Crowdsourcing is an emerging computing paradigm that takes advantage of the intelligence of a crowd to solve complex problems effectively. Besides collecting and processing data, it is also a great demand for the crowd to conduct optimization. Inspired by this, this paper intends to introduce crowdsourcing into evolutionary computation (EC) to propose a crowdsourcing-based evolutionary computation (CEC) paradigm for distributed optimization. EC is helpful for optimization tasks of crowdsourcing and in turn, crowdsourcing can break the spatial limitation of EC for large-scale distributed optimization. Therefore, this paper firstly introduces the paradigm of crowdsourcing-based distributed optimization. Then, CEC is elaborated. CEC performs optimization based on a server and a group of workers, in which the server dispatches a large task to workers. Workers search for promising solutions through EC optimizers and cooperate with connected neighbors. To eliminate uncertainties brought by the heterogeneity of worker behaviors and devices, the server adopts the competitive ranking and uncertainty detection strategy to guide the cooperation of workers. To illustrate the satisfactory performance of CEC, a crowdsourcing-based swarm optimizer is implemented as an example for extensive experiments. Comparison results on benchmark functions and a distributed clustering optimization problem demonstrate the potential applications of CEC.

翻译：众包是一种新兴的计算范式，利用群体智能有效解决复杂问题。除数据采集与处理外，群体执行优化任务也存在巨大需求。受此启发，本文拟将众包引入进化计算（EC），提出一种基于众包的进化计算（CEC）范式用于分布式优化。EC有助于众包优化任务，同时众包能够突破EC在规模化分布式优化中的空间限制。为此，本文首先介绍了基于众包的分布式优化范式，继而详细阐述CEC机制。CEC通过服务器与工人群体协作实施优化：服务器将大规模任务分发至工人群体，工人利用EC优化器搜索可行解并与相邻节点协同。为消除工人行为与设备异构性带来的不确定性，服务器采用竞争排名与不确定性检测策略引导工人协作。为验证CEC的优越性能，本文以基于众包的群智能优化器为例开展大量实验。基准函数测试与分布式聚类优化问题的对比结果表明，CEC具有广阔的应用前景。