The rapid development of parallel and distributed computing paradigms has brought about great revolution in computing. Thanks to the intrinsic parallelism of evolutionary computation (EC), it is natural to implement EC on parallel and distributed computing systems. On the one hand, the computing power provided by parallel computing systems can significantly improve the efficiency and scalability of EC. On the other hand, data are collected and processed in a distributed manner, which brings a novel development direction and new challenges to EC. In this paper, we intend to give a systematic review on distributed EC (DEC). First, a new taxonomy for DEC is proposed from top design mechanism to bottom implementation mechanism. Based on this taxonomy, existing studies on DEC are reviewed in terms of purpose, parallel structure of the algorithm, parallel model for implementation, and the implementation environment. Second, we clarify two major purposes of DEC, i.e., improving efficiency through parallel processing for centralized optimization and cooperating distributed individuals/sub-populations with partial information to perform distributed optimization. Third, noting that the latter purpose of DEC is an emerging and attractive trend for EC with the booming of spatially distributed paradigms, this paper gives a systematic definition of the distributed optimization and classifies it into dimension distributed-, data distributed-, and objective distributed-optimization problems. Formal formulations for these problems are provided and various DEC studies on these problems are reviewed. We also discuss challenges and potential research directions, aiming to enlighten the design of DEC and pave the way for future developments.

翻译：并行与分布式计算范式的快速发展带来了计算领域的重大变革。得益于进化计算（EC）固有的并行性，将其部署在并行与分布式计算系统上具有天然优势。一方面，并行计算系统提供的算力可显著提升进化计算的效率与可扩展性；另一方面，数据以分布式方式采集和处理，为进化计算带来了全新发展方向与挑战。本文旨在系统性地综述分布式进化计算（DEC）领域。首先，我们从顶层设计机制到底层实现机制提出了一种新的DEC分类体系。基于该分类法，从算法目标、并行结构、实现并行模型及实现环境四个维度对现有DEC研究进行梳理。其次，我们明确了DEC的两大核心目标：一是通过并行处理提升集中式优化的效率，二是利用局部信息协调分布式个体/子种群实现分布式优化。进而指出，随着空间分布式范式的蓬勃发展，DEC的后一类目标正成为进化计算领域新兴且备受关注的趋势。本文对分布式优化进行了系统性定义，将其划分为维度分布式、数据分布式与目标分布式优化问题，给出了这些问题的形式化表述，并综述了相关DEC研究。最后，我们探讨了当前面临的挑战与潜在研究方向，旨在启迪DEC设计，为未来发展铺平道路。