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Complex networks analysis of the energy landscape of the low autocorrelation binary sequences problem

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  • Tomassini, Marco

Abstract

We provide an up-to-date view of the structure of the energy landscape of the low autocorrelation binary sequences problem, a typical representative of the NP-hard class. To study the landscape features of interest we use the local optima network methodology through exhaustive extraction of the optima graphs for problem sizes up to 24. Several metrics are used to characterize the networks: number and type of optima, optima basins structure, degree and strength distributions, shortest paths to the global optima, and random walk-based centrality of optima. Taken together, these metrics provide a quantitative and coherent explanation for the difficulty of the low autocorrelation binary sequences problem and provide information that could be exploited by optimization heuristics for this problem, as well as for a number of other problems having a similar configuration space structure.

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  • Tomassini, Marco, 2021. "Complex networks analysis of the energy landscape of the low autocorrelation binary sequences problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 577(C).
    • Handle: RePEc:eee:phsmap:v:577:y:2021:i:c:s0378437121003629
    DOI: 10.1016/j.physa.2021.126089
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    References listed on IDEAS

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    1. Sebastian Herrmann & Gabriela Ochoa & Franz Rothlauf, 2018. "PageRank centrality for performance prediction: the impact of the local optima network model," Journal of Heuristics, Springer, vol. 24(3), pages 243-264, June.
    2. Daolio, Fabio & Tomassini, Marco & Vérel, Sébastien & Ochoa, Gabriela, 2011. "Communities of minima in local optima networks of combinatorial spaces," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(9), pages 1684-1694.
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