Titlebook: Construct, Merge, Solve & Adapt; A Hybrid Metaheurist Christian Blum Book 2024 The Editor(s) (if applicable) and The Author(s), under exclu

终点

neolith

Additional Research Lines Concerning CMSA,d instead of an integer linear programming solver for sub-instance solving. Following an examination that elucidates the relationship between large neighborhood search and CMSA, the chapter wraps up by underscoring promising avenues for future research.

注意

https://doi.org/10.1007/978-1-349-05806-8 already used for the illustration of other CMSA variants in previous chapters. In particular, applications to the Minimum Dominating Set (MDS) problem and the Far From Most String (FFMS) problem are presented.

残忍

Hemiplegia

Properties and Mechanics of Solids,ssible to simply eliminate certain values from these domains. In this chapter, we present an illustration of CMSA applied to a combinatorial optimization problem naturally formulated as a non-binary ILP. Specifically, we make use of the Bounded Knapsack Problem with Conflicts.

混合物

Adding Learning to CMSA, already used for the illustration of other CMSA variants in previous chapters. In particular, applications to the Minimum Dominating Set (MDS) problem and the Far From Most String (FFMS) problem are presented.

闹剧

Replacing Hard Mathematical Models with Set Covering Formulations,e Windows and Simultaneous Pickups and Deliveries (EVRP-TW-SPD). In both applications, CMSA based on a set covering model significantly outperforms CMSA when using an assignment-type model. Moreover, state-of-the-art results are obtained for both considered optimization problems.

涂掉

floodgate

震惊

Book 2024ctory chapter about standard CMSA, subsequent chapters cover a self-adaptive CMSA variant as well as a variant equipped with a learning component for improving the quality of the generated solutions over time. Furthermore, on outlining the advantages of using set-covering-based integer linear progra