Inspired from colonizing weeds, a simple but effective multi-objective optimization algorithm, named as Multi-objective Invasive Weed Optimization (IWO_MO), has been proposed recently and proved to be superior to other state-of-the-art algorithms. In this paper, we propose the Intra-and Inter-operator, which exchanges information among the Intra- and Inter-Communities of weeds, to further improve the performance of the IWO_MO. The proposed algorithm, named as IWO_MO2, is tested on various multi-objective benchmark test functions. Results suggest that the proposed IWO_MO2 is more effective on tackling multi-objective problems and the obtained Pareto approximative Front is very close to the true Pareto optimal Front.
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