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IEEE Congress on Evolutionary Computation (CEC)

作者： Wang, Guan Wu, Degang Szeto, K. Y. Hong Kong Univ Sci & Technol Dept Phys Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781424478354

Systematic testing of parallel genetic algorithm defined on simple topologies such as ring, band, and fully connect graph are made with two benchmark problems, the 0-1 Knapsack Problem and the Weierstrass function. With fixed communication rule between sub-populations, our numerical results indicate that the band configuration is superior to other topologies tested. A heuristic understanding in terms of the compromise between exploitation and exploration in the solution space is suggested.

关键词： parallel genetic algorithms Topology Optimization Knapsack Problem Weierstrass Function

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Extending an Agent-Based FMS Scheduling Approach with parallel genetic algorithms

Extending an Agent-Based FMS Scheduling Approach with Parall...

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36th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)/4th IEEE International Conference on E-Learning in Industrial Electronics/IES Industry Forum

作者： Abaza, Ghada Badr, Iman Goehner, Peter Jeschke, Sabina Univ Stuttgart Inst Ind Automat & Software Engn D-7000 Stuttgart Germany Rhein Westfal TH Aachen Univ ZLW IMA Ctr Learning & Knowledge Management Inst Informat Management Mech Engn Aachen Germany

ISBN: (纸本)9781424452262

Flexible manufacturing systems (FMS) aim at efficiently reacting to changing market needs to stand the increasing competitiveness. This imposes efficiency and flexibility requirements on FMS scheduling. Manufacturing scheduling is the process of allocating available manufacturing resources to the set of planned jobs over time. It is an optimization process by which limited manufacturing resources are to be allocated to several jobs of different products efficiently. The agent-based scheduling approach has shown the ability to fulfill the flexibility requirement. Although this approach emphasizes flexibility, it lacks the optimization support. In this paper, an agent-based scheduling approach is extended with parallel genetic algorithms (PGA) to provide the required optimization support. Test results have shown a remarkable enhancement to the optimality of the generated schedules with respect to the predefined set of manufacturing objectives. The extended approach fulfils both flexibility and efficiency requirements on manufacturing scheduling.

关键词： agent based FMS scheduling approach flexible manufacturing system flexible manufacturing systems genetic algorithms manufacturing scheduling multi-agent systems optimization process parallel genetic algorithms scheduling

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On super-linearity in parallel genetic algorithms

On super-linearity in parallel genetic algorithms

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2nd IASTED International Conference on Neural Networks and Computational Intelligence

作者： Konfrst, Z Czech Tech Univ Dept Cybernet FEE CR-16635 Prague Czech Republic

ISBN: (纸本)0889863733

This paper examines how the false claims of super-linear speedup (super-linearity) have become common in the research works of parallel genetic algorithms (PGAs). A basic review of relevant theory of parallel computing is presented, along with some cases of super-linearity. The discussion section answers why super-linear speedup is met and the conclusion summarizes all worthwhile theories connected to this topic.

关键词： speedup super-linearity parallel genetic algorithms

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A parallel genetic algorithms optimization approach to calibrate microsimulation networks

A parallel genetic algorithms optimization approach to calib...

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4th IASTED International Conference on Modelling, Simulation and Optimization

作者： Rachdi, A Abdulhai, B Univ Toronto Intelligent Transportat Syst Ctr Toronto ON M5S 1A4 Canada

ISBN: (纸本)0889864241

This paper introduces GENOSIM-p: a Generic traffic microsimulation parameter optimization tool using parallel genetic algorithms (PGA), and its implementation to the St. Clair network in Downtown Toronto, Canada. GENOSIM-p is the parallel version of previous optimization software GENOSIM [1]. GENOSIM-p employs PGA to calibrate traffic microsimulation models. In this research, we will use PARAMICS: a microscopic traffic simulation platform. PARAMICS consists of high performance cross-linked traffic models having multiple user-adjustable parameters. GENOSIM-p will use PGA to manipulate those control parameters and search for an optimal set of values that minimize the discrepancy between simulation output and real field data.

