A compact ant lion optimizer for three-dimensional path planning

Current 3D path planning algorithms of missile only use distance as objective function, and have not consider the requirements of communication connectivity and concealment, which can not get better tactical effect. Metaheuristic algorithms usually need a large amount of calculation.That is hard to be applied in resource-limited devices. In this paper, an improved ant lion optimizer based on compact strategy is proposed to solve 3D path planning problem. Distance, communication connectivity and concealment are taken into account to optimize the problem. In our method, the virtual population is obtained by truncated distribution model in place of the actual population, so as to optimize the steps of the algorithm and reduce the amount of calculation. MATLAB software is used to evaluate the performance of compact ant lion optimizer(cALO), comparing with Wolf pack algorithm(WPA), Particle swarm optimization(PSO), Ant colony optimization(ACO) and Ant lion optimizer(ALO). The results show that the cALO can greatly improve the comprehensive effect, using less memory and getting higher convergence speed, which is very suitable for missile missions.