A Multi-Strategy Fusion Improved Kepler Optimization Algorithm: Comprehensive Performance Evaluation and Three-Dimensional UAV Trajectory Path Planning under Multiple Constraints

As a metaheuristic algorithm, the Kepler Optimization Algorithm (KOA) has exhibited remarkable performance in different test sets and optimization tasks. However, KOA has some issues of slow convergence speed, low convergence accuracy, and imbalanced exploration and exploitation. To address these challenges, this paper proposes a multi-strategy improved Kepler Optimization Algorithm called Sophisticated Policies-based Kepler Optimization Algorithm (SPKOA). Initially, Latin Hypercube Sampling (LHS) is used to initialize the population, which boosts its diversity. Next, a dynamic orbital perturbation-improved Levy flight mechanism is proposed to broaden the global search space, dynamically balancing exploration and exploitation. Furthermore, a multi-elite gravitational mechanism is proposed, which uses multiple high-quality solutions to guide the current solution towards higher-quality directions. A local fine-tuning mechanism is also introduced to enhance local search capability in the solution space. Finally, an improved elite crossover recombination mechanism is applied to prevent the loss of elite individuals and accelerate convergence. Comparative experiments with various high-performance algorithms across the CEC2017, CEC2020, and CEC2022 test sets demonstrate the competitive optimization performance of SPKOA. In addition, simulation experiments for unmanned aerial vehicle (UAV) three-dimensional path planning are conducted in complex environments including terrain obstacles, no-fly zones, and dynamic obstacles, where SPKOA achieves the shortest path length among all seven compared algorithms while maintaining zero constraint violations and zero infeasible runs across all 30 independent trials, demonstrating its strong applicability to complex constrained optimization problems.