Enhancing multi-objective Invasive Weed Optimization with information exchange in Intra- and Inter-Communities

Abstract

Inspired from colonizing weeds, a simple but effective multi-objective optimization algorithm, named as Multiobjective Invasive Weed Optimization (IWO MO), has been proposed recently and proved to be superior to other stateof-the-art algorithms. In this paper, we propose the Intraand 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.