用优化规划提高最大顶点权团问题的随机局部搜索性能
Improving the Performance of Stochastic Local Search for Maximum Vertex Weight Clique Problem Using Programming by Optimization
摘要：
最大顶点权团问题（MVWCP）是最大团问题（MCP）的一个重要推广，有着广泛的实际应用。在不需要关于解的最优性的严格保证的情况下，MVWCP通常使用随机局部搜索（SLS）算法来求解，这也定义了解决该问题的最新技术。然而，没有一种SLS算法可以在所有类别的MVWCP实例中提供最佳性能，并且有效地识别最适合每类MVWCP实例的算法是具有挑战性的。在这项工作中，我们遵循的范式编程优化（PbO）开发一个新的，灵活的和高度参数化的SLS框架解决MVWCP，结合，第一次，广泛的有效的启发式机制。通过自动配置这个PbO-MWC框架，我们在解决MVWCP方面取得了巨大的进步，包括两个来自移植医学（肾脏交换）和研究卓越评估的实际应用。
英文摘要：
The maximum vertex weight clique problem (MVWCP) is an important generalization of the maximum clique problem (MCP) that has a wide range of real-world applications. In situations where rigorous guarantees regarding the optimality of solutions are not required, MVWCP is usually solved using stochastic local search (SLS) algorithms, which also define the state of the art for solving this problem. However, there is no single SLS algorithm which gives the best performance across all classes of MVWCP instances, and it is challenging to effectively identify the most suitable algorithm for each class of MVWCP instances. In this work, we follow the paradigm of Programming by Optimization (PbO) to develop a new, flexible and highly parametric SLS framework for solving MVWCP, combining, for the first time, a broad range of effective heuristic mechanisms. By automatically configuring this PbO-MWC framework, we achieve substantial advances in the state-of-the-art in solving MVWCP over a broad range of prominent benchmarks, including two derived from real-world applications in transplantation medicine (kidney exchange) and assessment of research excellence.
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