Path Planning for Uavs with Chaotic Difference Electric Eel Foraging Optimization

Evolutionary algorithms, such as the Electric Eel Foraging Optimization (EEFO) algorithm, are widely used for UAV path planning. However, traditional EEFO may struggle with complex terrains due to its tendency to converge to local optima. This paper proposes the Chaotic Difference Electric Eel Foraging Optimization (CDEEFO) scheme, which integrates cubic chaotic maps and the differential evolution algorithm to enhance exploration and search efficiency. The cubic chaotic maps improve the uniformity of the initial solution distribution, while the differential evolution algorithm boosts robustness and search capability. Comparative experiments in simple and complex scenarios demonstrate the superior performance of CDEEFO, particularly in terms of solution quality and adaptability. The proposed method significantly enhances the performance of UAV path planning in complex environments.