Distributed ACO based on a crowdsourcing model for multiobjective problem

MOP (Multiobjective Optimization Problem) is a prevailing research field for its well-modeling on the decision-making dilemma in the real world. We present a distributed ACO (Ant Colony Optimization) algorithm based on a crowdsourcing model, with a few innovative strategies as enhancement, for continuous MOPs. The original MOP is expected to be decomposed into multiple single-objective subtasks, which are then distributed to the individuals on the network, and the non-dominated front for the MOP is constructed by aggregating the solutions from the crowd. Finally, an experiment on the KUR test problem illustrates that our approach is practical.

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