GPU-based cooperative coevolution for large-scale global optimization

To resolve the issue of the curse of dimensionality in continuous large-scale optimization problems, the cooperative coevolution divide-and-conquer framework was proposed by dividing the problem into several subcomponents either randomly or based on the interaction between variables, each of which can be optimized separately using metaheuristic suboptimizers. The goal of researchers is to optimize the performance of algorithms in terms of both quality of solution and computational speed, seeing that large-scale optimization can be a computationally expensive process. This work proposes a parallel implementation to the cooperative coevolution framework for solving large-scale global optimization problems using the Graphics Processing Unit (GPU) and CUDA platform. A distributed variant of the cooperative coevolution framework is outlined to expose a degree of parallelism. Features of the GPU parallel technology and CUDA platform such as shared and global memories are used to optimize the subcomponents of the problem in parallel, speeding up the optimization process while attempting to maintain comparable search quality to works in the literature. The CEC 2010 large-scale global optimization benchmark functions are used for conducting experiments and comparing results in terms of improvements in search quality and search efficiency. Results of proposed parallel implementation show that a speedup of up to x13.01 is possible on large-scale global optimization benchmarks using the GPUs.

Automated summary

In this paper, a parallel implementation of the cooperative coevolution framework for solving continuous large-scale optimization problems is proposed, utilizing GPU and CUDA platform to optimize problem subcomponents in parallel, leading to significant speedup in the optimization process.