The Sand Cat Swarm Optimization Algorithm (SCSO) is a new meta-heuristic optimization algorithm inspired by the search and attack behavior of sand cats in nature. To address the shortcomings of the standard sand cat swarm optimization algorithm, such as its tendency to fall into local optima and slow convergence during the optimization process, and to enhance its performance in solving complex problems, this article proposes an adaptive selection dual-population sand cat swarm algorithm with modification strategies(MDPSCSO). First, the HaltonRR2 initialization method is employed in the initial phase to generate a higher-quality and more uniform initial population. Modification strategies are subsequently applied to adjust the best and worst solutions within the population under different fitness landscape conditions, enabling the algorithm to avoid local optima and increase solution diversity during the optimization process. Finally, a dual-population collaboration mechanism is introduced, along with two selection methods, to guide the population toward the correct direction of exploration and accelerate the convergence speed of the population. The combination of these strategies is used to enhance the algorithm’s optimization performance in solving complex problems, and these modifications have been successfully applied to UAV path planning scenarios in complex environments. To validate the proposed algorithm, tests were conducted on the CEC2017 benchmark suite, and simulation experiments were performed in eight 3D UAV path planning scenarios with varying complexities to evaluate the performance of MDPSCSO. The experimental results demonstrate that MDPSCSO outperforms other advanced algorithms in terms of search efficiency, solution accuracy, and convergence speed in solving global optimization problems and that it can effectively generate higher-quality paths for UAVs in complex 3D environments, showing significant potential for practical applications.
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