  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.
