Including Users Preferences in the Decision Making for Discrete Many Objective Optimization Problems
Nancy Pérez, Oliver Cuate, Oliver Schütze, Alejandro Alvarado · Computación y Sistemas · 2016
In many applications one is faced with the problem that many objectives have to be optimized concurrently leading to a many objective optimization problem (MaOP). One important characteristic of discrete MaOPs is that its solution set, the so-called Pareto set, consists of too many elements to be efficiently computed. Thus, though specialized evolutionary algorithms are in principle capable of computing a set S of well spread candidate solutions along the Pareto set, it is not guaranteed that the decision maker of the underlying problem will find the "ideal" solution within S for his or her problem. We argue in this paper that it makes sense to perform akind of post processing for a selected solution s 2 S. More precisely, we will propose two different methods that allow to steer the search from s along the Paretoset into user specified directions. Numerical resultson instances of the vehicle routing problem with time windows will show the effectivity of the novel methods.