Over the last decades, parallel computing has been revealed as an unavoidable way to deal with large problem instances of difficult optimization problems. The design and implementation of parallel metaheuristics are strongly influenced by the computing platform.

Graphics processing units (GPU) have emerged as a new popular support for massively parallel computing. Such resources supply a great computing power, are energy-efficient, and unlike grids, they are highly available everywhere. Indeed, GPU computing is believed to be extremely useful to speed up many complex algorithms.

One of the major issues for metaheuristics is to rethink existing parallel models and programming paradigms to allow their deployment on GPU accelerators.

The aim of this special session is to present recent research works on the parallelization of metaheuristics on modern parallel architectures, in particular GPU architectures. We invite a broad spectrum of papers, ranging from case studies focused on specific problems and applications to theoretical aspects and frameworks. Topics of interest include, but are not limited to:

- Graphics processing units in optimization - Parallelization of metaheuristics on multi-core machines - Design and implementation of metaheuristics on cluster or grid-based parallel architectures - Parallel metaheuristics on other parallel and distributed architectures - Software frameworks for the deployment of parallel metaheuristics - Statistical assessment of performance for parallel algorithms

Submission of papers should be in shape of a 2-page abstract (including figures, tables, and references).