Enhanced optimisation of MPLS network traffic using a novel adjustable Bat algorithm with loudness optimizer

The increasing dependency on network connectivity necessitates superior network performance, characterized by continuous availability and high reliability. Multiprotocol Label Switching (MPLS) networks have emerged as a robust solution to the limitations of traditional IP networks. However, determining optimal Label Switched Paths (LSPs) across multiple domains becomes a complex optimisation problem when considering multiple objectives. This paper addresses this challenge by transforming the path computation into an optimisation task. The Bat Algorithm, a popular meta-heuristic approach, is frequently employed for solving such optimisation problems. Despite its effectiveness, the Bat Algorithm is prone to premature convergence and the local optima problem, leading to sub-optimal solutions. This study explores the influence of the algorithm's loudness parameter and introduces a novel Adjustable Bat Algorithm (ABAT) that dynamically optimizes this parameter. The ABAT is integrated with a Pareto-based approach to enhance the discovery of non-dominant solutions. The proposed method effectively computes optimal paths while identifying the Pareto front of solutions, providing a balanced trade-off among competing objectives. The performance of the proposed algorithm was evaluated on networks of varying scales and compared against the standard Bat Algorithm and other meta-heuristic methods. The evaluation focused on convergence rate and computational complexity, demonstrating that the proposed Adjustable Bat Algorithm outperforms existing methods in both metrics, offering a more robust and efficient solution for multi-objective path optimisation in MPLS networks. Experimental results indicate that the proposed Adjustable Bat Algorithm (ABAT) outperformed the regular Bat Algorithm in terms of convergence rate by up to 35% and reduced load balancing expenses and routing latency by 15% to 20% at various MPLS network scales.