Improved Multi-Objective Jellyfish Search Algorithm for UAV 3D Trajectory Planning
The problem of 3D trajectory planning for unmanned aerial vehicles(UAVs)was addressed in this article by proposing an Improved Multi-Objective Jellyfish Search(MOJS)algorithm.The study included several improvements to the classic MOJS algorithm,such as the SPM chaotic mapping-based jellyfish population initialization strategy,the convex lens imaging-based reverse learning strategy for enhancing population diversity,the Cauchy inverse cumulative distribution operator-based passive movement behavior optimization strategy,and the tangent flight operator-based active movement behavior optimization strategy.Simulation results demonstrated the superiority of the improved MOJS algorithm over the classic MOJS algorithm in various performance metrics,including total trajectory length,algorithm running time,and threat cost.Furthermore,the potential areas for future work were discussed,including algorithm robustness,real-time performance,high-dimensional optimization,integrated learning,and actual flight testing.
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