• Title/Summary/Keyword: Genetic Algorithm

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An Optimal Control of the Crane System Using a Genetic Algorithm (유전알고리즘을 이용한 크레인 시스템의 최적제어)

  • 최형식
    • Journal of Advanced Marine Engineering and Technology
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    • v.22 no.4
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    • pp.498-504
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    • 1998
  • This paper presents an optimal control algorithm for the overhead crane. To control the swing motion and the position tracking of the payload of the overhead crane a state feedback control algorithm is applied. by using a hybrid genetic algorithm the feedback gains of the state feedback is optimized to minimize the cost function composed of position errors and payload swing angle under unknown constant disturbances. Computer simulation is performed to demonstrate the effectiveness of the proposed control algorithm.

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Convergence Enhanced Successive Zooming Genetic Algorithm far Continuous Optimization Problems (연속 최적화 문제에 대한 수렴성이 개선된 순차적 주밍 유전자 알고리듬)

  • Gwon, Yeong-Du;Gwon, Sun-Beom;Gu, Nam-Seo;Jin, Seung-Bo
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.26 no.2
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    • pp.406-414
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    • 2002
  • A new approach, referred to as a successive zooming genetic algorithm (SZGA), is Proposed for identifying a global solution for continuous optimization problems. In order to improve the local fine-tuning capability of GA, we introduced a new method whereby the search space is zoomed around the design point with the best fitness per 100 generation. Furthermore, the reliability of the optimized solution is determined based on the theory of probability. To demonstrate the superiority of the proposed algorithm, a simple genetic algorithm, micro genetic algorithm, and the proposed algorithm were tested as regards for the minimization of a multiminima function as well as simple functions. The results confirmed that the proposed SZGA significantly improved the ability of the algorithm to identify a precise global minimum. As an example of structural optimization, the SZGA was applied to the optimal location of support points for weight minimization in the radial gate of a dam structure. The proposed algorithm identified a more exact optimum value than the standard genetic algorithms.

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.

A Hybrid Genetic Algorithm for Job Shop Scheduling (Job Shop 일정계획을 위한 혼합 유전 알고리즘)

  • 박병주;김현수
    • Journal of the Korean Operations Research and Management Science Society
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    • v.26 no.2
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    • pp.59-68
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    • 2001
  • The job shop scheduling problem is not only NP-hard, but is one of the well known hardest combinatorial optimization problems. The goal of this research is to develop an efficient scheduling method based on hybrid genetic algorithm to address job shop scheduling problem. In this scheduling method, generating method of initial population, new genetic operator, selection method are developed. The scheduling method based on genetic algorithm are tested on standard benchmark job shop scheduling problem. The results were compared with another genetic algorithm0-based scheduling method. Compared to traditional genetic, algorithm, the proposed approach yields significant improvement at a solution.

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An Optimization Technique For Crane Acceleration Using A Genetic Algorithm (유전자알고리즘을 이용한 크레인가속도 최적화)

  • 박창권;김재량;정원지;홍대선;권장렬;박범석
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2003.06a
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    • pp.1701-1704
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    • 2003
  • This paper presents a new optimization technique of acceleration curve for a wafer transfer crane movement in which high speed and low vibration are desirable. This technique is based on a genetic algorithm with a penalty function for acceleration optimization under the assumption that an initial profile of acceleration curves constitutes the first generation of the genetic algorithm. Especially the penalty function consists of the violation of constraints and the number of violated constraints. The proposed penalty function makes the convergence rate of optimization process using the genetic algorithm more faster than the case of genetic algorithm without a penalty function. The optimized acceleration of the crane through the genetic algorithm and commercial dynamic analysis software has shown to have accurate movement and low vibration.

