Abstract

This work presents a new approach of evolutionary multiobjective optimization algorithms augmented by collective intelligence interaction. In particular, we describe the extension of some well-known algorithms (NSGA-II, SMS-EMOA) to include collective online preferences and collaborative solutions into the optimization process. These innovative methods allow groups of decision makers to highlight the regions of Pareto frontier that are more relevant to them as to focus the search process mainly on those areas. Additionally, interactive and cooperative genetic algorithms work on users’ collaborative preferences to improve the reference points and the population quality throughout the evolutionary progress. Rather than a unique or small group of decision makers provided with unilateral preferences, this paper promotes dynamic group preferences to aggregate consistent collective reference points and creative solutions to enhance multi-objective results. As part of this work we test the algorithms efficiency when face with some synthetic problem as well as a real-world case scenario.