Multi-objective evolutionary algorithms in real-world applications: Some recent results and current challenges

Abstract

This chapter provides a short overview of the most significant research work that has been conducted regarding the solution of computationally expensive multi-objective optimization problems. The approaches that are briefly discussed include problem approximation, function approximation (i.e., surrogates) and evolutionary approximation (i.e., clustering and fitness inheritance). Additionally, the use of alternative approaches such as cultural algorithms, small population sizes and hybrids that use a few solutions (generated with optimizers that sacrifice diversity for the sake of a faster convergence) to reconstruct the Pareto front with powerful local search engines are also briefly discussed. In the final part of the chapter, some topics that (from the author's perspective) deserve more research, are provided.