Purpose and Topics

Plenty of hard problems in a wide range of areas including engineering design, telecommunications, logistics, biology, etc., have been modeled and tackled successfully with optimization approaches. These approaches fall into two major categories: meta-heuristics (evolutionary algorithms, particle swarm, ant/bee colonies, simulated annealing, Tabu search, etc.) and exact methods (Branch-and-X, dynamic programming, etc.). Nowadays, optimization problems become increasingly large and complex, forcing the use of parallel computing for their efficient and effective resolution. On the other hand, parallel computing has recently undergone a significant evolution with the emergence of new high performance computing environments including clusters, grids or cloud computing infrastructures, multi-core supercomputers and accelerators (e.g. GPU, FPGA). The design and implementation of parallel optimization methods raise several issues related to the characteristics of these methods and those of the new hardware execution environments at the same time. This workshop seeks to provide an opportunity for the researchers to present their original contributions on the joint use of advanced (discrete or continuous, single or multi-objective, static or dynamic) optimization methods and parallel computing, and any related issues.

Topics include (but are not limited to):

• Parallel models (algorithmic-level, iteration-level, solution-level, hierarchical) for optimization methods.
• Parallel mechanisms for hybridization of optimization methods.
• Redesign of parallel real or simulation-based algorithms for their adaptation to emerging computing environments including grids/clouds, multi-core supercomputers and accelerators.
• Implementation issues of parallel optimization algorithms on the above environments.
• (Grid/cloud or multi-core or accelerators)-aware frameworks for the design and implementation of efficient parallel optimization techniques.
• Computational studies reporting results for hard and challenging problems.
• Issues and novel approaches and experiments in parallel solving real-world applications in engineering design, telecommunications, logistics, transport, etc.
• Theory and performance metrics for parallel optimization on grids/clouds, multi-core supercomputers and accelerators.