低自相关二元序列能量景观问题的复杂网络分析
Complex Networks Analysis of the Energy Landscape of the Low Autocorrelation Binary Sequences Problem
摘要：
我们提供了一个最新的视图的结构的能量景观的低自相关二进制序列的问题，一个典型的代表的$NP$硬类。为了研究感兴趣的景观特征，我们使用局部最优网络方法，通过穷举提取最优图的问题大小达到$24$。几个指标被用来表征网络：最优的数量和类型，最优流域结构，度和强度分布，最短路径的全局最优，和随机行走为基础的最优中心。总之，这些指标提供了一个定量和连贯的解释的困难，低自相关二进制序列的问题，并提供信息，可以利用优化算法为这个问题，以及其他一些问题具有类似的配置空间结构。
英文摘要：
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, shortests 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
