Instance Space Analysis of Combinatorial Multi-objective Optimization Problems

In recent years, there has been a continuous stream of development in evolutionary multi-objective optimization (EMO) algorithms. The large quantity of existing algorithms introduces difficulty in selecting suitable algorithms for a given problem instance. In this paper, we perform instance space an...

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Bibliographic Details
Published in:2020 IEEE Congress on Evolutionary Computation (CEC) pp. 1 - 8
Main Authors: Yap, Estefania, Munoz, Mario A., Smith-Miles, Kate, Liefooghe, Arnaud
Format: Conference Proceeding
Language:English
Japanese
Published: IEEE 01.07.2020
Subjects:
black-box combinatorial optimization
Clustering algorithms
Correlation
Decision trees
Feature extraction
feature-based performance prediction
landscape analysis
Multi-objective optimization
Optimization
Prediction algorithms
Visualization
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Summary:In recent years, there has been a continuous stream of development in evolutionary multi-objective optimization (EMO) algorithms. The large quantity of existing algorithms introduces difficulty in selecting suitable algorithms for a given problem instance. In this paper, we perform instance space analysis on discrete multi-objective optimization problems (MOPs) for the first time under three different conditions. We create visualizations of the relationship between problem instances and algorithm performance for instance features previously identified using decision trees, as well an independent feature selection. The suitability of these features in discriminating between algorithm performance and understanding strengths and weaknesses is investigated. Furthermore, we explore the impact of various definitions of "good" performance. The visualization of the instance space provides an alternative method of algorithm discrimination by showing clusters of instances where algorithms perform well across the instance space. We validate the suitability of existing features and identify opportunities for future development.
DOI:10.1109/CEC48606.2020.9185664