<![CDATA[Efficient urban waste management is a critical challenge driven by rapid urbanization, with collection routes strongly influencing operational costs and environmental sustainability. This study addresses the optimization of waste collection routes by modeling the problem as a Travelling Salesman Problem (TSP), serving as a foundational step toward more complex routing frameworks. We propose a Lévy-flight-enhanced Grey Wolf Optimizer (LGWO), which extends the standard Grey Wolf Optimizer (GWO) by integrating a lévy flight mechanism designed to strengthen global exploration and mitigate premature convergence to local optima. The performance of LGWO is evaluated against six other metaheuristic algorithms (GWO, ACOR, WOA, PSO, ALO, and ABC) using a real-world dataset of 36 waste collection points in Bandung, Indonesia. Experimental results based on 30 independent trials per algorithm show that LGWO achieves the best overall performance, obtaining the shortest tour (60.85 km) and the lowest mean distance (77.72 km), whereas the Ant Lion Optimizer (ALO) yields the poorest performance with the highest average distance of 89.90 km. These findings indicate that incorporating a lévy flight mechanism into GWO improves solution quality and convergence behavior for TSP-based waste collection routing. This research offers a practical optimization tool for developing more efficient and cost-effective urban waste management strategies. Future work will extend this approach by incorporating dynamic factors such as service times and vehicle capacities, enabling a more realistic treatment of Vehicle Routing Problem (VRP) variants. ]]>
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