An updated performance metric for preference-based evolutionary multi-objective optimization algorithms
Abstract
Evolutionary multi-objective optimization (EMO) algorithms are widely used to solve problems involving multiple conflicting objectives. In general, these problems result in a well-distributed and diverse set of Pareto-optimal solutions, consisting of individual objective-optimal solutions at their extreme and various compromise objective solutions at their core. However, in practice, decision-makers (DMs) usually have certain pre-conceived preference information which may make a majority of the Pareto solution set uninteresting to the DMs. In such cases, DM's preference information can be utilized to update EMO algorithms to focus on the preferred part of the Pareto set, rather than the entire Pareto set. While EMO researchers have proposed preference-based EMO algorithms for this purpose, appropriate metrics to evaluate their performance have received lukewarm attention. In this paper, we critically analyze a recently proposed preference-based hypervolume (R-HV) metric for its sensitivity to handle various scenarios and propose an updated version to remedy the difficulties associated with it. The updated R-HV metric is then compared with the original R-HV metric on solutions obtained from a number of preference-based EMO algorithms. The suggestion of a more appropriate R-HV metric presented in this paper should encourage further research in preference-based multi-objective optimization.
Authors: Deepanshu Yadav, Palaniappan Ramu, Kalyanmoy Deb
Published in: Genetic and Evolutionary Computation Conference (GECCO) (2024)