Indicator-based Multi-objective Evolutionary Algorithms: A Comprehensive Survey

• Published in: ACM computing surveys 2021-03, Vol.53 (2), p.1-35, Article 29
• Main Authors: Falcón-Cardona, Jesús Guillermo, Coello, Carlos A. Coello
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Bio-inspired optimization; Computer science; Computing methodologies; Continuous optimization; Continuous space search; Design and analysis of algorithms; Evolutionary algorithms; Genetic algorithms; Mathematical optimization; Mopping; Multiple objective analysis; Optimization; Pareto optimization; Search methodologies; Taxonomy; Theory of computation
• ISSN: 0360-0300; 1557-7341
• DOI: 10.1145/3376916

For over 25 years, most multi-objective evolutionary algorithms (MOEAs) have adopted selection criteria based on Pareto dominance. However, the performance of Pareto-based MOEAs quickly degrades when solving multi-objective optimization problems (MOPs) having four or more objective functions (the so-called many-objective optimization problems), mainly because of the loss of selection pressure. Consequently, in recent years, MOEAs have been coupled with indicator-based selection mechanisms in furtherance of increasing the selection pressure so that they can properly solve many-objective optimization problems. Several research efforts have been conducted since 2003 regarding the design of the so-called indicator-based (IB) MOEAs. In this article, we present a comprehensive survey of IB-MOEAs for continuous search spaces since their origins up to the current state-of-the-art approaches. We propose a taxonomy that classifies IB-mechanisms into two main categories: (1) IB-Selection (which is divided into IB-Environmental Selection, IB-Density Estimation, and IB-Archiving) and (2) IB-Mating Selection. Each of these classes is discussed in detail in this article, emphasizing the advantages and drawbacks of the selection mechanisms. In the final part, we provide some possible paths for future research.