• Title, Summary, Keyword: Genetic algorithm (GA)

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Fuzzy genetic algorithm for optimal control (최적 제어에 대한 퍼지 유전 알고리즘의 적용 연구)

  • 박정식;이태용
    • 제어로봇시스템학회:학술대회논문집
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    • pp.297-300
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    • 1997
  • This paper uses genetic algorithm (GA) for optimal control. GA can find optimal control profile, but the profile may be oscillating feature. To make profile smooth, fuzzy genetic algorithm (FGA) is proposed. GA with fuzzy logic techniques for optimal control can make optimal control profile smooth. We describe the Fuzzy Genetic Algorithm that uses a fuzzy knowledge based system to control GA search. Result from the simulation example shows that GA can find optimal control profile and FGA makes a performance improvement over a simple GA.

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Direction Vector for Efficient Structural Optimization with Genetic Algorithm (효율적 구조최적화를 위한 유전자 알고리즘의 방향벡터)

  • Lee, Hong-Woo
    • Journal of the Korean Association for Spatial Structures
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    • v.8 no.3
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    • pp.75-82
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    • 2008
  • In this study, the modified genetic algorithm, D-GA, is proposed. D-GA is a hybrid genetic algorithm combined a simple genetic algorithm and the local search algorithm using direction vectors. Also, two types of direction vectors, learning direction vector and random direction vector, are defined without the sensitivity analysis. The accuracy of D-GA is compared with that of simple genetic algorithm. It is demonstrated that the proposed approach can be an effective optimization technique through a minimum weight structural optimization of ten bar truss.

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Embedded One Chip Computer Design for Hardware Implementation of Genetic Algorithm (유전자 알고리즘 하드웨어 구현을 위한 전용 원칩 컴퓨터의 설계)

  • 박세현;이언학
    • Journal of Korea Multimedia Society
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    • v.4 no.1
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    • pp.82-90
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    • 2001
  • Genetic Algorithm(GA) has known as a method of solving NP problem in various applications. Since a major drawback of the GA is that it needs a long computation time, the hardware implementation of Genetic Algorithm is focused on in recent studies. This paper proposes a new type of embedded one chip computer fort Hardware Implementation of Genetic Algorithm. The proposed embedded one chip computer consists of 16 Bit CPU care and hardware of genetic algorithm. In contrast to conventional hardware oriented GA which is dependent on main computer in the process of GA, the proposed embedded one chip computer is independent on main computer. Conventional hardware GA uses the fixed length of chromosome but the proposed embedded one chip computer uses the variable length of chromosome by employing the efficient 16 bit Pipeline Unit. Experimental results show that the proposed one chip computer is applicable to the design of evolvable hardware for Random NRZ bit synchronization circuit.

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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.

Comparative Study on Structural Optimal Design Using Micro-Genetic Algorithm (마이크로 유전자 알고리즘을 적용한 구조 최적설계에 관한 비교 연구)

  • 한석영;최성만
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.12 no.3
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    • pp.82-88
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    • 2003
  • SGA(Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, ${\mu}GA$(Micro-Genetic Algorithm) has recently been developed. In this study, ${\mu}GA$ which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of ${\mu}GA$ were compared with those of SGA. Solutions of ${\mu}GA$ for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that ${\mu}GA$ is a suitable and very efficient optimization algorithm for structural design.

Structural Optimization Using Micro-Genetic Algorithm (마이크로 유전자 알고리즘을 이용한 구조 최적설계)

  • 한석영;최성만
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • pp.9-14
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    • 2003
  • SGA (Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, $\mu$GA(Micro-Genetic Algorithm) has recently been developed. In this study, $\mu$GA which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of $\mu$GA were compared with those of SGA. Solutions of $\mu$GA for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that $\mu$GA is a suitable and very efficient optimization algorithm for structural design.

