• Title, Summary, Keyword: Parallel Genetic Algorithm

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Hybrid Parallel Genetic Algorithm for Traveling Salesman Problem (순회 판매원 문제를 위한 하이브리드 병렬 유전자 알고리즘)

  • Kim, Ki-Tae;Jeo, Geon-Wook
    • Journal of the Korea Safety Management and Science
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    • v.13 no.3
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    • pp.107-114
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    • 2011
  • Traveling salesman problem is to minimize the total cost for a traveling salesman who wants to make a tour given finite number of cities along with the cost of travel between each pair them, visiting each cities exactly once before returning home. Traveling salesman problem is known to be NP-hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study suggests a hybrid parallel genetic algorithm(HPGA) for traveling salesman problem The suggested algorithm combines parallel genetic algorithm, nearest neighbor search, and 2-opt. The suggested algorithm has been tested on 7 problems in TSPLIB and compared the results of existing methods(heuristics, meta-heuristics, hybrid, and parallel). Experimental results shows that HPGA could obtain good solution in total travel distance minimization.

A Genetic Algorithm for Scheduling Sequence-Dependant Jobs on Parallel Identical Machines (병렬의 동일기계에서 처리되는 순서의존적인 작업들의 스케쥴링을 위한 유전알고리즘)

  • Lee, Moon-Kyu;Lee, Seung-Joo
    • Journal of Korean Institute of Industrial Engineers
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    • v.25 no.3
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    • pp.360-368
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    • 1999
  • We consider the problem of scheduling n jobs with sequence-dependent processing times on a set of parallel-identical machines. The processing time of each job consists of a pure processing time and a sequence-dependent setup time. The objective is to maximize the total remaining machine available time which can be used for other tasks. For the problem, a hybrid genetic algorithm is proposed. The algorithm combines a genetic algorithm for global search and a heuristic for local optimization to improve the speed of evolution convergence. The genetic operators are developed such that parallel machines can be handled in an efficient and effective way. For local optimization, the adjacent pairwise interchange method is used. The proposed hybrid genetic algorithm is compared with two heuristics, the nearest setup time method and the maximum penalty method. Computational results for a series of randomly generated problems demonstrate that the proposed algorithm outperforms the two heuristics.

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A Genetic Algorithm for Minimizing Completion Time with Non-identical Parallel Machines (이종 병렬설비 공정의 작업완료시간 최소화를 위한 유전 알고리즘)

  • Choi, Yu Jun;Song, Han Sik;Lee, Ik Sun
    • Korean Management Science Review
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    • v.30 no.3
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    • pp.81-97
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    • 2013
  • This paper considers a parallel-machine scheduling problem with dedicated and common processing machines. Non-identical setup and processing times are assumed for each machine. A genetic algorithm is proposed to minimize the makespan objective measure. In this paper, a lowerbound and some heuristic algorithms are derived and tested through computational experiments.

Forward kinematic analysis of a 6-DOF parallel manipulator using genetic algorithm (유전 알고리즘을 이용한 6자유도 병렬형 매니퓰레이터의 순기구학 해석)

  • 박민규;이민철;고석조
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1624-1627
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    • 1997
  • The 6-DOF parallel manipulator is a closed-kindmatic chain robot manipulator that is capable of providing high structural rigidity and positional accuracy. Because of its advantage, the parallel manipulator have been widely used in many engineering applications such as vehicle/flight driving simulators, rogot maniplators, attachment tool of machining centers, etc. However, the kinematic analysis for the implementation of a real-time controller has some problem because of the lack of an efficient lagorithm for solving its highly nonliner forward kinematic equation, which provides the translational and orientational attitudes of the moveable upper platform from the lenght of manipulator linkages. Generally, Newton-Raphson method has been widely sued to solve the forward kinematic problem but the effectiveness of this methodology depend on how to set initial values. This paper proposes a hybrid method using genetic algorithm(GA) and Newton-Raphson method to solve forward kinematics. That is, the initial values of forward kinematics solution are determined by adopting genetic algorithm which can search grobally optimal solutions. Since determining this values, the determined values are used in Newton-Raphson method for real time calcuation.

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A 2-Dimension Torus-based Genetic Algorithm for Multi-disk Data Allocation (2차원 토러스 기반 다중 디스크 데이터 배치 병렬 유전자 알고리즘)

  • 안대영;이상화;송해상
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.41 no.2
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    • pp.9-22
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    • 2004
  • This paper presents a parallel genetic algorithm for the Multi-disk data allocation problem an NP-complete problem. This problem is to find a method to distribute a Binary Cartesian Product File on disk-arrays to maximize parallel disk I/O accesses. A Sequential Genetic Algorithm(SGA), DAGA, has been proposed and showed the superiority to the other proposed methods, but it has been observed that DAGA consumes considerably lengthy simulation time. In this paper, a parallel version of DAGA(ParaDAGA) is proposed. The ParaDAGA is a 2-dimension torus-based Parallel Genetic Algorithm(PGA) and it is based on a distributed population structure. The ParaDAGA has been implemented on the parallel computer simulated on a single processor platform. Through the simulation, we study the impact of varying ParaDAGA parameters and compare the quality of solution derived by ParaDAGA and DAGA. Comparing the quality of solutions, ParaDAGA is superior to DAGA in all cases of configurations in less simulation time.