关键词： parallel genetic algorithms calibration and traffic modeling

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Improving Performance of genetic algorithms for Transportation Systems: Case of parallel genetic algorithms

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JOURNAL OF INFRASTRUCTURE SYSTEMS 2016年第4期22卷 A4014002-A4014002页

作者： Abu-Lebdeh, Ghassan Chen, Hui Ghanim, Mohammad Amer Univ Sharjah Dept Civil Engn Sharjah Sharjah U Arab Emirates Parsons Brinkerhoff 500 Griswold St 2900 Detroit MI 48226 USA Amer Univ Dubai Dept Civil Engn Dubai U Arab Emirates

genetic algorithms (GAs) can be the tool of choice especially for optimizing combinatorial and complex problems in transport and infrastructure systems such as traffic signal control, pavement rehabilitation and design, and transit service scheduling. This paper presents an overview of different techniques to improve performance of GAs, with particular emphasis on parallel GAs (PGAs). Results are presented from applications of a simple GA (SGA) and a migration PGAs on a traffic control problem, a benchmark GA-difficult, and benchmark GA-easy problem. For all problems, savings in computation resources were realized when PGA was used. Advantages of PGAs are more pronounced for complex and difficult (deceptive) problems. On a difficult problem tested in this research, a PGA with four subpopulations was 7times more efficient than a serial one, and a PGA with eight subpopulations was more than 18times more efficient. With smaller and less complex problems, the impact of parallelism is less dramatic when the computation resources are limited. Use of parallel GAs does not reduce the importance of seeking efficient problem-specific operators and parameter values, but does magnify the effectiveness of such choices and increase the range of options available. The advantages PGAs offer mean more efficient and faster optimization for many applications in civil infrastructure design, operating management, and maintenance projects.

关键词： Transportation management Infrastructure Maintenance Case studies algorithms Optimization parallel genetic algorithms Optimization Operations research

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Parameter Identification of Hydraulic Low Pressure Pipeline Transient Model Using parallel genetic algorithms

Parameter Identification of Hydraulic Low Pressure Pipeline ...

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第五届流体传动与控制国际会议

作者： Yang Chifu~* Li Songjing Jiang Dan Department of Fluid Control and Automation,Harbin Institute Of Technology,Harbin,Heilongjiang,150001,China

Parameter identification of pressure transient models accompanying cavitation and gas bubbles inside low hydraulic pipelines using parallel genetic algorithms(PGA)in a cluster of computers is presented. In this paper,pressure transient models are given to describe the flow behavior in a pipeline,and the numerical models of cavitation and gas bubbles are built to calculate the cavitation volume and gas bubble volume during the *** unknown parameters of the pressure transient mathematical models include the initial gas bubble volume,gas resolving time and gas releasing time *** identification of pressure transient mathematical models with genetic algorithms(GA)on a single computer usually takes seven or eight days,which is out of *** execution time of GA becomes high due to time consuming of evaluating fitness *** order to predict the pressure transients and shorten the execution time of GA,MATLAB Distributed Computing toolbox and MATLAB genetic algorithms toolbox are used to perform evaluation of fitness function at each generation by PGA,which is a parallel calculation on a cluster of *** on the minimization of the least-square errors between experimental data and simulation results,PGA is applied to search for global optimal model *** rate of computation time using PGA to using GA is acquired. Simulation results with identified parameters obtained by PGA are *** of simulation results with experimental data indicates that PGA is feasible to estimate unknown parameters in hydraulic low pressure pipeline transient model.

关键词： parallel genetic algorithms parameter identification pressure transients

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Novel parallel hybrid genetic algorithms on the GPU for the generalized assignment problem

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JOURNAL OF SUPERCOMPUTING 2022年第1期78卷 144-167页

作者： Huang Zhi-Bin Fu Guang-Tao Dong Dan-Yang Xiao Chen Ding Zhe-Lun Dai Zhi-Tao Beijing Univ Posts & Telecommun BUPT Sch Comp Sci Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 CO Peoples R China

The emergence of GPU-CPU heterogeneous architecture has led to a significant paradigm shift in parallel programming. How to effectively implement parallel genetic Algorithm (GA) in these environments has become one of the current hot issues. GA's calculation and operators are closely related to specific problems, thereby significantly affecting the acceleration method of GA algorithms. The Generalized Assignment Problem (GAP) is a classic NP-hard combinatorial optimization problem. The more widely used genetic algorithms to solve the GAP in the CPU are difficult to be parallelized in a GPU environment due to severe data dependencies. To address this problem, two algorithms suitable for the implementation on the GPU are proposed, namely RPE algorithm and NNE algorithm, which obtain significant performance speedup by alleviating data dependencies and mutually exclusive synchronization overheads. At the same time, considering the new GPU architecture features and programming models, three different granular implementations of parallel genetic algorithms to solve the GAP are proposed, namely GPGA(thread), GPGA(warpsp) and GPGA(cgroup), by utilizing the warp-specialization technology and the cooperative group mechanism. GPGA series algorithms obtain better solution quality and very significant performance improvements compared with Serial GA, GTS (the GPU-CPU hybrid implementation of Scatter Search with Tabu lists) and Lagrange Relaxation algorithm on a CPU by solving 16 typical large-scale GAP instances.

