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General methodology for using Epanet as an optimization element in evolutionary algorithms in a grid computing environment for water distribution network design

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WATER SCIENCE AND TECHNOLOGY-WATER SUPPLY 2017年第1期17卷 39-51页

作者： Yesenia Avila-Melgar, Erika Antonio Cruz-Chavez, Marco Martinez-Bahena, Beatriz Autonomous Univ State Morelos CIICAp Av Univ 1001 Cuernavaca 62209 Morelos Mexico

In this paper, an evolutionary algorithm, called EA-WDND, is developed to optimize water distribution network design for real instances. The evolutionary algorithm uses the Epanet Solver which, while not an optimizer, helps to evaluate the operational constraints of mass conservation, energy conservation, pressure in nodes (nodal heads) of the network, and velocities of water in network pipes. Epanet is used by the EA-WDND to evaluate whether the looped network is operating properly. Consequently, the EA-WDND obtains feasible configurations of network design. The best configuration, which has the lowest cost and best performance according to defined constraints, is obtained by the EA-WDND. This configuration can be practically implemented in real life. In this paper, a methodology for using Epanet Solver with a parallel evolutionary algorithm is presented.

关键词： Epanet Solver optimal design parallel evolutionary algorithm water distribution network design

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Hyper-Parameter Selection in Deep Neural Networks Using parallel Particle Swarm Optimization 17

Hyper-Parameter Selection in Deep Neural Networks Using Para...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Lorenzo, Pablo Ribalta Nalepa, Jakub Sanchez Ramos, Luciano Ranilla Pastor, Jose Future Proc Gliwice Poland Silesian Tech Univ Future Proc Gliwice Poland Univ Oviedo Dept Informat Oviedo Spain

ISBN: (纸本)9781450349390

The need of manual hyper-parameter selection can seriously hamper the model optimization of Deep Neural Networks (DNNs). Conventional automated approaches tackling this problem suffer from poor scalability or fail in certain scenarios. In this paper, we introduce a parallel method that applies Particle Swarm Optimization (PSO) for the hyper-parameter selection in DNNs. To estimate the best hyper-parameters, a population of particles is evolved, with their fitness calculated in parallel. The experimental results demonstrate very desirable scalability properties for different DNNs. We show that the parallel PSO can further optimize existent models designed by experts in an affordable amount of time.

关键词： Deep neural networks hyper-parameter selection particle swarm optimization parallel evolutionary algorithm

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evolutionary-Statistical System: A parallel method for improving forest fire spread prediction

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JOURNAL OF COMPUTATIONAL SCIENCE 2015年第1期6卷 58-66页

作者： Bianchini, G. Caymes-Scutari, P. Mendez-Garabetti, M. Univ Tecnol Nacl Fac Reg Mendoza Dept Ingn Sistemas Informac LICPaD Mendoza Argentina Consejo Nacl Invest Cient & Tecn RA-1033 Buenos Aires DF Argentina

Fighting fires is a very risky job, where loss of life is a real possibility. Proper training is essential. Several firemen academies offer courses and programs whose goal is to enhance the ability of fire and emergency services to deal more effectively with fire. Among the tools that can be found in the training process are fire simulators, which are used both for training and for the prediction of forest fires. In many cases, the used simulators are based on models that present a series of limitations related to the need for a large number of input parameters. Moreover, such parameters often have some degree of uncertainty due to the impossibility of measuring all of them in real time. Therefore, they have to be estimated from indirect measurements, which negatively impacts on the output of the model. In this paper we present a method which combines Statistical Analysis with parallel evolutionary algorithms to improve the quality of the model output. (C) 2014 Elsevier B.V. All rights reserved.

关键词： parallel evolutionary algorithm Statistical System Forest fire prediction High performance computing parallel processing

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Matching island topologies to problem structure in parallel evolutionary algorithms

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SOFT COMPUTING 2013年第7期17卷 1209-1225页

作者： Arnaldo, Ignacio Contreras, Ivan Millan-Ruiz, David Ignacio Hidalgo, J. Krasnogor, Natalio Univ Complutense Madrid Parallel Architectures & Bioinspired Algorithms P Madrid Spain Univ Nottingham Sch Comp Sci Interdisciplinary Comp & Complex Syst ICOS Res Gr Nottingham NG7 2RD England

In the context of parallel evolutionary algorithms, it has been shown that different population structures induce different search performances. Nevertheless, no work has shown a clear cut evidence that there is a correlation between the solver's population structure and the problem's network structure. In this work, we verify this correlation performing a clear and systematic analysis of a large set of population structures (based on the well known beta-graphs and NK-landscape problems. Furthermore, we go beyond our findings in these idealised experiments by analysing the performance of variable-topology EAs on a dynamic real-world problem, the Multi-Skills Call Centre.

关键词： parallel evolutionary algorithm Island model Problem structure

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Large-scale M1 microcircuit model with plastic input connections from biological PMd neurons used for prosthetic arm control

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BMC Neuroscience 2015年第1期16卷 1-2页

作者： Salvador Dura-Bernal Samuel A Neymotin Joseph T Francis William W Lytton Cliff C Kerr Bejamin A Suter Gordon MG Shepherd Department of Physiology and Pharmacology SUNY Downstate Brooklyn NY 11203 USA School of Physics University of Sydney Sydney NSW Australia Department Physiology Northwestern University Chicago IL 60611 USA

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General Methodology for Converting a Sequential evolutionary algorithm into parallel algorithm with MPI to Water Design Networks

General Methodology for Converting a Sequential Evolutionary...

