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Metaheuristics are used for solving optimization problems since they are able to compute near optimal solutions in reasonable times. However, solving large instances it may pose a challenge even for these techniques. For this reason, metaheuristics parallelization is an interesting alternative in order to decrease the execution time and to provide a different search pattern. In the last years, GPUs have evolved at a breathtaking pace. Originally, they were specific-purpose devices, but in a few years they became general-purpose shared memory multiprocessors. Nowadays, these devices are a powerful low cost platform for implementing parallel algorithms. In this paper, we present a preliminary version of PUGACE, a cellular Evolutionary Algorithm framework implemented on GPU. PUGACE was designed with the goal of providing a tool for easily developing this kind of algorithms. The experimental results when solving the Quadratic Assignment Problem are presented to show the potential of the proposed framework.

This paper elaborates on a new, fresh parallel optimization algorithm specially engineered to run on Graphic Processing Units (GPUs). The undelying operation relates to Systolic Computation. The algorithm, called Systolic Genetic Search (SGS) is based on the synchronous circulation of solutions through a grid of processing units and tries to profit from the parallel architecture of GPUs. The proposed model has shown to outperform a random search and two genetic algorithms for solving the Knapsack Problem over a set of increasingly sized instances. Additionally, the parallel implementation of SGS on a GeForce GTX 480 graphics processing unit (GPU), obtaining a runtime reduction up to 35 times.

In the context of Mobile Robotics, the efficient resolution of the Path Planning problem is a key task. The model of the environment and the search algorithm are basic issues in the resolution of the problem. This paper highlights the main features of Path Planning proposal for mobile robots in static environments. In our proposal, the path planning is based on Voronoi diagrams, where obstacles in the environment are considered as the generating points of the diagram, and a genetic algorithm is used to find a path without collisions from the robot initial to target position. This work combines some ideas presented by Roque and Doering, who use Voronoi diagrams for modelling the environment, and other ideas presented by Zhang et al. who adopt a genetic algorithm for computing paths on a regular grid based environment, considering certain quality attributes. The main results were probed both in simulated and real environments.

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