Hybrid metaheuristic algorithms for sum coloring and bandwidth coloring

Abstract : The minimum sum coloring problem (MSCP) and the bandwidth coloring problem (BCP) are two important generalizations of the classical vertex coloring problem with numerous applications in diverse domains, including VLSI design, scheduling, resource allocation and frequency assignment in mobile networks, etc. Since the MSCP and BCP are NP-hard problems, heuristics and metaheuristics are practical solution methods to obtain high quality solutions in an acceptable computing time. This thesis is dedicated to developing effective hybrid metaheuristic algorithms for the MSCP and BCP. For the MSCP, we present two memetic algorithms which combine population-based evolutionary search and local search. An effective algorithm for maximum independent set is devised for generating initial solutions. For the BCP, we propose a learning-based hybrid search algorithm which follows a cooperative framework between an informed construction procedure and a local search heuristic. The proposed algorithms are evaluated on well-known benchmark instances and show highly competitive performances compared to the current state-of-the-art algorithms from the literature. Furthermore, the key issues of these algorithms are investigated and analyzed.
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Yan Jin. Hybrid metaheuristic algorithms for sum coloring and bandwidth coloring. Computation and Language [cs.CL]. Université d'Angers, 2015. English. ⟨NNT : 2015ANGE0062⟩. ⟨tel-02164604⟩

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