posted by organizer: konstant || 335 views || tracked by 2 users: [display]

MDPI Algorithms Journal 2016 : MDPI Algorithms Journal - Special Issue on Parallel Algorithms for Combinatorial Optimization Problems

FacebookTwitterLinkedInGoogle

Link: http://www.mdpi.com/journal/algorithms/special_issues/parallel_algorithms
 
When N/A
Where N/A
Submission Deadline Jun 30, 2016
Categories    parallel algorithms   combinatorial optimization
 

Call For Papers

Call for Papers

MDPI Algorithms Journal

Special Issue on Parallel Algorithms for Combinatorial Optimization Problems (http://www.mdpi.com/journal/algorithms/special_issues/parallel_algorithms)

Scope

Combinatorial optimization problems model most of the application scenarios frequently arising in practice. Unfortunately, optimal solutions to these problems are hard to obtain, with most of them having high computational complexity. Even in the case of problems admitting polynomial time solutions, e.g., the classical shortest path problem, the relevant applications should now work on very large input instances or should cope with a large number of concurrent users. Thus, faster solution methods are clearly needed for achieving real time responses for problems previously considered as easy ones. The same also holds for most heuristic methods or approximation algorithms that have been proposed for obtaining approximate solutions for hard optimization problems in acceptable execution times. For large-scale problems, these techniques are inadequate, and much faster algorithmic techniques are needed again.

Due to the aforementioned limitations, parallelism has been considered a means of deriving faster algorithmic solutions. Parallel computation is virtually ubiquitous nowadays and can be found in all modern computing platforms. Although the concept of parallel execution is simple, its application on combinatorial optimization problems is not straightforward, due to the inherently irregular control flow that the algorithms for this kind of problem commonly have. In this Special Issue, we solicit contributions that will propose new methodologies for solving problems in combinatorial optimization using parallel computation, either in shared-memory systems (e.g., multi-core/many-core processors, GPUs, etc.) or in distributed-memory systems (e.g., clusters, cloud architectures, etc.). Topics of interest include, but are not limited to:

- Parallel exact algorithms: e.g., divide-and-conquer, dynamic programming, etc.

- Parallel approximation algorithms- Parallel fixed parameter algorithms

- Parallel heuristics/metaheuristics for combinatorial optimization problems: e.g., local search, simulated annealing, evolutionary computation, swarm intelligence computation, etc.

- Parallel techniques in integer linear programming: e.g., parallelization of branch-and-bound, column generation, and cutting plane methods or their combinations (i.e., branch-and-cut or branch-and-price).

- Parallel algorithms for multi-objective optimization problems.

-----------------------------------------------------------------------------------------
Manuscript submission deadline: June 30, 2016
-----------------------------------------------------------------------------------------
Guest Editor
Dr. Charalampos Konstantopoulos
Department of Informatics
University of Piraeus, Greece (konstant@unipi.gr)

Related Resources

PDAA 2016   PDAA 2016: The 8th International Workshop on Parallel and Distributed Algorithms and Applications
COCOA 2016   Conference on Combinatorial Optimization and Applications
WACEBI 2016   2016 Workshop on Accelerator-Enabled Algorithms and Applications in Bioinformatics
ICDCS 2017   International Conference on Distributed Computing Systems
STACS 2017   34th International Symposium on Theoretical Aspects of Computer Science
ADMA 2016   Advanced Data Mining and Applications
SODA 2017   Symposium on Discrete Algorithms
ISAAC 2016   International Symposium on Algorithms and Computation
IA^3 2016   Sixth Workshop on Irregular Applications: Architecture and Algorithms
MLPM 2016   Special Session on Machine Learning for Predictive Models in Engineering Applications - IEEE ICMLA 2016