A multi-population multi-tasking variable neighborhood search algorithm with diversity enhancements for inter-domain path computation problem

Abstract

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.