Decomposition-Based Multi-objective Landscape Features and Automated Algorithm Selection

• Published in: Evolutionary Computation in Combinatorial Optimization Vol. 12692; pp. 34 - 50
• Main Authors: Cosson, Raphaël, Derbel, Bilel, Liefooghe, Arnaud, Aguirre, Hernán, Tanaka, Kiyoshi, Zhang, Qingfu
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 3030729036; 9783030729035
• ISSN: 0302-9743; 1611-3349; 1611-3349
• DOI: 10.1007/978-3-030-72904-2_3

Landscape analysis is of fundamental interest for improving our understanding on the behavior of evolutionary search, and for developing general-purpose automated solvers based on techniques from statistics and machine learning. In this paper, we push a step towards the development of a landscape-aware approach by proposing a set of landscape features for multi-objective combinatorial optimization, by decomposing the original multi-objective problem into a set of single-objective sub-problems. Based on a comprehensive set of bi-objective and three variants of the state-of-the-art Moea/d algorithm, we study the association between the proposed features, the global properties of the considered landscapes, and algorithm performance. We also show that decomposition-based features can be integrated into an automated approach for predicting algorithm performance and selecting the most accurate one on blind instances. In particular, our study reveals that such a landscape-aware approach is substantially better than the single best solver computed over the three considered Moea/d variants.