Path planning for a swarm of drones is primarily concerned with avoiding collision among the drones and environmental obstacles while determining the most efficient flight path to the region of interest. This paper proposes an efficient methodology for drone swarm path planning problems in 3D environments. An improved population based meta-heuristic algorithm, Sine Cosine Algorithm (SCA), has been proposed to solve this problem. As part of the improvements, the population of SCA is initialized using a chaotic map, and a non-linearly decreasing step size is used to balance the local and global search. In addition, a convergence factor is employed to increase the convergence rate of the original SCA. The performance of the proposed improved SCA (iSCA) is tested over the drone swarm path planning problem, and the results are compared with those of the original SCA, and other state-of-the-art meta-heuristic algorithms. The experimental results show that the drone swarm 3D path planning problem can be efficiently handled with the proposed improved SCA