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A Parallel Algorithm for Global Optimization Problems in a Distribuited Computing Environment

Marco Gaviano, Daniela Lera, Elisabetta Mereu
2012 Applied Mathematics  
The problem of finding a global minimum of a real function on a set S R n occurs in many real world problems.  ...  To evaluate the algorithm performances the speedup and the efficiency are reported for each configuration.   ...  Introduction In this paper we consider the following global optimization problem.  ... 
doi:10.4236/am.2012.330194 fatcat:yixxkwjpobcvrmtdnn7oa5unqy

Parallel algorithms for continuous multifacility competitive location problems

J. L. Redondo, J. Fernández, I. García, P. M. Ortigosa
2009 Journal of Global Optimization  
This hard-to-solve global optimization problem has been addressed in [38] using several heuristic approaches.  ...  The parallelization follows a coarse-grain model, where each processing element executes the UEGO algorithm independently of the others during most of the time.  ...  Whereas some continuous location models are easy to solve (like, for instance, the classical Weber problem [45] ), other more realistic models lead to global optimization problems which are very hard  ... 
doi:10.1007/s10898-009-9455-6 fatcat:int5erv7i5ef5it7ovpx2v7e7i

Parallel hybrid algorithm for global optimization of problems occurring in MDS-based visualization

A. Žilinskas, J. Žilinskas
2006 Computers and Mathematics with Applications  
For the implementation of a multidimensional scaling technique a difficult global optimization problem should be solved.  ...  To attack such problems a hybrid global optimization method is developed combining evolutionary global search with local descent.  ...  A parallel version of the proposed hybrid algorithm is developed applicable to a reliable solution of global optimization problems up to 300 variables corresponding to visualization of real-world data.  ... 
doi:10.1016/j.camwa.2006.08.016 fatcat:4nfmh5yaczblxhdp5naroqv22m

An exact parallel objective space decomposition algorithm for solving multi-objective integer programming problems

Ozgu Turgut, Evrim Dalkiran, Alper E. Murat
2019 Journal of Global Optimization  
Journal of Global Optimization (2019) 75:35-62 37 optimizing objectives lexicographically.  ...  We propose a deterministic exact parallel algorithm for solving MOIP problems with any number of objectives.  ...  Acknowledgements We would like to thank to the Editor and three anonymous reviewers for their invaluable feedback, suggestions, and comments that helped greatly improve the content, presentation clarity  ... 
doi:10.1007/s10898-019-00778-x fatcat:6niethkmvjaqtkta4kklhso7je

Parallel Global Optimization Algorithm for Obtaining Uniform Convergence When Simultaneously Solving a Set of Global Optimization Problems
Параллельный алгоритм для получения равномерного приближения решений множества задач глобальной оптимизации с нелинейными ограничениями

2020 Bulletin of the South Ural State University Series Computational Mathematics and Software Engineering  
Sets of similar global optimization problems appear for an instance after scalarization of multi-objective problems or when a global optimization problem has a discrete parameter which takes a finite number  ...  Efficiency of the implemented parallel algorithm is evaluated on several sets of synthetically generated constrained global optimization problems and on a scalarized multi-objective problem.  ... 
doi:10.14529/cmse200201 fatcat:jorf3pvfxbefdb6ujivh3pclri

Use of the Parallel Characteristical Algorithms for Solving Multivariate Problems of Global Optimization
Использование параллельных характеристических алгоритмов для решения многомерных задач глобальной оптимизации

2014 Bulletin of the South Ural State University Series Computational Mathematics and Software Engineering  
CHARACTERISTICAL ALGORITHMS FOR SOLVING MULTIVARIATE PROBLEMS OF GLOBAL OPTIMIZATION K.A.  ...  Keywords: global optimization, multiextremal functions, dimension reduction, characteristical algorithms, parallel algorithms. Глобально-оптимальное решение - * = (0, . . . ,0), ( * ) = 0.  ... 
doi:10.14529/cmse140409 fatcat:pbwa4s62dnfypnvi4vwgx3vfey

Limitations of parallel global optimization for large-scale human movement problems

Byung-Il Koh, Jeffrey A. Reinbolt, Alan D. George, Raphael T. Haftka, Benjamin J. Fregly
2009 Medical Engineering and Physics  
This study evaluates the applicability of a parallel particle swarm global optimization algorithm to large-scale human movement problems.  ...  For both problems, a single run with a gradient-based nonlinear least squares algorithm found a significantly better solution than did 10 runs with the global particle swarm algorithm.  ...  Acknowledgments This study was supported by NIH National Library of Medicine Grant R03LM007332, a Whitaker Foundation Biomedical Engineering Research Grant, and NIH National Center for Medical Rehabilitation  ... 
doi:10.1016/j.medengphy.2008.09.010 pmid:19036629 pmcid:PMC2757319 fatcat:qsjts4spozbndozjb3chs23i2i

