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A parallel genetic algorithm for generation expansion planning

IEEE TRANSACTIONS ON POWER SYSTEMS 1996年第2期11卷 955-961页

作者： Fukuyama, Y Chiang, HD CORNELL UNIV SCH ELECT ENGNITHACANY 14853

This paper presents an application of parallel genetic algorithm to optimal long-range generation expansion planning. The problem is formulated as a combinatorial optimization problem that determines the number of newly introduced generation units of each technology during different time intervals. A new string representation method for the problem is presented. Binary and decimal coding for the string representation method are compared. The method is implemented on transputers, one of the practical multi-processors. The effectiveness of the proposed method is demonstrated on a typical generation expansion problem with four technologies, five intervals, and a various number of generation units. It is compared favorably with dynamic programming and conventional genetic algorithm. The results reveal the speed and effectiveness of the proposed method for solving this problem.

关键词： generation expansion planning combinatorial optimization parallel genetic algorithm parallel computation multi-processors

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THE parallel genetic algorithm AS FUNCTION OPTIMIZER

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parallel COMPUTING 1991年第6-7期17卷 619-632页

作者： MUHLENBEIN, H SCHOMISCH, M BORN, J INST INFORMAT & RECHENTECH W-1199 BERLIN ADLERSHOF GERMANY

In this paper, the parallel genetic algorithm PGA is applied to the optimization of continuous functions. The PGA uses a mixed strategy. Subpopulations try to locate good local minima. If a subpopulation does not progress after a number of generations, hillclimbing is done. Good local minima of a subpopulation are diffused to neighboring subpopulations. Many simulation results are given with popular test functions. The PGA is at least as good as other genetic algorithms on simple problems. A comparison with mathematical optimization methods is done for very large problems. Here a breakthrough can be reported. The PGA is able to find the global minimum of Rastrigin's function of dimension 400 on a 64 processor system! Furthermore, we give an example of a superlinear speedup.

关键词： SEARCH METHODS OPTIMIZATION METHODS parallel genetic algorithm PERFORMANCE EVALUATION SPEEDUP RESULTS

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parallel genetic algorithm based automatic path planning for crane lifting in complex environments

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AUTOMATION IN CONSTRUCTION 2016年 62卷 133-147页

作者： Cai, Panpan Cai, Yiyu Chandrasekaran, Indhumathi Zheng, Jianmin Nanyang Technol Univ Sch Mech & Aerosp Engn 50 Nanyang Ave Singapore 639798 Singapore Nanyang Technol Univ Sch Comp Engn Nanyang Ave Singapore 639798 Singapore Nanyang Technol Univ Inst Media Innovat 50 Nanyang DrRes Techno Plaza Singapore 637553 Singapore

Heavy lifting is a common and important task in industrial plants. It is conducted frequently during the time of plant construction, maintenance shutdown and new equipment installation. To find a safe and cost effective way of lifting, a team works for weeks or even months doing site investigation, planning and evaluations. This paper considers the lifting path planning problem for terrain cranes in complex environments. The lifting path planning problem takes inputs such as the plant environment, crane mechanical data, crane position, start and end lifting configurations to generate the optimal lifting path by evaluating costs and safety risks, We formulate the crane lifting path planning as a multi-objective nonlinear integer optimization problem with implicit constraints. It aims to optimize the energy cost, time cost and human operation conformity of the lifting path under constraints of collision avoidance and operational limitations. To solve the optimization problem, we design a Master-Slave parallel genetic algorithm and implement the algorithm on Graphics Processing Units using CUDA programming. In order to handle complex plants, we propose a collision detection strategy using hybrid configuration spaces based on an image-based collision detection algorithm. The results show that the method can efficiently generate high quality lifting paths in complex environments. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Crane lifting path planning parallel genetic algorithm Image-space collision detection GPU computing Hybrid C-space method

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parallel genetic algorithm for service restoration in electric power distribution systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 1996年第2期18卷 111-119页

作者： Fukuyama, Y Chiang, HD Miu, KN su.edu Fuji Electric Corporate R & D Ltd No 1 Fuji-machi Hino-city Tokyo 191 Japan

This paper develops a coarse-grain parallel genetic algorithm for solving a service restoration problem in electric power distribution systems. Service restoration is performed to restore electricity for out-of-service areas. Developing effective service restoration procedures is a cost-effective approach to improving service reliability and enhancing customer satisfaction. The main objective in service restoration procedures is to restore as much load as possible by transferring de-energized loads via network reconfigurations to other supporting distribution feeders without violating operating and engineering constraints. Details of the parallel genetic algorithm developed in this paper are described The proposed method is implemented on transputers for parallel computations. The feasibility of the developed algorithm for service restoration is demonstrated on several distribution networks with promising results.

