POMM 2015 - International Workshop on Parallel Optimization using High Performance Computing (POMM 2015)

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 or 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, multi and many-core computing (e.g. GPU, FPGA) has recently undergone a significant evolution with the emergence of new high performance computing environments. Indeed, multi and many-core computing are provided in laptops, high performance workstations, clusters, grids, and clouds.
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, deterministic or stochastic, hybrid) optimization methods and distributed and/or parallel multi/many-core computing, and any related issues.
The POMM Workshop topics include (but are not limited to) the following:
Parallel models for optimization methods
Parallel mechanisms for hybridization of optimization methods
Redesign of parallel real or simulation-based optimization algorithms
Implementation issues of parallel optimization algorithms
Parallel-aware frameworks for parallel optimization techniques
Computational studies reporting results for hard and challenging optimization problems
Issues and novel approaches and experiments in parallel solving real-world applications
Theory and performance metrics for parallel optimization