In the last years, we have experienced a change in computing towards parallelism. The physical limits of integrated circuits being reached, computing performance now keep Moore’s law thanks to the replication of components. The default computer is nowadays a parallel machine. In this scenario, parallelism is a must in any modern software in order to make an effective use of the available resources.
The purpose of this special issue is to collect the main recent trends and designs in parallel and distributed computing for solving hard optimization problems.
Topics of interests include:
•Integer programming, linear programming, nonlinear programming;
•Global optimization, combinatorial optimization, multi-objective optimization, dynamic optimization;
•Exact methods, heuristics, metaheuristics, nature inspired algorithms, machine learning;
•Multi-core, many-core, GPGPU, cluster, Grid, and Cloud computing platforms;
•Parallel sparse matrix computations, graph algorithms, load balancing;
•Peer to peer computing and optimization problems;
•Theoretical studies on parallel and distributed computing
•Real world applications
All high quality submitted papers related to the listed topics will be considered for publication in this special issue, provided they are recommended for publication after the review process. All manuscripts submission and review will be handled by Elsevier Editorial System http://ees.elsevier.com/jpdc. Please, make sure to choose PDCO2017 as article type. All papers should be prepared according to JPDC Guide for Authors. Manuscripts should be no longer than 35 double-spaced pages without the title page, abstract, or references.
– Full Paper Regular Submission Due: September 15th, 2017
– Notification of Results: December 15th, 2017
– Revisions Due: January 31st, 2017
– Notifications of Final Acceptance: April 30th, 2018
– Submissions of Final Revised Papers: May 31st, 2018
– Bernabe Dorronsoro, University of Cadiz, Spain
– Gregoire Danoy, University of Luxembourg, Luxembourg
– Didier El Baz, team CDA, LAAS-CNRS, France