Evolutionary Multi-Objective Optimization Algorithm Framework with Three Solution Sets

TL;DR

This paper proposes a flexible evolutionary multi-objective optimization framework with three solution sets to better accommodate different decision-making needs, enhancing solution presentation and explainability.

Contribution

It introduces a general EMO framework with three solution sets, allowing customizable population, archive, and final solution sets for diverse decision-making scenarios.

Findings

Framework outperforms standard approaches in experiments

Allows flexible solution set sizes and selection

Provides a basis for explainable EMO methods

Abstract

It is assumed in the evolutionary multi-objective optimization (EMO) community that a final solution is selected by a decision maker from a non-dominated solution set obtained by an EMO algorithm. The number of solutions to be presented to the decision maker can be totally different. In some cases, the decision maker may want to examine only a few representative solutions from which a final solution is selected. In other cases, a large number of non-dominated solutions may be needed to visualize the Pareto front. In this paper, we suggest the use of a general EMO framework with three solution sets to handle various situations with respect to the required number of solutions. The three solution sets are the main population of an EMO algorithm, an external archive to store promising solutions, and a final solution set which is presented to the decision maker. The final solution set is selected from the archive. Thus the population size and the archive size can be arbitrarily specified as long as the archive size is not smaller than the required number of solutions. The final population is not necessarily to be a good solution set since it is not presented to the decision maker. Through computational experiments, we show the advantages of this framework over the standard final population and final archive frameworks. We also discuss how to select a final solution set and how to explain the reason for the selection, which is the first attempt towards an explainable EMO framework.