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The maximum vertex weight clique problem (MVWCP) is an importantgeneralization of the maximum clique problem (MCP) that has a wide range ofreal-world applications. In situations where rigorous guarantees regarding theoptimality of solutions are not required, MVWCP is usually solved usingstochastic local search (SLS) algorithms, which also define the state of theart for solving this problem. However, there is no single SLS algorithm whichgives the best performance across all classes of MVWCP instances, and it ischallenging to effectively identify the most suitable algorithm for each classof MVWCP instances. In this work, we follow the paradigm of Programming byOptimization (PbO) to develop a new, flexible and highly parametric SLSframework for solving MVWCP, combining, for the first time, a broad range ofeffective heuristic mechanisms. By automatically configuring this PbO-MWCframework, we achieve substantial advances in the state-of-the-art in solvingMVWCP over a broad range of prominent benchmarks, including two derived fromreal-world applications in transplantation medicine (kidney exchange) andassessment of research excellence.

Improving the Performance of Stochastic Local Search for Maximum Vertex Weight Clique Problem Using Programming by Optimization