SCOPE AND OBJECTIVES
Plenty of hard optimization problems in a wide range of areas such as health, manufacturing and production, logistics and supply chain management, energy, engineering, etc. are increasingly large and complex in terms of number of input parameters, decision variables, objective functions and landscape complexity. These problems are often tackled using optimization approaches including greedy algorithms, exact methods (dynamic programming, Branch-and-X, constraint programming, A*, etc.) and metaheuristics (evolutionary algorithms, particle swarm, ant or bee colonies, simulated annealing, Tabu search, etc.). Solving efficiently and effectively large and complex problems requires the joint use of these approaches and massively parallel heterogeneous computing.
In addition, for many real-world problems (e.g. in engineering design) the evaluation of the objective function(s) often consist(s) of the execution of an expensive simulation as a black-box complex system. This is for instance typically the case in aerodynamics where a CFD-based simulation may require several hours. Optimization algorithms, even if combined with parallel computing, fail to solve these simulation-based optimization problems. A typical approach to deal with the computational burden is to use (instead of real values) cheaper data-driven approximations of the objective function(s) socalled surrogates or meta-models. A wide range of surrogates was applied during the last decade including: classical regression models such as polynomial regression or response surface methodology, support vector machines, artificial neural networks (ANN), radial basis functions, and kriging or Gaussian process. ANN is probably the most prevalent of them with the recent \”explosion\” of the deep learning popularized thanks to GPGPUs.
This special issue seeks to provide an opportunity for researchers to present their original contributions on the joint use of advanced single- or multi-objective optimization methods, simulation and/or its data-driven approximation, and distributed and/or parallel multi/many-core computing, and any related issues. The special issue topics include (but are not limited to) the following:
– Simulation-based optimization.
– Parallel exact optimization/metaheuristics for solving complex problems on multi-core processors, accelerators/co-processors (e.g. GPU), clusters, grids/clouds, etc.
– (Parallel) hybrid algorithms combining optimization and simulation.
– (Parallel) surrogate-assisted optimization.
– Implementation issues of methods combining optimization, simulation and/or parallel computing.
– Software frameworks for the design and implementation of parallel and/or distributed simulation and/or surrogate-assisted techniques.
– Simulation-based optimization applications including healthcare, manufacturing, logistics, biological applications, advanced big data analytics, engineering design, etc.
– Computational/theoretical studies reporting results on solving complex problems using the joint use of parallel computing, optimization and simulation.
The first step of the submission process is to send a one-page abstract including a title, a list of authors and their affiliations and emails. It should be indicated the intent to submit to this special issue either a new manuscript or a significantly extended version of a previously published/presented paper. The authors are invited to submit their abstract as an email attachment to the Special Issue Guest Editors. In the second step of the submission process, only manuscripts of approved abstracts will be considered. The submitted manuscripts must not have been published or simultaneously submitted elsewhere. For the submitted extended papers, an extension of at least 30% beyond that in the published proceedings is expected. Each submitted paper (extended or not) will receive thorough reviews and evaluation. Papers will be selected based mainly on their originality, scientific and technical quality of the contributions, organization and presentation and relevance to the special issue. The JoCS\’s submission system will be open for submissions to our Special Issue from 07 February 2019. When submitting your manuscript please select the article type \”VSI: PaCOS\”. Please submit your manuscript before 28 February 2019.
Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.
Please see an example here: https://www.sciencedirect.com/journal/science-of-the-totalenvironment/special-issue/10SWS2W7VVV
Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and link to submit your manuscript is available on the Journal\’s homepage at: https://www.journals.elsevier.com/journal-of-computational-science/
Inquiries, including questions about appropriate topics, may be sent electronically to the Guest Editors.
IMPORTANT DATES (Subject to change)
– Abstract submission due: January 15th, 2019
– Paper submission due: February 28th, 2019
– First-round acceptance decision notification: May 31th, 2019
– First revision submission due: August 31th, 2019
– Second-round acceptance decision notification: October 31th, 2019
– Second-round submission due: November 30th, 2019
– Notification of final decision: January 15th, 2020
– Target (tentative) publication date: March 2020
GUEST EDITORS (INFORMATION)
Prof. Nouredine Melab
Université de Lille/INRIA Lille – Nord Europe/CNRS CRIStAL, Lille, France
Dr. Imen Chakroun
Exascience Life Lab, IMEC Research Center, Leuven, Belgium
Dr. Jan Gmys
Université de Mons / Faculté Polytechnique, Mons, Belgium
Dr. Peter Korošec
Jožef Stefan Institute, Ljubljana, Slovenia