Multi-strategy Hybrid Improved Intelligent Algorithm for Solving UAV-MTSP

Abstract

Unmanned aerial vehicles (UAVs) have been increasingly used in fire monitoring and rescue operations, offering flexibility and efficiency. However, determining the shortest path for all UAVs to visit all regions is a crucial issue, known as the Multiple Traveling Salesman Problem (MTSP), which aims to save time and energy. This paper proposes a novel hybrid heuristic algorithm, MCPWOA, to solve MTSP with a focus on UAV path planning applications. The algorithm integrates the Whale Optimization Algorithm (WOA), Crested Porcupine Optimizer (CPO), Chaotic Mapping Strategy (CMS), Arcsine Control Strategy (ACS) and Reverse Learning Strategy (RLS) to diversify the initial population and achieve rapid exploration. The algorithm's performance is evaluated using the CEC2022 benchmark function set and TSPLIB dataset for function minimization and UAV-MTSP experimental solution finding. Results indicate that MCPWOA outperforms existing WOA, CPO, and other advanced algorithms on most tests, showing higher convergence accuracy. Moreover, MCPWOA's effectiveness is demonstrated in actual UAV fire monitoring and rescue path planning, enhancing fire response efficiency through optimized UAV configuration and task allocation.