Parallel Genetic Algorithm using Fuzzy Logic (퍼지 논리를 이용한 병렬 유전 알고리즘)

  • An Young-Hwa;Kwon Key-Ho
    • The KIPS Transactions:PartA
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    • v.13A no.1
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    • pp.53-56
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    • 2006
  • Genetic algorithms(GA), which are based on the idea of natural selection and natural genetics, have proven successful in solving difficult problems that are not easily solved through conventional methods. The classical GA has the problem to spend much time when population is large. Parallel genetic algorithm(PGA) is an extension of the classical GA. The important aspect in PGA is migration and GA operation. This paper presents PGAs that use fuzzy logic. Experimental results show that the proposed methods exhibit good performance compared to the classical method.

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

  • Yi, Moo-Keun;Kim, Chun-Gon
    • Composites Research
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    • v.21 no.1
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    • pp.30-39
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    • 2008
  • In this paper, a parallel micro genetic algorithm was utilized in the optimal design of composite structures instead of a conventional genetic algorithm(SGA). Micro genetic algorithm searches the optimal design variables with only 5 individuals. The diversities from the nominal convergence and the re-initialization processes make micro genetic algorithm to find out the optimums with such a small population size. Two different composite structure optimization problems were proposed to confirm the efficiency of micro genetic algorithm compared with SGA. The results showed that micro genetic algorithm can get the solutions of the same level of SGA while reducing the calculation costs up to 70% of SGA. The composite laminated structure optimization under the load uncertainty was conducted using micro genetic algorithm. The result revealed that the design variables regarding the load uncertainty are less sensitive to load variation than that of fixed applied load. From the above-mentioned results, we confirmed micro genetic algorithm as a optimization method of composite structures is efficient.

An Optimization Technique for Diesel Engine Combustion Using a Micro Genetic Algorithm (유전알고리즘을 이용한 디젤엔진의 연소최적화 기법에 대한 연구)

  • 김동광;조남효;차순창;조순호
    • Transactions of the Korean Society of Automotive Engineers
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    • v.12 no.3
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    • pp.51-58
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    • 2004
  • Optimization of engine desist and operation parameters using a genetic algorithm was demonstrated for direct injection diesel engine combustion. A micro genetic algorithm and a modified KIVA-3V code were used for the analysis and optimization of the engine combustion. At each generation of the optimization step the micro genetic algorithm generated five groups of parameter sets, and the five cases of KIVA-3V analysis were to be performed either in series or in parallel. The micro genetic algorithm code was also parallelized by using MPI programming, and a multi-CPU parallel supercomputer was used to speed up the optimization process by four times. An example case for a fixed engine speed was performed with six parameters of intake swirl ratio, compression ratio, fuel injection included angle, injector hole number, SOI, and injection duration. A simultaneous optimization technique for the whole range of engine speeds would be suggested for further studies.

A Parallel Genetic Algorithms with Diversity Controlled Migration and its Applicability to Multimodal Function Optimization

  • YAMAMOTO, Fujio;ARAKI, Tomoyuki
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.629-633
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    • 1998
  • Proposed here is a parallel genetic algorithm accompanied with intermittent migration among subpopulations. It is intended to maintain diversity in the population for a long period . This method was applied to finding out the global maximum of some multimodal functions for which no other methods seem to be useful . Preferable results and their detailed analysis are also presented.

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Genetic Algorithm with an Effective Dispatching Method for Unrelated Parallel Machine Scheduling with Sequence Dependent and Machine Dependent Setup Times (작업순서와 기계 의존적인 작업준비시간을 고려한 이종병렬기계의 일정계획을 위한 효과적인 작업할당 방법을 이용한 유전알고리즘)

  • Joo, Cheol-Min;Kim, Byung-Soo
    • IE interfaces
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    • v.25 no.3
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    • pp.357-364
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    • 2012
  • This paper considers a unrelated parallel machine scheduling problem with ready times, due times and sequence and machine-dependent setup times. The objective of this problem is to determine the allocation of jobs and the scheduling of machines to minimize the total tardy time. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, a genetic algorithm using an effective dispatching method is proposed. The performance of the proposed genetic algorithm is evaluated using several randomly generated examples.