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A multi-population multi-tasking variable neighborhood search algorithm with diversity enhancements for inter-domain path computation problem
Ist Teil von
Swarm and evolutionary computation, 2024-04, Vol.86, Article 101501
Ort / Verlag
Elsevier B.V
Erscheinungsjahr
2024
Link zum Volltext
Quelle
Elsevier ScienceDirect Journals Complete
Beschreibungen/Notizen
In recent years, as networks have become increasingly complex and expansive, network navigation faces new challenges in routing and resource utilization, particularly in the case of multi-domain networks. The Inter-Domain Path Computation under Node-defined Domain Uniqueness Constraint (IDPC-NDU) problem is one of many such scenarios. The problem requires finding the shortest path in a multi-domain network, but it cannot visit any domain twice. Since this is a proven NP-Hard problem, it is reasonable to approach IDPC-NDU from a meta-heuristics perspective. By integrating the search prowess of Variable Neighborhood Search (VNS) with the meta-learning capabilities of Multi-population-based Multitasking (MM), this paper proposes a Multi-population multi-tasking VNS (MM-VNS) algorithm to solve the Inter-Domain Path Computation under Domain Uniqueness constraint (IDPC-DU). In this combination, VNS is used to exploit solution space while meta-learning maintains diversity by transferring knowledge. Numerous experiments performed indicate the proposed approach finds better solutions in comparison with the state-of-the-art metaheuristics in many cases.