Parallel Algorithms for Combinatorial Optimization Problems

Dear Colleagues,

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.

Charalampos Konstantopoulos
Guest Editor

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• Parallel exact algorithms;
• Parallel approximation algorithms;
• Parallel fixed parameter algorithms;
• Parallel heuristics/metaheuristics for combinatorial optimization problems;
• Parallel techniques in integer linear programming;
• Parallel algorithms for multi-objective optimization problems