Identifying Solutions of Interest for Practical Many-objective Problems using Recursive Expected Marginal Utility

Real-world problems often involve optimization of multiple conflicting objectives. Significant research has been directed recently towards development of multi-objective evolutionary algorithms that are scalable, i.e., able to deal with problems involving more than 3 objectives, commonly referred to as many-objective optimization problems. This has led to the emergence several new techniques that can deliver a set of trade-off solutions to approximate the Pareto optimal front of the problem. However, means to select solution(s) from this large trade-off set for final implementation/decision making has received relatively scarce attention. This paper aims to study and demonstrate the performance of recursive expected marginal utility (EMUr) approach for informed decision-making. Towards this goal, we apply the EMUr approach to identify solutions of interest for two practical examples and analyze the obtained set of solutions. The study highlights the desirable trade-off characteristics that the chosen solutions have over the rest of the trade-off set, highlighting its potential as a decision-making tool, especially in cases where other preference information or domain knowledge is unavailable.