An implementation of a parallel generalized branch and bound template

M. Baravykaite, R. Čiegis
2007 Mathematical Modelling and Analysis  
A parallel version of user's algorithm is obtained automatically. A new derivative-free global optimization algorithm is proposed for solving nonlinear global optimization problems.  ...  MPI is used for underlying communications. A paradigm of domain decomposition (data parallelization) is used to construct a parallel algorithm.  ...  Acknowledgment This work was supported by the Lithuanian State Science and Studies Foundation within the project on B-03/2007 "Global optimization of complex systems using high performance computing and  ... 
doi:10.3846/1392-6292.2007.12.277-289 fatcat:5pmlrxipffahzi6xcoeupymarq

Heterogeneous Parallel Computations for Solving Global Optimization Problems1

Ilja Lebedev, Victor Gergel
2015 Procedia Computer Science  
This paper presents an integrated approach to parallel solution of global optimization time-consuming problems.  ...  This approach is based on combining several schemes for reducing multidimensional optimization problems to one-dimensional ones.  ...  for solving high dimensional global optimization problems.  ... 
doi:10.1016/j.procs.2015.11.008 fatcat:5b5dcpcazzaodgzzfjezcmy5si

FPGA-Based Parallel Metaheuristic PSO Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation

Hsu-Chih Huang
2013 Journal of Intelligent and Robotic Systems  
This paper presents a field-programmable gate array (FPGA)-based parallel metaheuristic particle swarm optimization algorithm (PPSO) and its application to global path planning for autonomous robot navigating  ...  Experimental results are conducted to show the merit of the proposed FPGA-based PPSO path planner and smoother for global path planning of autonomous mobile robot navigation.  ...  This metaheuristic algorithm has been shown useful for global optimization problems in a wide variety of applications [12] [13] [14] [15] .  ... 
doi:10.1007/s10846-013-9884-9 fatcat:depsoxzptnhepjegzk5nin3vwa

Parallel Computing Approach to Solve Travelling Salesman Problem

Harshala C., Vivek B.
2017 International Journal of Computer Applications  
Travelling Salesman Problem (TSP) is eminent in combinatorial optimization problem.  ...  However, branch and bound algorithm not suitable for large scale TSP with sequential execution.  ...  the problem get visited and obtained the global optimized value i.e. the solution for that TSP.  ... 
doi:10.5120/ijca2017912780 fatcat:yohnjqzk2ffgplusmvfshhdrzi

A Theoretical Model for Global Optimization of Parallel Algorithms

Julian Miller, Lukas Trümper, Christian Terboven, Matthias S. Müller
2021 Mathematics  
We present a parallel algorithm model that allows for global optimization of their synchronization and dataflow and optimal mapping to complex and heterogeneous architectures.  ...  It utilizes a hierarchical decomposition of parallel design patterns as well-established building blocks for algorithmic structures and captures them in an abstract pattern tree (APT).  ...  Optimizations While the model allows for rich optimizations of parallel algorithms, it is limited in finding completely different algorithms for a specific problem.  ... 
doi:10.3390/math9141685 fatcat:disxmrpqtfa7fbwt6n7cuyqz7q

Parallel surrogate-assisted global optimization with expensive functions – a survey

Raphael T. Haftka, Diane Villanueva, Anirban Chaudhuri
2016 Structural And Multidisciplinary Optimization  
This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization.  ...  In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed.  ...  the global optimum or to solve more difficult optimization problems.  ... 
doi:10.1007/s00158-016-1432-3 fatcat:p6gewbd5o5dfpcmciznpnqm7am

New Optimization Approach Using Clustering-Based Parallel Genetic Algorithm [article]

Masoumeh Vali
2013 arXiv   pre-print
Clustering-Based Parallel Genetic Algorithm (CBPGA) in optimization problems is one of the solutions of this problem.  ...  In many global Optimization Problems, it is required to evaluate a global point (min or max) in large space that calculation effort is very high.  ...  The use of parallelism Genetic Algorithms in Optimization problem are one of the solutions for SLM.  ... 
arXiv:1307.5667v1 fatcat:ovbzqfke4ze3lavijgzr7aucki

PBCOPSO: A Parallel Optimization Algorithm for Task Scheduling in Cloud Environment

R. Jemina Priyadarsini, L. Arockiam
2015 Indian Journal of Science and Technology  
This work adopts a parallel approach that considers Bee Colony Optimization (BCO) in parallel with Particle Swarm Optimization (PSO) for cloud task scheduling.  ...  Methods: Evolutionary algorithms are widely used to find the suboptimal solution of a problem.  ...  The challenge of optimization methods is finding a global optimal. In Bee swarm Algorithm, tasks are chosen randomly for first algorithm. Then, makespan is calculated for that set of tasks.  ... 
doi:10.17485/ijst/2015/v8i16/63248 fatcat:6ceu3ab5hzh33ify7jq3f5glbq
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