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Distributed evolutionary algorithms and their models: A survey of the state-of-the-art
2015
Applied Soft Computing
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Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and ...
The increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. ...
In a pool-based distributed model for EAs, how to implement the resource pool is a crucial issue to address. ...
doi:10.1016/j.asoc.2015.04.061
fatcat:5fhlxe7evfdbljyffzqyjpcpuy
A Survey on Metaheuristics for Solving Large Scale Optimization Problems
2017
International Journal of Computer Applications
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In the research community, they are generally labeled as Large Scale Global Optimization (LSGO) problems. Several Metaheuristics has been proposed to tackle these problems. ...
As a result, numerous realworld optimization problems in science and engineering, possessing very high dimensions, have appeared. ...
CEC organizes a special competition on Large Scale Global Optimization, where authors compete with their algorithms specially designed for tackling Large scale optimization problems. ...
doi:10.5120/ijca2017914839
fatcat:2lhciqf4lbgetpyeouf5xykps4
A large-scale flight multi-objective assignment approach based on multi-island parallel evolution algorithm with cooperative coevolutionary
2016
Science China Information Sciences
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Hence, an effective multi-objective optimization algorithm is proposed based on the multi-island parallel evolution framework (PEA) with a left-right probability migration topology. ...
on decomposition, a CC-based multi-objective algorithm as well as other two parallel evolution algorithms with different migration topologies. ...
problem which is a large-scale combinatorial optimization problem. ...
doi:10.1007/s11432-015-5495-3
fatcat:hxwtbghpbncz7ngmzpct472ple
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
2018
SC18: International Conference for High Performance Computing, Networking, Storage and Analysis
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To our knowledge, this is the first large-scale science application of the TensorFlow framework at supercomputer scale with fully-synchronous training. ...
We also utilize the Cray PE Machine Learning Plugin for efficient scaling to multiple nodes. ...
DISCLAIMERS Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. ...
doi:10.1109/sc.2018.00068
fatcat:sd35wm56z5eylot7oxtceck3li
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
[article]
2018
arXiv
pre-print
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To our knowledge, this is the first large-scale science application of the TensorFlow framework at supercomputer scale with fully-synchronous training. ...
We also utilize the Cray PE Machine Learning Plugin for efficient scaling to multiple nodes. ...
DISCLAIMERS Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. ...
arXiv:1808.04728v2
fatcat:u4ud4jm4ffegzbi7b755iw25su
Research challenges, opportunities and synergism in systems engineering and computational biology
2005
AIChE Journal
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Global optimization addresses the computation and characterization of global minima and maxima of a nonconvex objective function subject to a nonconvex set of equality and inequality constraints. ...
There are five primary objectives in deterministic global optimization: (1) determine an epsilonglobal minimum with theoretical guarantee; (2) calculate valid and tight lower and upper bounds on the global ...
to large-scale twice continuously differentiable nonlinear optimization models, such as pooling problems; (d) developing methods for medium and large-scale mixed-integer nonlinear optimization models ...
doi:10.1002/aic.10620
fatcat:4hgnhia5lnd5zpraheedpgtbpm
Human Action Recognition with Multi-Laplacian Graph Convolutional Networks
[article]
2019
arXiv
pre-print
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We also introduce a novel pooling operator, on graphs, that proceeds in two steps: context-dependent node expansion is achieved, followed by a global average pooling; the strength of this two-step process ...
The main contribution of this method resides in a new design principle that learns graph-laplacians as convex combinations of other elementary laplacians each one dedicated to a particular topology of ...
Finally, a global average pooling 5 . ...
arXiv:1910.06934v1
fatcat:kfm5bh6msncyjk7rc2p3mpgbzm
Metabolic Engineering from a Cybernetic Perspective. 1. Theoretical Preliminaries
1999
Biotechnology progress (Print)
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It is postulated that the objective of the base network and the altered system are identical (at least on the time scale required for the organism to "learn" new objectives). ...
This implies, with respect to resource allocation, that the base network and its genetically altered counterpart may still be treated as optimal systems; however, the set of competing physiological choices ...
