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.
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Manuscript submission deadline: February 29, 2016
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Guest Editor
Dr. Charalampos Konstantopoulos
Department of Informatics
University of Piraeus, Greece (konstant@unipi.gr)
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