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We present a set of interactive evolutionary multiple objective optimization (MOO) methods, called NEMO-GROUP. All proposed approaches incorporate pairwise comparisons of several decision makers (DMs) into the evolutionary search, though evaluating the suitability of solutions for inclusion in the next population in different ways. The performance of algorithms is quantified with various convergence factors derived from the extensive computational tests on a set of benchmark problems. The best individuals and complete populations of solutions constructed by the proposed approaches are evaluated in terms of both utilitarian and egalitarian group value functions for different numbers of DMs. Our results indicate that more promising directions for optimization can be discovered when exploiting the set of value functions compatible with the DMs' preferences rather than selecting a single representative value function for each DM or all DMs considered jointly. We demonstrate that NEMO-GROUP is flexible enough to account for the weights assigned to the DMs. We also show that by appropriately adjusting the elicitation interval and starting generation of the elicitation, one could significantly decrease the number of pairwise comparisons the DMs need to perform to construct a satisfactory solution.

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