PGCO 2014 - International Workshop on Parallel and Grid Computing for Optimization (PGCO 2014)

Plenty of hard problems in a wide range of areas, including engineering design, telecommunications, logistics, etc. have been modeled and tackled successfully with optimization approaches. These approaches fall into two major categories: meta-heuristics (evolutionary algorithms, scatter or swarm search, 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 and grid computing for their efficient and effective resolution. The design and implementation of parallel and/or Grid-based optimization methods raise several issues related to the characteristics of these methods and those of the hardware execution support 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 optimization methods and parallel and Grid computing, and any related issues.
The PGCO Workshop topics of interest include (but are not limited to) the following:
* Parallel Models (island model, master-worker, multi-start, etc.) for Meta-heuristics
* Parallel Models (multi-parametric, parallel tree exploration, etc.) for Exact Methods
* Parallel Mechanisms for Hybridization of Optimization Methods
* Implementation of Parallel Optimization Algorithms and Simulation on Clusters and Multi-core Processors
* Adaptation of Parallel Optimization Algorithms to Grids or P2P systems
* Frameworks for the Design and Implementation of Parallel and/or Grid-based Optimization Techniques
* Parallel Simulation-based Optimization Algorithms
* Computational Studies Reporting Results for Hard/Interesting Problems
* Issues and Novel Approaches and Experiments in Solving Real-world Applications in
Engineering Design, Telecommunications, Logistics, Transport, Business, etc.
* Theory and Performance Metrics for Parallel and/or Grid-based Optimization Algorithms