• Title/Summary/Keyword: Simple genetic algorithm

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An Analytical Study on System Identification of Steel Beam Structure for Buildings based on Modified Genetic Algorithm (변형 유전 알고리즘을 이용한 건물 철골 보 구조물의 시스템 식별에 관한 해석적 연구)

  • Oh, Byung-Kwan;Choi, Se-Woon;Kim, Yousok;Cho, Tong-Jun;Park, Hyo-Seon
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.27 no.4
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    • pp.231-238
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    • 2014
  • In the buildings, the systems of structures are influenced by the gravity load changes due to room alteration or construction stage. This paper proposes a system identification method establishing mass as well as stiffness to parameters in model updating process considering mass change in the buildings. In this proposed method, modified genetic algorithm, which is optimization technique, is applied to search those parameters while minimizing the difference of dynamic characteristics between measurement and FE model. To search more global solution, the proposed modified genetic algorithm searches in the wider search space. It is verified that the proposed method identifies the system of structure appropriately through the analytical study on a steel beam structure in the building. The comparison for performance of modified genetic algorithm and existing simple genetic algorithm is carried out. Furthermore, the existing model updating method neglecting mass change is performed to compare with the proposed method.

A Study on Development of Program for Estimating Reservoirs Outflow using Genetic Algorithm (유전자알고리즘을 이용한 저수지(貯水池)의 방류량(放流量) 추정(推定) 프로그램 개발 연구)

  • Ahn, Sang-Dae;Kim, Won-Il;Ahn, Byung-Chan;Ahn, Won-Sik
    • Journal of the Korean Society of Hazard Mitigation
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    • v.9 no.6
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    • pp.153-159
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    • 2009
  • In order to estimate release water from reservoirs located on ungaged watersheds, an algorithm was suggested based on hydrologic reservoir routing and real time calibrating watershed parameters. A prototype - simple computer program was developed to implement the algorithm with Genetic Algorithm technic. The program was applied to a mid-size reservoir and its ungauged watershed area using observed rainfall data, spillway gates operation data and reservoir water stage time series data under a existing storm event. The result shows that the algorithm and the prototype would be useful to simulate released water from reservoirs.

Design of PID Controller for Magnetic Levitation RGV Using Genetic Algorithm Based on Clonal Selection (클론선택기반 유전자 알고리즘을 이용한 자기부상 RGV의 PID 제어기 설계)

  • Cho, Jae-Hoon;Kim, Yong-Tae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.22 no.2
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    • pp.239-245
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    • 2012
  • This paper proposes a novel optimum design method for the PID controller of magnetic levitation-based Rail-Guided Vehicle(RGV) by a genetic algorithm using clone selection method and a new performance index function with performances of both time and frequency domain. Generally, since an attraction type levitation system is intrinsically unstable and requires a delicate controller that is designed considering overshoot and settling time, it is difficult to completely satisfy the desired performance through the methods designed by conventional performance indexes. In the paper, the conventional performance indexes are analyzed and then a new performance index for Maglev-based RGV is proposed. Also, an advanced genetic algorithm which is designed using clonal selection algorithm for performance improvement is proposed. To verify the proposed algorithm and the performance index, we compare the proposed method with a simple genetic algorithm and particle swarm optimization. The simulation results show that the proposed method is more effective than conventional optimization methods.

A Distributed Nearest Neighbor Heuristic with Bounding Function (분기 함수를 적용한 분산 최근접 휴리스틱)

  • Kim, Jung-Sook
    • Journal of KIISE:Computer Systems and Theory
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    • v.29 no.7
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    • pp.377-383
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    • 2002
  • The TSP(Traveling Salesman Problem) has been known as NP-complete, there have been various studies to find the near optimal solution. The nearest neighbor heuristic is more simple than the other algorithms which are to find the optimal solution. This paper designs and implements a new distributed nearest neighbor heuristic with bounding function for the TSP using the master/slave model of PVM(Parallel Virtual Machine). Distributed genetic algorithm obtains a near optimal solution and distributed nearest neighbor heuristic finds an optimal solution for the TSP using the near optimal value obtained by distributed genetic algorithm as the initial bounding value. Especially, we get more speedup using a new genetic operator in the genetic algorithm.

Development of a Fuzzy-Genetic Algorithm-based Incident Detection Model with Self-adaptation Capability (Fuzzy-Genetic Algorithm기반의 자가적응형 돌발상황 검지모형 개발 연구)

  • Lee, Si-Bok;Kim, Young-Ho
    • Journal of Korean Society of Transportation
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    • v.22 no.4 s.75
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    • pp.159-173
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    • 2004
  • This study utilizes the fuzzy logic and genetic algorithm to improve the existing incident detection models by addressing the problems associated with "crisp" thresholds and model transferability (applicability). The model's major components were designed to be a set of the fuzzy inference engines, and for the self-adaptation capability the genetic algorithm was introduced in optimization(or training) of the fuzzy membership functions. This approach is often called "the hybrid of fuzzy-genetic algorithm" The model performance was tested and found to be compatible with that of the existing well-recognized models in terms of performance measures such as detection rate, false alarm rate, and detection time. This study was not an effort for simple improvement of the model performance, but an experimental attempt to incorporate new characteristics essential for the incident detection model to be universally applicable for various roadway and traffic conditions. The study results prove that the initial objective of the study was satisfied, and suggest a direction that the future research work in this area must follow.