关键词： service restoration combinatorial optimization parallel genetic algorithm parallel computation multi-processors

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parallel genetic algorithm with parameter adaptation

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INFORMATION PROCESSING LETTERS 2002年第1期82卷 47-54页

作者： Tongchim, S Chongstitvatana, P Chulalongkorn Univ Dept Comp Engn Bangkok 10330 Thailand

This paper presents an adaptive algorithm that can adjust parameters of a genetic algorithm according to the observed performance. The parameter adaptation occurs in parallel to the running of the genetic algorithm. The proposed method is compared with the algorithms that use random parameter sets and a standard parameter set. The experimental results show that the proposed method offers two advantages over the other competing methods: the reliability in finding the optimal solution and the time required for finding the optimal solution. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： parallel genetic algorithm parameter adaptation

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parallel genetic algorithm with a knowledge base for a redundancy allocation problem considering the sequence of heterogeneous components

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 113卷 328-338页

作者： Kim, Heungseob Republ Korea Air Force Acad Dept Syst Engn Cheongju 635 South Korea

This article presents a new version of the redundancy allocation problem with mixed components (RAPMC) considering the component sequence because it severely affects the reliability of a standby redundant system. It provides a system configuration with exceedingly higher reliability than existing RAPs under same constraints. However, its solution space is significantly expanded according to the number of candidate types and the scale of the system, and thus this study proposed a parallel genetic algorithm with a knowledge base (PGAKB) to efficiently solve it. It includes two strategies, which are the emulation of an expert system and the cooperation between GAs. An individual of the PGAKB creates and exploits the knowledge of the society, and the accumulated knowledge is used for the local search, the final stage for the PGAKB. In conclusion, for solving a complex optimization problem, the PGAKB operates in the form of an expert system and describes a society developing itself by accumulating knowledge. Furthermore, regarding the quality and robustness of solutions and computational time, the effectiveness of the PGAKB was analytically demonstrated by experiments on a famous example. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Redundancy allocation problem parallel genetic algorithm Knowledge base Component sequencing Heterogeneous components

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A parallel genetic algorithm approach to solving the unit commitment problem: Implementation on the transputer networks

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IEEE TRANSACTIONS ON POWER SYSTEMS 1997年第2期12卷 661-668页

作者： Yang, HT Yang, PC Huang, CL NATL CHENG KUNG UNIV DEPT ELECT ENGNTAINAN 701TAIWAN

Through a constraint handling technique, this paper proposes a parallel genetic algorithm (GA) approach to solving the thermal unit commitment (UC) problem. The developed algorithm is implemented on an eight-processor transputer network, processors of which are arranged in master-slave and dual-direction ring structures, respectively. The proposed approach has been tested on a 38-unit thermal power system over a 24-hour period. Speed-up and efficiency for each topology with different number of processor are compared to those of the sequential GA approach. The proposed topology of dual-direction ring is shown to be well amenable to parallel implementation of the GA for the UC problem.

关键词： unit commitment combinatorial optimization parallel genetic algorithm transputer network

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parallel genetic algorithm for the Uncapacited Single Allocation Hub Location Problem on GPU 13

Parallel genetic algorithm for the Uncapacited Single Alloca...

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13th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA)

作者： Benaini, Abdelhamid Berrajaa, Achraf Boukachour, Jaouad Oudani, Mustapha Normandie Univ UNIHAVRE LMAH Le Havre France Univ Mohammed 1 LaRT Oujda Morocco Univ Mohammed ben Abdellah MSC Lab Fes Morocco

ISBN: (纸本)9781509043200

A parallel genetic algorithm (GA) implemented on GPU clusters is proposed to solve the Uncapacitated Single Allocation Hub Location problem. The GA uses binary and integer encoding with genetic operators adapted to this problem. Our GA is improved by initially locating hubs at middle nodes. In our implementation we use the power of the GPU to compute in parallel several initial solutions, varying the number of hubs. The obtained experimental results compared with the best known solutions on all benchmarks. They show that our approach outperforms most well-known heuristics in terms of solution quality and time execution. Also it allowed to solve instances problem unsolved before.

关键词： parallel genetic algorithm GPU implementation single allocation hub location p-hub location problem

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parallel genetic algorithm for optimizing run-time reconfigurable circuits

Parallel genetic algorithm for optimizing run-time reconfigu...

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10th Mediterranean Electrotechnical Conference on Information Technology and Electrotechnology for the Mediterranean Countries

作者： Fröhlich, H Kosir, A Zajc, B Univ Ljubljana Fac Elect Engn Ljubljana 1000 Slovenia

ISBN: (纸本)078036290X

In this paper a methodology for finding the maximal common subgraph of two directed graphs with parallel genetic algorithm is discussed. The method is directly applicable to the optimization of configurations of FPGA (Field Programmable Gate Array) circuits in Run-Time Reconfigurable systems. The problem of finding the maximal common subgraph is known to be NP-complete. The advantage of our approach is that we find optimal or near-optimal solutions in polynomial time using a genetic algorithm. Since the cost function of the optimization task is multimodal, an implementation of parallel genetic algorithm assures significant improvments of the results.

关键词： reconfigurable circuits parallel genetic algorithm

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parallel genetic algorithm model based on AHP and neural networks for enterprise comprehensive business

Parallel genetic algorithm model based on AHP and neural net...

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4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing

作者： Qu, Xilong Feng, Jian Sun, Wenfang Coll Comp & Commun Hunan Inst Engn Xiangtan 411101 Peoples R China Southwest Jiaotong univ Coll Logist Chengdu 610031 Peoples R China

ISBN: (纸本)9780769532783

The comprehensive business includes many links, and the radical of agile networked manufacturing is reducing cost and improving agility for manufacturing enterprises. So, in order to form an agile networked manufacturing system, it needs selecting appropriate partners in every link. But it is difficult to select from so large number of partners. genetic algorithm is a method for resolving combinatorial optimization problem, however, simple genetic algorithm still exist defect on computing speed, combining parallel method and neural networks, the computer speed of parallel genetic algorithm can become faster. Moreover, to determine the important of each selection attributes for agile networked manufacturing system, we adopt AHP. With parallel genetic algorithm Model Based on AHP, the agile networked manufacturing alliance can establish swiftly and accurately.

关键词： parallel genetic algorithm neural networks enterprise comprehensive business

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