A Hybrid K-Means and Particle Swarm Optimization Technique for Solving the Rechargeable E-Scooters Problem

A Hybrid K-Means and Particle Swarm Optimization Technique for Solving the Rechargeable E-Scooters Problem

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E-scooters are gaining popularity for short-distance travel, but their recharging presents challenges.To reduce their downtime, we propose a Hybrid K-Means/Particle Swarm Optimisation (PSO) approach, optimizing charging routes using machine learning and simply southern cat shirt meta-heuristics.The research in this paper attempts to determine if a combination of a meta-heuristic such as PSO and a machine learning algorithm for clustering such as K-Means, would be effective at solving the vehicle routing problem for e-scooters.We compared this method with other algorithms and found that Tabu Search excelled in over 95% of tests.While Hybrid K-Means/PSO led in only approximately 52% of scenarios, it was also the only one to provide an output that surpassed Tabu Search in one of the scenarios.

The core difference in efficiency is due to traditional meta-heuristic methods providing routes that while optimal, may also travel from locations relatively far from each other, while Hybrid K-Means/PSO will provide routes between locations that are clustered and in local groups.This results in Hybrid K-Means/PSO being slightly less efficient but may be more practical for charging personnel as they can operate in designated areas close to each other rather silver lining herbs kidney support than a more optimal route with nodes further apart.This research underscores the effectiveness of Tabu Search and the potential of our Hybrid K-Means/PSO approach for optimizing e-scooter charging routes.