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

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, es...

• Type: Conference
• Year: 2009
• Where: AUSAI
• Authors: Upali K. Wickramasinghe, Xiaodong Li
• Keywords: Algorithm, Artificial Intelligence, AUSAI 2009, Distance Metric, Many-objective Problems