• Title, Summary, Keyword: Genetic Algorithm

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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.

Path-finding Algorithm using Heuristic-based Genetic Algorithm (휴리스틱 기반의 유전 알고리즘을 활용한 경로 탐색 알고리즘)

  • Ko, Jung-Woon;Lee, Dong-Yeop
    • Journal of Korea Game Society
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    • v.17 no.5
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    • pp.123-132
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    • 2017
  • The path-finding algorithm refers to an algorithm for navigating the route order from the current position to the destination in a virtual world in a game. The conventional path-finding algorithm performs graph search based on cost such as A-Star and Dijkstra. A-Star and Dijkstra require movable node and edge data in the world map, so it is difficult to apply online games with lots of map data. In this paper, we provide a Heuristic-based Genetic Algorithm Path-finding(HGAP) using Genetic Algorithm(GA). Genetic Algorithm is a path-finding algorithm applicable to game with variable environment and lots of map data. It seek solutions through mating, crossing, mutation and evolutionary operations without the map data. The proposed algorithm is based on Binary-Coded Genetic Algorithm and searches for a path by performing a heuristic operation that estimates a path to a destination to arrive at a destination more quickly.

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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    • 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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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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    • 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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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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    • 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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A Study on the Dynamics of Genetic Algorithm Based on Stochastic Differential Equation (유전 알고리즘의 확률 미분방정식에 의한 동역학 분석에 대한 연구)

  • 석진욱;조성원
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.296-300
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    • 1997
  • Recently, the genetic algorithm has been applied to the various types of optimization problems and these attempts have very successfully. However, in most cases on these approaches, there is not given by investigator about to the theoritical analysis. The reason that the analysis of the dynamics for genetic algorithm is not clear, is the probablitic aspect of genetic algorithm. In this paper, we investigate the analysis of the internal dynamics for genetic algorithm using stochastic differential method. In addition, we provide a new genetic algorithm, based on the study of the convergence property for the genetic algorithm.

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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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Multi-Objective Micro-Genetic Algorithm for Multicast Routing (멀티캐스트 라우팅을 위한 다목적 마이크로-유전자 알고리즘)

  • Jun, Sung-Hwa;Han, Chi-Geun
    • IE interfaces
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    • v.20 no.4
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    • pp.504-514
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    • 2007
  • The multicast routing problem lies in the composition of a multicast routing tree including a source node and multiple destinations. There is a trade-off relationship between cost and delay, and the multicast routing problem of optimizing these two conditions at the same time is a difficult problem to solve and it belongs to a multi-objective optimization problem (MOOP). A multi-objective genetic algorithm (MOGA) is efficient to solve MOOP. A micro-genetic algorithm(${\mu}GA$) is a genetic algorithm with a very small population and a reinitialization process, and it is faster than a simple genetic algorithm (SGA). We propose a multi-objective micro-genetic algorithm (MO${\mu}GA$) that combines a MOGA and a ${\mu}GA$ to find optimal solutions (Pareto optimal solutions) of multicast routing problems. Computational results of a MO${\mu}GA$ show fast convergence and give better solutions for the same amount of computation than a MOGA.