Sequence-based Selection Hyper-heuristics with a Prolog Knowledge-based System for Real-World Network Design Optimisation

• Prolog knowledge-based system enhances network domain flexibility. • Sequence-based hyper-heuristics escape local optima effectively. • Method scales well and yields cost-effective solutions for fibre rollouts. • Hybrid framework improves modularity and usability, while eases automation. As demand for high-speed telecommunication increases, automating the design of fibre networks is critical for minimising deployment costs and ensuring consistent service. However, existing search strategies within British Telecom’s (BT) design software struggle to consistently locate near-global optima as network scale increases, and the hard-coded definition of network domains limits flexibility for non-developers. To address these challenges, this paper proposes a novel framework with two key contributions: a Prolog Advisory System (PAS) for modular domain definition and a sequence-based selection hyper-heuristic for enhanced optimisation. The PAS utilises logic programming to decouple constraint checking and cost calculation from the core framework, enabling rapid domain modification. For optimisation, we introduce intelligent move sequencing using Luby-based and Hidden Markov Model approaches, augmented by a continuous pivot strategy that increases the exploration of constraint-breaking moves. Experimental results on real-world case studies illustrate the robustness and effectiveness of our method to obtain high-quality solutions, regardless of the size of the network. Specifically, on large-scale instances, the proposed method outperforms BT’s current approach, reducing average design cost by approximately 3.8% and decreasing the required evaluations by up to 65%. This work offers vital efficiency gains for BT’s multi-billion pound fibre roll-out program, one of the UK’s largest infrastructure investments, where even small design improvements yield substantial financial savings.