However, in this instance, we must invoke the postulate 2.7 to account for allocation to a single enzyme from multiple pools. ...
doi:10.1021/bp990017p
pmid:10356258
fatcat:ovpuhxdno5eevlqub4to7sldaq
Wing aerodynamic optimization using efficient mathematically-extracted modal design variables
2018
Optimization and Engineering
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Wing shape optimization in transonic flow is performed using an upwind flow-solver and parallel gradient-based optimizer, and a small number of global deformation modes are compared to a section-based ...
A novel approach is used for deriving design variables using a singular value decomposition of a set of training aerofoils to obtain an efficient, reduced set of orthogonal 'modes' that represent typical ...
A global mode is a single deformation of all control points, with the modes scaled and rotated according to the local geometry. 4. ...
doi:10.1007/s11081-018-9376-7
fatcat:sv5sj4rmcnd4zidwnra2gougcy
Cooperative co-evolution for feature selection in Big Data with random feature grouping
2020
Journal of Big Data
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A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. ...
RFG can be used in CC-based FS processes, hence called Cooperative Co-Evolutionary-Based Feature Selection with Random Feature Grouping (CCFSRFG). ...
Erchuan Zhang of the ECU School of Science to assist in defining the probability functions for the proposed random feature grouping decomposition strategies. ...
doi:10.1186/s40537-020-00381-y
fatcat:zbajchanbve5hm5xwtlkxt6pne
2020 SP Year End Indexes
2020
IEEE Signal Processing Magazine
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, MSP May 2020 114-122 Computing Large-Scale Matrix and Tensor Decomposition With Structured Factors: A Unified Nonconvex Optimization Perspective. ...
., +, MSP Nov. 2020 195-199 D Data analysis Computing Large-Scale Matrix and Tensor Decomposition With Structured Factors: A Unified Nonconvex Optimization Perspective. ...
doi:10.1109/msp.2020.3037982
fatcat:ny2cx6ix45cfhcqgpvituyiri4
Parallel computational optimization in operations research: A new integrative framework, literature review and research directions
2019
European Journal of Operational Research
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This heterogeneity is accompanied by a lack of unifying frameworks for parallel optimization across methodologies, application fields and problems, and it has finally led to an overall fragmented picture ...
This review addresses the aforementioned issues with three contributions: First, we suggest a new integrative framework of parallel computational optimization across optimization problems, algorithms and ...
On parallelization of a stochastic dynamic programming algorithm for solving large-scale mixed 0-1 problems under uncertainty. Top 23, 703-742. ...
doi:10.1016/j.ejor.2019.11.033
fatcat:5olyajxlyrca7kmqscpgjef4vq
Parallel Local Search
[chapter]
2018
Handbook of Parallel Constraint Reasoning
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This situation is further compounded by the good adequacy exhibited by this class of search procedures for large-scale parallel operation. ...
As real-life cases of combinatorial optimization easily degrade into intractable territory for exact or approximation algorithms, local search metaheuristics hold undeniable interest. ...
The 2007 work by Aydin [11] proposed a study of different cooperative topologies for agent-based metaheuristics. ...
doi:10.1007/978-3-319-63516-3_10
fatcat:ah7ngeucercjvbbo6qr757boq4
Scaling Spark on HPC Systems
2016
Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing - HPDC '16
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For example, on the software side we develop a file pooling layer able to improve per node performance up to 2.8⇥. ...
On the hardware side we evaluate a system with a large NVRAM bu↵er between compute nodes and the backend Lustre file system: this improves scaling at the expense of per-node performance. ...
Jacobsen at NERSC for implementing the support for file mounts inside Shifter. ...
doi:10.1145/2907294.2907310
dblp:conf/hpdc/ChaimovMCIIS16
fatcat:cuske3juqbd3rdq7nzkeuvnf7a
Parametric Topology Optimization with Multi-Resolution Finite Element Models
2019
International Journal for Numerical Methods in Engineering
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2018 pre-print arXiv:1808.10367v1 fatcat:mhxnoix6nfejtc3od6sf7bei6i
We present a methodical procedure for topology optimization under uncertainty with multi-resolution finite element models. ...
We use our framework in a bi-fidelity setting where a coarse and a fine mesh corresponding to low- and high-resolution models are available. ...
Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. ...
doi:10.1002/nme.6063
fatcat:xtmsl4a3e5bh7nq2asijgptmtm
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