Abstract
Optimizing the quality of service in a multicast routing is a persistent research problem for data transmission in computer networks. It is known to be an NP-hard problem, so several meta-heuristics are applied for an approximate resolution. In this paper, we resolve the quality of service multicast routing problem (QoSMRP) with using a combined approach that uses a newly meta-heuristic called Dragonfly Algorithm (DFA) and Quantum Evolutionary Algorithm (QEA), we adopted a quantum representation of the solutions by a vector of continuous real values which allowed us to use the continuous version of the DFA without discretization, we also use the equation of DFA to calculate $$\varDelta \theta $$ in QEA. The interest of these contributions is to avoid premature convergence, to improve the diversity of solutions, and to increase the efficiency and performance of the proposed algorithm. The experimental results show the feasibility, scalability, and effectiveness of our proposed approach compared to other algorithms such as Genetic Algorithm (GA), Quantum Evolutionary Algorithm (QEA), and Dragonfly Algorithm (DFA).
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