Learning to diversify in complex interactive Multiobjective O ptimization

Many real-world problems have a natural formulation as Multiobjective Optimization Problems (MOPs), in which multiple conflicting objectives need to be simultaneously optimized. A popular approach to deal with the resulting complexity consists of interacting with the Decision Maker (DM) during optimization, progressively focusing towards her preferred area in the decision space. In BC-EMO, an evolutionary MOP approach based on a “learning while optimizing” strategy, machine learning techniques are used to interactively learn an approximation of the DM utility function, which guides the search for candidate solutions. While extremely effective in early focusing towards the most promising search directions, the algorithm suffers from a lack of diversification in dealing with complex MOP problems: a premature convergence often returns suboptimal solutions. In this paper we address the problem by introducing improved diversification strategies both at the evolutionary level and in DM preference elicitation. Substantial improvements are obtained on challenging benchmark problems with complex Pareto-optimal sets and non-linear DM utility functions.