MCDA and multiobjective evolutionary algorithms

Abstract

Evolutionarymultiobjective optimization promises to efficiently generate a representative set of Pareto optimal solutions in a single optimization run. This allows the decision maker to select the most preferred solution from the generated set, rather than having to specify preferences a priori. In recent years, there has been a growing interest in combining the ideas of evolutionary multiobjective optimization and MCDA.MCDA can be used before optimization, to specify partial user preferences, after optimization, to help select the most preferred solution from the set generated by the evolutionary algorithm, or be tightly integrated with the evolutionary algorithm to guide the optimization towards the most preferred solution. This chapter surveys the state of the art of using preference information within evolutionary multiobjective optimization.