Improving the Performance of Stochastic Local Search for Maximum Vertex Weight Clique Problem Using Programming by Optimization
This work addresses the problem of algorithm selection for combinatorial optimization in domains like healthcare and academia, but it is incremental as it builds on existing SLS methods with a new configuration approach.
The authors tackled the challenge of selecting the best stochastic local search algorithm for different classes of the maximum vertex weight clique problem by developing a flexible, parametric framework using Programming by Optimization, achieving substantial advances in state-of-the-art performance across various benchmarks, including real-world applications in kidney exchange and research assessment.
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.