360-364
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    • 2009
  • This paper suggests the techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase induction machine using genetic algorithm. The parameter estimation procedure is based on the steady-state phase current versus slip and input power versus slip characteristics. The propose estimation algorithm is of non-linear kind based on selection in genetic algorithm. The machine parameters are obtained as the solution of a minimization of objective function by genetic algorithm. Simulation shows good performance of the propose procedures.

Improved Route Search Method Through the Operation Process of the Genetic Algorithm (유전 알고리즘의 연산처리를 통한 개선된 경로 탐색 기법)

  • Ji, Hong-il;Seo, Chang-jin
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.64 no.4
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    • pp.315-320
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    • 2015
  • Proposal algorithm in this paper introduced cells, units of router group, for distributed processing of previous genetic algorithm. This paper presented ways to reduce search delay time of overall network through cell-based genetic algorithm. As a result of performance analysis comparing with existing genetic algorithm through experiments, the proposal algorithm was verified superior in terms of costs and delay time. Furthermore, time for routing an alternative path was reduced in proposal algorithm, in case that a network was damaged in existing optimal path algorithm, Dijkstra algorithm, and the proposal algorithm was designed to route an alternative path faster than Dijkstra algorithm, as it has a 2nd shortest path in cells of the damaged network. The study showed that the proposal algorithm can support routing of alternative path, if Dijkstra algorithm is damaged in a network.

Optimal Design of Composite Laminated Stiffened Structures Using micro Genetic Algorithm (마이크로 유전자 알고리즘을 이용한 복합재 적층 구조물의 최적설계)

  • Yi, Moo-Keun;Kim, Chun-Gon
    • Proceedings of the Korean Society For Composite Materials Conference
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    • 2005.11a
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    • pp.268-271
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    • 2005
  • Researches based on genetic algorithms have been performed in composite laminated structures optimization since 1990. However, conventional genetic algorithms have a disadvantage that its augmentation of calculation costs. A lot of variations have been proposed to improve the performance and efficiency, and micro genetic algorithm is one of them. In this paper, micro Genetic Algorithm was employed in the optimization of laminated stiffened composite structures to maximize the linear critical buckling load and the results from both conventional genetic algorithm and micro genetic algorithm were compared.

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Size, Shape and Topology Optimum Design of Trusses Using Shape & Topology Genetic Algorithms (Shape & Topology GAs에 의한 트러스의 단면, 형상 및 위상최적설계)

  • Park, Choon-Wook;Yuh, Baeg-Youh;Kim, Su-Won
    • 한국공간정보시스템학회:학술대회논문집
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    • 2004.05a
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    • pp.43-52
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    • 2004
  • The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algerian was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

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A Handling Method of Linear Constraints for the Genetic Algorithm (유전알고리즘에서 선형제약식을 다루는 방법)

  • Sung, Ki-Seok
    • Journal of the Korean Operations Research and Management Science Society
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    • v.37 no.4
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    • pp.67-72
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    • 2012
  • In this paper a new method of handling linear constraints for the genetic algorithm is suggested. The method is designed to maintain the feasibility of offsprings during the evolution process of the genetic algorithm. In the genetic algorithm, the chromosomes are coded as the vectors in the real vector space constrained by the linear constraints. A method of handling the linear constraints already exists in which all the constraints of equalities are eliminated so that only the constraints of inequalities are considered in the process of the genetic algorithm. In this paper a new method is presented in which all the constraints of inequalities are eliminated so that only the constraints of equalities are considered. Several genetic operators such as arithmetic crossover, simplex crossover, simple crossover and random vector mutation are designed so that the resulting offspring vectors maintain the feasibility subject to the linear constraints in the framework of the new handling method.

Optimum Design of Piled Raft Foundations using Genetic Algorithm (유전자 알고리즘을 이용한 Piled Raft 기초의 최적설계)

  • 김홍택;강인규;황정순;전응진;고용일
    • Proceedings of the Korean Geotechical Society Conference
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    • 1999.10a
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    • pp.415-422
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    • 1999
  • This paper describes a new optimum design approach for piled raft foundations using the genetic algorithm. The objective function considered is the cost-based total weight of raft and piles. The genetic algorithm is a search or optimization technique based on nature selection. Successive generation evolves more fit individuals on the basis of the Darwinism survival of the fittest. In formulating the genetic algorithm-based optimum design procedure, the analysis of piled raft foundations is peformed based on the 'hybrid'approach developed by Clancy(1993), and also the simple genetic algorithm proposed by the Goldberg(1989) is used. To evaluate a validity of the optimum design procedure proposed based on the genetic algorithm, comparisons regarding optimal pile placement for minimizing differential settlements by Kim et at.(1999) are made. In addition using proposed design procedure, design examples are presented.

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Efficiency Analysis Genetic Algorithm for Job Shop Scheduling with Alternative Routing (대체공정을 고려한 Job Shop 일정계획 수립을 위한 유전알고리즘 효율 분석)

  • Kim, Sang-Cheon
    • Journal of the Korea Computer Industry Society
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    • v.6 no.5
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    • pp.813-820
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    • 2005
  • To develop a genetic algorithm about job shop scheduling with alternative routing, we are performed that genetic algorithm efficiency analysis of job shop scheduling with alternative routing, First, we proposed genetic algorithm for job shop scheduling with alternative routing. Second, we applied genetic algorithm to traditional benchmak problem appraise a compatibility of genetic algorithm. Third, we compared with dispatching rule and genetic algorithm result for problem Park[3].

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