This study presents a multi-objective optimization model for drone path planning that simultaneously considers path length, flight safety, and path diversity. The drone flight environment is first modeled, followed by the construction of an optimization model based on the path length and the drone flight-related threat level. An improved Particle Swarm Optimization (PSO) algorithm is then proposed, incorporating a Sugeno function for dynamically adjusting nonlinear inertia weights and learning factors, thereby enhancing the proposed model’s global search capability and drone path planning accuracy. The experimental results for two test scenarios (Maps A and B) demonstrate that the proposed algorithm identifies the optimal drone flight paths after 68 and 75 iterations, respectively, with average path lengths of 1.52 km and 1.65 km. These findings show that the enhanced PSO algorithm provides an effective and reliable solution for drone path planning, with broader implications for algorithmic optimization in related fields.
Drone path planning, Sugeno function, Multi-objective optimization, Particle swarm optimization algorithm.
Aihua ZHANG, "Improved Particle Swarm Optimization Algorithm for Multi-Objective Drone Path Planning", Studies in Informatics and Control, ISSN 1220-1766, vol. 35(1), pp. 87-98, 2026. https://doi.org/10.24846/v35i1y202608
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