Research on evolutionary multiobjective optimization (EMO) started in the mid 1980s and---unlike many multicriteria decision making (MCDM) approaches---deals with stochastic algorithms that are able to find multiple near Pareto-optimal solutions in a single algorithm run. Evolutionary algorithms and other classes of metaheuristics are often used to solve difficult problems arising in both combinatorial and continuous multiobjective optimization. Such metaheuristics include evolutionary algorithms, neighborhood-based search, simulated annealing, tabu search, iterated local search, memetic algorithms, hyperheuristics, etc.
As both EMO and MCDM deal with multiobjective optimization problems, it is natural to bring these two research fields together. The first attempts to do so have been conducted in recent years. Joint Dagstuhl seminars as well as several EMO tracks and workshops at the last MCDM conferences and the MCDM sessions at the EMO conferences are most prominent examples. As this cross-fertilization of ideas has been gathering momentum, where now really significant research outcomes are emerging, the integration of the upcoming EMO session into the mainstream MCDM'2015 conference is therefore a nice opportunity to bring together researchers from the main research fields in multiobjective optimization. It follows the tradition of EMO workshops and special sessions during the MCDM conference, and in particular its predecessors in 2008 in Auckland, New Zealand, in 2011 in Jyväskylä, Finland and in 2013 in Málaga, Spain.
The main focus of the EMO session is to present the most recent advances in the EMO field to MCDM researchers in order to establish and foster collaborations between the two fields.
Associated with the EMO session will be an upcoming Special Issue of the Computers & Operations Research journal with the focus of presenting the current state-of-the-art in EMO to the Operations Research community. Further details can be found on the Special Issue page.