MEMOBWO: A Novel Multi-Objective Optimization Algorithm for UAV Path Planning in Complex Urban Environments

Path planning for Unmanned Aerial Vehicles (UAV) in complex urban environments poses significant challenges for autonomous systems. This paper proposes a Multi-strategy Enhanced Multi-Objective Beluga Whale Optimization algorithm, termed MEMOBWO, to address these problems. The proposed MEMOBWO adopts a multi-objective optimization framework to overcome the limitations of traditional single-objective approaches while simultaneously enhancing exploration and exploitation capabilities through three complementary strategies. Firstly, a Chaotic Quasi-Opposition-Based Learning (CQOBL) strategy is introduced to enhance initial population diversity and quality. Secondly, a Hybrid Adaptive Position Update (HAPU) strategy is designed to dynamically balance global exploration and local exploitation. Finally, a Multi-Objective Thinking Innovation (MOTI) strategy is proposed as a targeted repair operator to overcome specific performance deficiencies of whale agents in weaker objectives. To evaluate its performance, the MEMOBWO was comprehensively tested through 20 standard multi-objective benchmark functions, as well as three-dimensional (3D) UAV path planning experiments in simulated urban environments with varying obstacle configurations, and was compared against a series of classical and recently proposed multi-objective optimization algorithms. Moreover, the overall performance of the algorithms was assessed using Hypervolume (HV) and Inverted Generational Distance (IGD) metrics and further tested using the Friedman test and Wilcoxon rank-sum test. Experimental results demonstrated that MEMOBWO achieved competitive performance across benchmark functions, and showed favorable overall performance against comparison algorithms in path planning tasks, attaining the lowest average Friedman rank as 1.14 and HV improvements of 15.24% to 30.86%. This study provides a promising optimization framework for multi-objective UAV path planning problems in urban environments, thereby lowering the tracking burden of downstream UAV flight-control and trajectory tracking.