An Overview of Evolutionary Algorithms in Multiobjective Optimization

TLDR

EAs are increasingly applied to multiobjective optimization, yet most research concentrates on the selection stage to reconcile vectorial objectives with the scalar fitness reward of offspring. The review proposes future research directions in multiobjective fitness assignment and search strategies, including integrating decision making into selection, fitness sharing, and adaptive representations. It surveys multiobjective EA approaches—from objective aggregation to population‑based and Pareto‑ranking methods—and examines how objective scaling and trade‑off surface concavities influence the induced fitness landscapes.

Abstract

The application of evolutionary algorithms (EAs) in multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage of EAs, due to the need to integrate vectorial performance measures with the inherently scalar way in which EAs reward individual performance, that is, number of offspring. In this review, current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality. The sensitivity of different methods to objective scaling and/or possible concavities in the trade-off surface is considered, and related to the (static) fitness landscapes such methods induce on the search space. From the discussion, directions for future research in multiobjective fitness assignment and search strategies are identified, including the incorporation of decision making in the selection procedure, fitness sharing, and adaptive representations.