CrowdEC: Crowdsourcing-Based Evolutionary Computation for Distributed Optimization

Crowdsourcing utilizes the crowd intelligence for pervasive data sensing and processing. When the processing task is a decision-making and optimization problem, the objective is evaluated based on sensed data, which is defined as crowdsourcing-based distributed optimization (CrowdDO). As evolutionary computation (EC) is a powerful technique for black-box and data-driven optimization problems, this paper combines crowdsourcing and EC to propose crowdsourcing-based EC (CrowdEC) for CrowdDO. CrowdEC performs optimization based on a server and a crowd of workers. Once receiving a CrowdDO request, the server posts the problem to workers. Each worker senses its own data and makes local decisions by local EC optimizer. Due to the heterogeneity of worker behaviors and devices, the sensed data are partial with noises, and thus the server needs to coordinate global optimization based on workers information. To avoid the leakage of worker privacy, workers only compare optimization results with adjacent workers and report comparison results to the server. With partial comparison results, the server adopts the competitive ranking to guide workers cooperation and develop the reliability detection to distinguish unreliable workers. A crowdsourcing-based level-based learning swarm optimizer is implemented as an example. Comparison experiments on benchmark testsuites and distributed clustering optimization demonstrate the potential applications of CrowdEC.