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7th IEEE Electronics, Robotics and Automotive Mechanics Conference (CERMA)

作者： Antonio Cruz-Chavez, Marco Avila-Melgar, E. Y. Serna Barquera, Sergio A. Juarez-Perez, Fredy Univ Autonoma Estado Morelos CIICAp Av Univ 1001 Cuernavaca 62209 Morelos Mexico

ISBN: (纸本)9780769542041

This paper presents a general methodology for the conversion of the sequential evolutionary algorithms into parallel evolutionary algorithms using MPI. The methodology is showed by applying it to a combinatorial optimization problem well-known as Water Distribution Networks. Also a comparison of two parallelization approaches is done, showing the advantages and disadvantages of each approach. The implementation of this parallel algorithm reduces the necessary time to obtain good solutions for NP-Complete problems as Water Distribution Networks.

关键词： MPI combinatorial mathematics combinatorial optimization evolutionary computation parallel algorithms parallel evolutionary algorithm sequential evolutionary algorithm water design network water distribution network

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parallel DIFFERENTIAL EVOLUTION WITH ENDEMIC RANDOMIZED CONTROL PARAMETERS

PARALLEL DIFFERENTIAL EVOLUTION WITH ENDEMIC RANDOMIZED CONT...

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4th International Conference on Bioinspired Optimization Methods and their Applications (BIOMA)

作者： Weber, Matthieu Neri, Ferrante Tirronen, Ville Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla Finland

ISBN: (纸本)9789612640170

This paper proposes the use of endemic control parameters within a parallel Differential Evolution algorithm. The Differential Evolution running at each subpopulation is associated with randomly initialized scale factor and crossover rate, which are then repeatedly updated during the optimization process. Numerical results show that the Endemic Randomized Control Parameter parallel Differential Evolution seems to be a simple, robust, and efficient algorithm suited for various applications. An important finding of this study is that randomized initial values of both control parameters and repeated updates of the scale factor are beneficial to the optimization process, whereas repeated updates of the crossover rate are detrimental.

关键词： Differential Evolution parallel evolutionary algorithm Self adaptation

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parallel mesh-partitioning algorithms for generating shape optimised partitions using evolutionary computing

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ADVANCES IN ENGINEERING SOFTWARE 2009年第2期40卷 141-157页

作者： Rao, A. Rama Mohan Struct Engn Res Ctr Madras 600113 Tamil Nadu India

In this paper, parallel mesh-partitioning algorithms are proposed for generating submeshes with optimal shape using evolutionary computing techniques. It is preferred to employ a formulation for mesh partitioning, which maintains constant number of design variables irrespective of the size of the mesh. Two distinct parallel computing models have been employed. The first model of parallel evolutionary algorithm uses the master-slave concept (single population model) and a new synchronous model is proposed to optimise the performance even on heterogeneous parallel hardware. Alternatively, a multiple population model is also developed which simulates it's sequential counter part. The advantage of the second model is that it can fit in large size problems with large population even on moderate capacity parallel computing nodes. The performance of the evolutionary computing based mesh-partitioning algorithm is demonstrated first by solving several practical engineering problems and also several benchmark test problems available in the literature and comparing the results with the multilevel algorithms. Later the speedup of the parallel evolutionary algorithms on parallel hardware is evaluated by solving large scale practical engineering problems. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Mesh partitioning Aspect ratio parallel FEM parallel evolutionary algorithm Master-slave model Multiple population model

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evolutionary computing on consumer graphics hardware

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IEEE INTELLIGENT SYSTEMS 2007年第2期22卷 69-78页

作者： Fok, Ka-Ling Wong, Tien-Tsin Wong, Man-Leung Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China Lingnan Univ Dept Comp & Decis Sci Tuen Mun Hong Kong Peoples R China

We propose implementing a parallel EA on consumer graphics cards, which we can find in many PCs. This lets more people use our parallel algorithm to solve large-scale, real-world problems such as data mining. parallel evolutionary algorithms run on consumer-grade graphics hardware achieve better execution times than ordinary evolutionary algorithms and offer greater accessibility than those run on high-performance computers

关键词： Computer graphics Concurrent computing Engines evolutionary computation Genetic programming Hardware parallel algorithms Pipelines Rendering (computer graphics) Toy industry computer graphic equipment computer graphics consumer graphics card consumer-grade graphics hardware evolutionary algorithms evolutionary computation evolutionary computing high-performance computer parallel algorithm parallel algorithms parallel evolutionary algorithm pervasive computing scientific computing on graphics-processing units ubiquitous computing

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An Analysis of the Effects of Population Structure on Scalable Multiobjective Optimization Problems 07

An Analysis of the Effects of Population Structure on Scalab...

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Annual Conference of Genetic and evolutionary Computation Conference

作者： Kirley, Michael Stewart, Robert Univ Melbourne Dept Comp Sci & Software Engn Melbourne Vic 3010 Australia

ISBN: (纸本)9781595936974

Multiobjective evolutionary algorithms (MOEA) are an effective tool for solving search and optimization problems containing several incommensurable and possibly conflicting objectives. Unfortunately, many MOEAs face difficulties in solving problems when the number of objectives increases. lit this paper, we investigate the efficacy of spatially structured MOEAs for scalable multiobjective problems. The algorithm is an extension of the standard cellular evolutionary algorithm, where the population is mapped to nodes of alternative complex networks. A selection regime based on a non-dominance rating and a crowding mechanism guides the evolutionary trajectory and an epsilon-dominance external archive is used to maintain a spread of solutions across the Pareto-optimal front. An important outcome of this work is the classification of the network models based on their impact on convergence speed and solution quality as the number of objectives increases for a given problem.

关键词： Complex network parallel evolutionary algorithm Multiobjective optimization

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