Advanced Route Optimization using Hybrid Algorithms and Road-based Distance Calculation

Abstract

This study proposes a hybrid approach to route optimization, comparing and combining Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Dynamic Programming (DP) to solve the Traveling Salesman Problem (TSP) and similar routing challenges. The objective is to minimize travel time and cost by incorporating real-time road data from OpenRouteService and Google Maps APIs. The hybrid algorithms are tested on large datasets, demonstrating their scalability and adaptability to real-world complexities such as fluctuating traffic conditions. Through mathematical modelling and pseudocode, the performance of each algorithm is compared, highlighting their effectiveness in optimizing logistics operations. The results indicate that this approach significantly reduces computational time and operational costs, providing a robust solution for modern logistics.