Abstract: Aiming at the shortcomings of the salp swarm algorithm, such as low search accuracy, slow convergence speed, and poor stability of optimization, an adaptive inertia weight salp swarm algorithm based on chaotic mapping is proposed. First, Tent chaotic mapping populations are used in the initialization phase to make the search space more uniformly distributed. Then, logistic chaos is added to the leader position, while adaptive inertia weights are introduced to the follower positions, thus enhancing the diversity of the population. Finally, the food source is operated by Gaussian variation, which makes the algorithm jump out of the local optimum and improves the search accuracy. The improved salp swarm algorithm is evaluated through convergence curve analysis, function test results comparison, and algorithm ranking evaluation. The results show that the adaptive inertial weights salp swarm algorithm based on chaotic mapping has higher search accuracy, faster convergence, better optimization ability, and higher stability. In simulation experiments on planning optimal paths for cruising in complex mountainous areas, the improved algorithm outperforms thesalp swarm algorithm in terms of planning quality, path length, and solution stability, indicating its superior applicability for unmanned aerial vehicles path planning in mountainous environments.

Key words: salp swarm algorithm, chaotic map, adaptive inertia weights, path planning, UAV