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A Multi-Objective Optimization Framework Combining NSGA-II and MOPSO for UAV Path Planning
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DOI：10.1007/s12293-026-00521-6.png)
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Abstract
En 中文
This study addresses the path planning challenges for multiple UAVs engaged in cooperative operations and multi-task point coverage by introducing a multi-objective optimization model, referred to as the UAV path planning model (UAV-PPM). The model’s objective is to minimize path length, suppress altitude fluctuations, enhance trajectory smoothness, and reduce flight risk in complex environments. Traditional optimization methods often exhibit insufficient convergence and limited solution diversity under high-dimensional, multi-objective, and dynamic conditions. To overcome these limitations, we have developed a multi-objective intelligent optimization algorithm, the wise wayfinding algorithm (WWA), which integrates mechanisms from non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO). From a memetic computing perspective, WWA incorporates three cooperative strategies: (1) the oscillation diversity strategy, which employs difference operators and Lévy flights to facilitate dynamic propagation and local refinement; (2) the adaptive compensation strategy, which fuses multi-source information with weight adjustment to enhance individual learning; and (3) the jet convergence strategy, which balances local convergence with global exploration while incorporating non-elite information to maintain population diversity. Experimental results demonstrate that WWA exhibits favorable convergence and robust solution distribution on standard benchmark functions (ZDT, DTLZ, UF). When applied to the UAV-PPM problem, WWA outperforms NSGA-II, MOPSO, NSGA-III, and multi-objective rime-ice (MORIME), achieving improvements of 22.96%, 25.74%, 9.28%, and 57.12% in IGD and 80.21%, 41.45%, 29.87%, and 117.96% in HV, respectively.
Keywords:
UAV path planning model
Multi-objective optimization
Meta-heuristic algorithm
Cooperative scheduling
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