Optimizing Assembly Line Balancing Problems with Soft Constraints (소프트 제약을 포함하는 조립라인 밸런싱 문제 최적화)

  • Choi, Seong-Hoon;Lee, Geun-Cheol
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.41 no.2
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    • pp.105-116
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    • 2018
  • In this study, we consider the assembly line balancing (ALB) problem which is known as an very important decision dealing with the optimal design of assembly lines. We consider ALB problems with soft constraints which are expected to be fulfilled, however they are not necessarily to be satisfied always and they are difficult to be presented in exact quantitative forms. In previous studies, most researches have dealt with hard constraints which should be satisfied at all time in ALB problems. In this study, we modify the mixed integer programming model of the problem introduced in the existing study where the problem was first considered. Based on the modified model, we propose a new algorithm using the genetic algorithm (GA). In the algorithm, new features like, a mixed initial population selection method composed of the random selection method and the elite solutions of the simple ALB problem, a fitness evaluation method based on achievement ratio are applied. In addition, we select the genetic operators and parameters which are appropriate for the soft assignment constraints through the preliminary tests. From the results of the computational experiments, it is shown that the proposed algorithm generated the solutions with the high achievement ratio of the soft constraints.

GA-Based ORPD considering Transmission Losses Re-Distribution (송전손실 재분배를 고려한 유전 알고리즘 기반의 무효전력 최적배분)

  • Chae, Myung-Suck;Lee, Myung-Hwan;Kim, Byung-Seop;Shin, Joong-Rin
    • Proceedings of the KIEE Conference
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    • 1999.11b
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    • pp.190-192
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    • 1999
  • This paper presents an algorithm for optimal reactive power dispatch problem based on genetic algorithm. Optimal reactive power dispatch is particularized to the minimization of transmission line losses by suitable selection of generator reactive power outputs and transformer tap settings. To attain for the objective, in this paper, loss re-distribution algorithm(LRDA) is applied to ORPD. The proposed method has been evaluated on the IEEE 30 bus system. Results of the application of the method are compared with a simple genetic algorithm.

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Development and Efficiency Evaluation of Metropolis GA for the Structural Optimization (구조 최적화를 위한 Metropolis 유전자 알고리즘을 개발과 호율성 평가)

  • Park Kyun-Bin;Kim Jeong-Tae;Na Won-Bae;Ryu Yeon-Sun
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.19 no.1 s.71
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    • pp.27-37
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    • 2006
  • A Metropolis genetic algorithm (MGA) is developed and applied for the structural design optimization. In MGA, favorable features of Metropolis criterion of simulated annealing (SA) are incorporated in the reproduction operations of simple genetic algorithm (SGA). This way, the MGA maintains the wide varieties of individuals and preserves the potential genetic information of early generations. Consequently, the proposed MGA alleviates the disadvantages of premature convergence to a local optimum in SGA and time consuming computation for the precise global optimum in SA. Performances and applicability of MGA are compared with those of conventional algorithms such as Holland's SGA, Krishnakumar's micro GA, and Kirkpatrick's SA. Typical numerical examples are used to evaluate the computational performances, the favorable features and applicability of MGA. The effects of population sizes and maximum generations are also evaluated for the performance reliability and robustness of MGA. From the theoretical evaluation and numerical experience, it is concluded that the proposed MGA Is a reliable and efficient tool for structural design optimization.

Derivative Evaluation and Conditional Random Selection for Accelerating Genetic Algorithms

  • Jung, Sung-Hoon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.5 no.1
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    • pp.21-28
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    • 2005
  • This paper proposes a new method for accelerating the search speed of genetic algorithms by taking derivative evaluation and conditional random selection into account in their evolution process. Derivative evaluation makes genetic algorithms focus on the individuals whose fitness is rapidly increased. This accelerates the search speed of genetic algorithms by enhancing exploitation like steepest descent methods but also increases the possibility of a premature convergence that means most individuals after a few generations approach to local optima. On the other hand, derivative evaluation under a premature convergence helps genetic algorithms escape the local optima by enhancing exploration. If GAs fall into a premature convergence, random selection is used in order to help escaping local optimum, but its effects are not large. We experimented our method with one combinatorial problem and five complex function optimization problems. Experimental results showed that our method was superior to the simple genetic algorithm especially when the search space is large.

Game Agent Learning with Genetic Programming in Pursuit-Evasion Problem (유전 프로그래밍을 이용한 추격-회피 문제에서의 게임 에이전트 학습)

  • Kwon, O-Kyang;Park, Jong-Koo
    • The KIPS Transactions:PartB
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    • v.15B no.3
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    • pp.253-258
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    • 2008
  • Recently, game players want new game requiring more various tactics and strategies in the complex environment beyond simple and repetitive play. Various artificial intelligence techniques have been suggested to make the game characters learn within this environment, and the recent researches include the neural network and the genetic algorithm. The Genetic programming(GP) has been used in this study for learning strategy of the agent in the pursuit-evasion problem which is used widely in the game theories. The suggested GP algorithm is faster than the existing algorithm such as neural network, it can be understood instinctively, and it has high adaptability since the evolving chromosomes can be transformed to the reasoning rules.