Data for GECCO2023 Paper "Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness" (Q6683174)

Data for Paper Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness instances.tar.xzcontains 𝜌mnk-landscape instances metrics.csvcontains the (C)PLOS-net metric-values performance.csvcontains the performance of the different algorithms oneach instance merged.csvcontains the merged data from the 2 csv files above Reference Arnaud Liefooghe, Gabriela Ochoa, Sbastien 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 1519, 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.