A Distance Metric for Evolutionary Many-Objective Optimization Algorithms Using User-Preferences

Abstract

Abstract. In this paper we propose to use a distance metric based on user-preferences to efficiently find solutions for many-objective problems. In a user-preference based algorithm a decision maker indicates regions of the objective-space of interest, the algorithm then concentrates only on those regions to find solutions. Existing user-preference based evolutionary many-objective algorithms rely on the use of dominance comparisons to explore the search-space. Unfortunately, this is ineffective and computationally expensive for many-objective problems. The proposed distance metric allows an evolutionary many-objective algorithm's search to be focused on the preferred regions, saving substantial computational cost. We demonstrate how to incorporate the proposed distance metric with a user-preference based genetic algorithm, which implements the reference point and light beam search methods. Experimental results suggest that the distance metric based algorithm is effective and efficient, especially for difficult many-objective problems.

Incorporating user preferences in many-objective optimization using relation ε-preferred

“…Furthermore, several MOO problems in industrial applications consist of subproblems that have different levels of importance. The importance of a subproblem is specified by the user and different methods exist to model these user preferences or priorities (Schmiedle et al, 2001;Wickramasinghe and Li, 2009;Wagner and Trautmann, 2012). Considering both, many-objective optimization problems and user preferences, there is a need for algorithms that can combine these properties.…”

“…Furthermore, approaches are presented that consider user preferences in many-objective optimization (Wickramasinghe and Li, 2009;Auger et al, 2009;Wagner and Trautmann, 2012). In (Wickramasinghe and Li, 2009) a user-defined distance metric is used to guide the search on the basis of the Dominates relation.…”

“…In (Wickramasinghe and Li, 2009) a user-defined distance metric is used to guide the search on the basis of the Dominates relation. The incorporation of user preferences to the hypervolume approach has been investigated in (Auger et al, 2009;Wagner and Trautmann, 2012).…”

Incorporating User Preferences in Many-Objective Optimization using Relation Epsilon-Preferred

“…Furthermore, approaches are presented that consider user preferences in many-objective optimization (Wickramasinghe and Li, 2009;Auger et al, 2009;Wagner and Trautmann, 2012). In (Wickramasinghe and Li, 2009) a user-defined distance metric is used to guide the search on the basis of the Dominates relation. The incorporation of user preferences to the hypervolume approach has been investigated in (Auger et al, 2009;Wagner and Trautmann, 2012).…”