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An efficient method for multiprocessor scheduling problem using genetic algorithm (Genetic algorithm을 이용한 다중 프로세서 일정계획문제의 효율적 해법)

  • 오용주;박승헌
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • pp.220-229
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    • 1995
  • Generally the Multiprocessor Scheduling(MPS) problem is difficult to solve because of the precedence of the tasks, and it takes a lot of time to obtain its optimal solution. Though Genetic Algorithm(GA) does not guarantee the optimal solution, it is practical and effective to solve the MPS problem in a reasonable time. The algorithm developed in this research consists of a improved GA and CP/MISF(Critical Path/Most Immediate Successors First). A new genetic operator is derived to make GA more efficient. It runs parallel CP/MISF with Ga to complement the faults of GA. The solution by the developed algorithm is compared with that of CP/MISF, and the better is taken as a final solution. As a result of comparative analysis by using numerical examples, although this algorithm does not guarantee the optimal solution, it can obtain an approximate solution that is much closer to the optimal solution than the existing GA's.

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An Efficient Method for Multiprocessor Scheduling Problem Using Genetic Algorithm (Genetic Algorithm을 이용한 다중 프로세서 일정계획문제의 효울적 해법)

  • 박승헌;오용주
    • Journal of the Korean Operations Research and Management Science Society
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    • v.21 no.1
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    • pp.147-161
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    • 1996
  • Generally the Multiprocessor Scheduling (MPS) problem is difficult to solve because of the precedence of the tasks, and it takes a lot of time to obtain its optimal solution. Though Genetic Algorithm (GA) does not guarantee the optimal solution, it is practical and effective to solve the MPS problem in a reasonable time. The algorithm developed in this research consists of a improved GA and GP/MISF (Critical Path/Most Immediate Successors First). An efficient genetic operator is derived to make GA more efficient. It runs parallel CP/MISF with GA to complement the faults of GA. The solution by the developed algorithm is compared with that of CP/MISF, and the better is taken as a final solution. As a result of comparative analysis by using numerical examples, although this algorithm does not guarantee the optimal solution, it can obtain an approximate solution that is much closer to the optimal solution than the existing GA's.

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A 3-D Genetic Algorithm for Finding the Number of Vehicles in VRPTW

  • Paik, Si-Hyun;Ko, Young-Min;Kim, Nae-Heon
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.22 no.53
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    • pp.37-44
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    • 1999
  • The problem to be studied here is the minimization of the total travel distance and the number of vehicles used for delivering goods to customers. Vehicle routes must also satisfy a variety of constraints such as fixed vehicle capacity, allowed operating time. Genetic algorithm to solve the VRPTW with heterogeneous fleet is presented. The chromosome of the proposed GA in this study has the 3-dimension. We propose GA that has the cubic-chromosome for VRPTW with heterogeneous fleet. The newly suggested ‘Cubic-GA (or 3-D GA)’ in this paper means the 2-D GA with GLS(Genetic Local Search) algorithms and is quite flexible. To evaluate the performance of the algorithm, we apply it to the Solomon's VRPTW instances. It produces a set of good routes and the reasonable number of vehicles.

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Reusable Network Model using a Modified Hybrid Genetic Algorithm in an Optimal Inventory Management Environment (최적 재고관리환경에서 개량형 하이브리드 유전알고리즘을 이용한 재사용 네트워크 모델)

  • Lee, JeongEun
    • Journal of the Korea Industrial Information Systems Research
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    • v.24 no.5
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    • pp.53-64
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    • 2019
  • The term 're-use' here signifies the re-use of end-of-life products without changing their form after they have been thoroughly inspected and cleaned. In the re-use network model, the distributor determines the product order quantity on the network through which new products are received from the suppliers or products are supplied to the customers through re-use of the recovered products. In this paper, we propose a reusable network model for reusable products that considers the total logistics cost from the forward logistics to the reverse logistics. We also propose a reusable network model that considers the processing and disposal costs for reuse in an optimal inventory management environment. The authors employe Genetic Algorithm (GA), which is one of the optimization techniques, to verify the validity of the proposed model. And in order to investigate the effect of the parameters on the solution, the priority-based GA (priGA) under three different parameters and the modified Hybrid GA (mhGA), in which parameters are adjusted for each generation, were applied to four examples with varying sizes in the simulation.