Data for GECCO2023 Paper "Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness"

Data for Paper "Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness"

instances.tar.xz contains 𝜌mnk-landscape instances

metrics.csv contains the (C)PLOS-net metric-values

performance.csv contains the performance of the different algorithms on each instance

merged.csv contains the merged data from the 2 csv files above

Reference

Arnaud Liefooghe, Gabriela Ochoa, Sébastien Verel, and Bilel Derbel. 2023. Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness. In Genetic and Evolutionary Computation Conference (GECCO ’23), July 15–19, 2023, Lisbon, Portugal. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3583131.3590474

Abstract

The structure of local optima in multi-objective combinatorial optimization and their impact on algorithm performance are not yet properly understood. In this paper, we are interested in the representation of multi-objective landscapes and their multi-modality. More specifically, we revise and extend the network of Pareto local optimal solutions (PLOS-net), inspired by the well-established local optima network from single-objective optimization. We first define a compressed PLOS-net which allows us to enhance its perception while preserving the important notion of connectedness between local optima. We then study an alternative visualization of the (compressed) PLOS-net that focuses on good-quality solutions, improves the distinction between connected components in the network, and generalizes well to landscapes with more than 2 objectives. We finally define a number of network metrics that characterize the PLOS-net, some of them being strongly correlated with search performance. We visualize and experiment with small-size multi-objective nk-landscapes, and we disclose the effect of PLOS-net metrics against well-established multi-objective local search and evolutionary algorithms.