Toward Large-Scale Evolutionary Multitasking: A GPU-Based Paradigm
针对现有进化多任务算法只能处理两个任务、无法满足云端大规模优化需求的问题，提出一种基于GPU和CUDA的岛模型新范式，能高效处理大量连续优化任务，并开发了隐式和显式知识迁移机制。
Evolutionary multitasking (EMT), which shares knowledge across multiple tasks while the optimization progresses online, has demonstrated superior performance in terms of both optimization quality and convergence speed over its single-task counterpart in solving complex optimization problems. However, most of the existing EMT algorithms only consider handling two tasks simultaneously. As the computational cost incurred in the evolutionary search and knowledge transfer increased rapidly with the number of optimization tasks, these EMT algorithms cannot meet today’s requirements of optimization service on the cloud for many real-world applications, where hundreds or thousands of optimization requests (labeled as large-scale EMT) are often received simultaneously and require to be optimized in a short time. Recently, graphics processing unit (GPU) computing has attracted extensive attention to accelerate the applications possessing large-scale data volume that are traditionally handled by the central processing unit (CPU). Taking this cue, toward large-scale EMT, in this article, we propose a new EMT paradigm based on the island model with the compute unified device architecture (CUDA), which is able to handle a large number of continuous optimization tasks efficiently and effectively. Moreover, under the proposed paradigm, we develop the GPU-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">implicit</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explicit</i> knowledge transfer mechanisms for EMT. To evaluate the performance of the proposed paradigm, comprehensive empirical studies have been conducted against its CPU-based counterpart in large-scale EMT.