Exponential-trigonometric Optimization Algorithm with Multi-Strategy Fusion for UAV three-dimensional path planning

With the rapid advancement of Unmanned Aerial Vehicle (UAV) technology, trajectory planning has become a focus research. This paper proposes a three-dimensional path planning method for UAV based on an improved Exponential-triangle Optimization Algorithm (IETO). By constructing a multi-objective optimization function that considers factors such as path length, flight altitude, and turning angle, a comprehensive evaluation of path quality is able to be achieved. The IETO algorithm incorporates interval-constrained logistic chaotic mapping, dynamic reverse learning strategy, and an adaptive artificial bee colony algorithm (ABC) escape mechanism within the ETO algorithm. These enhancements prevent premature convergence to local optima. Through benchmark function tests on the CEC2017 test set and simulations in peak threat environments, the IETO algorithm demonstrated superior robustness. Compared to mainstream algorithms like GWO and GJO, IETO achieves the best performance in 62% of function tests. It also demonstrates exceptional performance in solving complex functions, effectively balances exploration and exploitation capabilities. In mountainous environments, the IETO algorithm generates the smoothest paths with the lowest costs and quickly converges to the optimal solution.