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Real-time processing system for embedded hardware genetic algorithm (임베디드 하드웨어 유전자 알고리즘을 위한 실시간 처리 시스템)

  • Park Se-hyun;Seo Ki-sung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.8 no.7
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    • pp.1553-1557
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    • 2004
  • A real-time processing system for embedded hardware genetic algorithm is suggested. In order to operate basic module of genetic algorithm in parallel, such as selection, crossover, mutation and evaluation, dual processors based architecture is implemented. The system consists of two Xscale processors and two FPGA with evolvable hardware, which enables to process genetic algorithm efficiently by distributing the computational load of hardware genetic algorithm to each processors equally. The hardware genetic algorithm runs on Linux OS and the resulted chromosome is executed on evolvable hardware in FPGA. Furthermore, the suggested architecture can be extended easily for a couple of connected processors in serial, making it accelerate to compute a real-time hardware genetic algorithm. To investigate the effect of proposed approach, performance comparisons is experimented for an typical computation of genetic algorithm.

A Water-saving Irrigation Decision-making Model for Greenhouse Tomatoes based on Genetic Optimization T-S Fuzzy Neural Network

  • Chen, Zhili;Zhao, Chunjiang;Wu, Huarui;Miao, Yisheng
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.6
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    • pp.2925-2948
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    • 2019
  • In order to improve the utilization of irrigation water resources of greenhouse tomatoes, a water-saving irrigation decision-making model based on genetic optimization T-S fuzzy neural network is proposed in this paper. The main work are as follows: Firstly, the traditional genetic algorithm is optimized by introducing the constraint operator and update operator of the Krill herd (KH) algorithm. Secondly, the weights and thresholds of T-S fuzzy neural network are optimized by using the improved genetic algorithm. Finally, on the basis of the real data set, the genetic optimization T-S fuzzy neural network is used to simulate and predict the irrigation volume for greenhouse tomatoes. The performance of the genetic algorithm improved T-S fuzzy neural network (GA-TSFNN), the traditional T-S fuzzy neural network algorithm (TSFNN), BP neural network algorithm(BPNN) and the genetic algorithm improved BP neural network algorithm (GA-BPNN) is compared by simulation. The simulation experiment results show that compared with the TSFNN, BPNN and the GA-BPNN, the error of the GA-TSFNN between the predicted value and the actual value of the irrigation volume is smaller, and the proposed method has a better prediction effect. This paper provides new ideas for the water-saving irrigation decision in greenhouse tomatoes.

A Study on Adaptive Random Signal-Based Learning Employing Genetic Algorithms and Simulated Annealing (유전 알고리즘과 시뮬레이티드 어닐링이 적용된 적응 랜덤 신호 기반 학습에 관한 연구)

  • Han, Chang-Wook;Park, Jung-Il
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.10
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    • pp.819-826
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    • 2001
  • Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain because they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, hybridizing a genetic algorithm with other algorithms can produce better performance than using the genetic algorithm or other algorithms independently. In this paper, we propose an efficient hybrid optimization algorithm named the adaptive random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural networks. This paper describes the application of genetic algorithms and simulated annealing to a random signal-based learning in order to generate the parameters and reinforcement signal of the random signal-based learning, respectively. The validity of the proposed algorithm is confirmed by applying it to two different examples.

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A Hybrid Genetic Algorithm for Generating Cutting Paths of a Laser Torch (레이저 토치의 절단경로 생성을 위한 혼합형 유전알고리즘)

  • 이문규;권기범
    • Journal of Institute of Control, Robotics and Systems
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    • v.8 no.12
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    • pp.1048-1055
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    • 2002
  • The problem of generating torch paths for 2D laser cutting of a stock plate nested with a set of free-formed parts is investigated. The objective is to minimize the total length of the torch path starting from a blown depot, then visiting all the given Parts, and retuning back to the depot. A torch Path consists of the depot and Piercing Points each of which is to be specified for cutting a part. The torch path optimization problem is shown to be formulated as an extended version of the standard travelling salesman problem To solve the problem, a hybrid genetic algorithm is proposed. In order to improve the speed of evolution convergence, the algorithm employs a genetic algorithm for global search and a combination of an optimization technique and a genetic algorithm for local optimization. Traditional genetic operators developed for continuous optimization problems are used to effectively deal with the continuous nature of piercing point positions. Computational results are provided to illustrate the validity of the proposed algorithm.

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.