关键词： parallel genetic algorithms GPU Warp specialization Cooperative group Generalized assignment problem

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parallel heterogeneous genetic algorithms for continuous optimization

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parallel COMPUTING 2004年第5-6期30卷 699-719页

作者： Alba, E Luna, F Nebro, AJ Troya, JM ETS Ingn Informat Dept Lenguajes & Ciencias Computac Malaga 29071 Spain

In this paper we address the physical parallelization of a very efficient genetic algorithm (GA) known as gradual distributed real-coded GA (GD-RCGA). This search model naturally provides a set of eight subpopulations residing in a cube topology having two faces for promoting exploration and exploitation. The resulting technique has been shown to yield very accurate results in continuous optimization by using crossover operators tuned to explore and exploit the solutions inside each subpopulation. Here, we encompass the actual parallelization of the technique, and get deeper into the importance of running a synchronous versus an asynchronous version of the basic GD-RCGA model. We also present the evaluation of the parallel execution of GD-RCGA over two local area networks, a Fast-Ethernet network and a Myrinet network. Our results indicate that the GD-RCGA model maintains a very high level of accuracy for continuous optimization when run in parallel, and we also demonstrate the relative advantages of each algorithm version over the two networks. Finally, we show that the async parallelization scales better than the sync one, what suggests future research lines for WAN execution and new models of search based on the original two-faced cube. (C) 2004 Elsevier B.V. All rights reserved.

关键词： parallel genetic algorithms heterogeneity continuous optimization

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parallel HYBRID genetic algorithms FOR SOLVING Q3AP ON COMPUTATIONAL GRID

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INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE 2012年第2期23卷 483-500页

作者： Loukil, Lakhdar Mehdi, Malika Melab, Nouredine Talbi, El-Ghazali Bouvry, Pascal Univ Oran Fac Sci Oran Algeria Univ Luxembourg Fac Sci Technol & Commun L-1359 Luxembourg Luxembourg INRIA Lille Nord Europe F-59650 Villeneuve Dascq France King Saud Univ Riyadh Saudi Arabia

This paper deals with the resolution of the Quadratic 3-dimensional Assignment Problem hereafter referred to as Q3AP. Q3AP is an extension of the well-known Quadratic Assignment Problem (QAP) and of the Axial 3-Assignment Problem (A3AP). It finds its application amongst others in Hybrid Automatic Repeat reQuest (HARQ) error-control mechanism used in wireless communication systems. This problem is computationally NP-hard. As far as we know, the largest Q3AP instance size solved to optimality is 13 whereas practical Q3AP instance size can be of 8, 16, 32 or 64. Sequential exact methods such branch-and-bound or sequential metaheuristics are therefore not suited to solve large size instances for the excessive needed computation time. In this paper, we propose parallel hybrid genetic-based metaheuristics for solving the Q3AP. The parallelism in our methods is of two hierarchical levels. The first level is an insular model where a fixed number of genetic algorithms (GA) evolve independently on separate islands and periodically exchange genetic material. The second level is a parallel transformation of individuals in each GA. Implementation has been done using ParadisEO(a) framework, and the experiments have been performed on GRID5000, the French nation-wide computational grid. The experimental results produced by our method were confronted with those reported in the literature. The optimum or the best so far known solutions have been reached in a reasonable computation time.

关键词： Quadratic 3-dimensional assignment problem metaheuristics insular model simulated annealing hybridization parallel genetic algorithms grid computing

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parallel Optimization of Program Instructions Using genetic algorithms

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Computers, Materials & Continua 2021年第6期67卷 3293-3310页

作者： Petre Anghelescu partment of Electronics Communications and ComputersUniversity of PitestiPitesti110040Romania

This paper describes an efficient solution to parallelize softwareprogram instructions, regardless of the programming language in which theyare written. We solve the problem of the optimal distribution of a set ofinstructions on available processors. We propose a genetic algorithm to parallelize computations, using evolution to search the solution space. The stagesof our proposed genetic algorithm are: The choice of the initial populationand its representation in chromosomes, the crossover, and the mutation operations customized to the problem being dealt with. In this paper, geneticalgorithms are applied to the entire search space of the parallelization ofthe program instructions problem. This problem is NP-complete, so thereare no polynomial algorithms that can scan the solution space and solve theproblem. The genetic algorithm-based method is general and it is simple andefficient to implement because it can be scaled to a larger or smaller number ofinstructions that must be parallelized. The parallelization technique proposedin this paper was developed in the C# programming language, and our resultsconfirm the effectiveness of our parallelization method. Experimental resultsobtained and presented for different working scenarios confirm the theoreticalresults, and they provide insight on how to improve the exploration of a searchspace that is too large to be searched exhaustively.

关键词： parallel instruction execution parallel algorithms genetic algorithms parallel genetic algorithms artificial intelligence techniques evolutionary strategies

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