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

<strong>Data for Paper "Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness"</strong> <strong>instances.tar.xz</strong> contains 𝜌mnk-landscape instances <strong>metrics.csv</strong> contains the (C)PLOS-net metric-values <strong>performance.csv</strong> contains the performance of the different algorithms on each instance <strong>merged.csv</strong> contains the merged data from the 2 csv files above <strong>Reference</strong> Arnaud Liefooghe, Gabriela Ochoa, Sébastien Verel, and Bilel Derbel. 2023. <strong>Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness</strong>. 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 <strong>Abstract</strong> 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.