Secure Flight Robotic Aircraft in Multi Constraints Landscape Using Parallel Hippo Swarm Optimization

Abstract

     Considering the rapid development of robotic aircraft (RA) in many applications, secure path planning in complex environments is a key field of study. This study converts path-planning into a multi-constraint optimization problem including terrain obstacles, radar emissions, path length, and fuel consumption. An improved Hippo swarm optimization (IHSO) is proposed to enhance the traditional HSO algorithm. The proposed IHSO enhances initial population diversity by using cubic chaos mapping and creates a parallel foraging environment to accelerate the algorithm's performance. The path planning information is encrypted using key policy attribute based encryption to provide a secure path planning model. The proposed algorithm was verified in urban and mountain environments and compared with four state-of-the-art algorithms: IPOA, MDBO, SLTSO, and HISOS-SCPSO. The IHSO successfully identified optimal paths with a mean of 84.916 and a standard deviation (SD) of 0.735 in an urban environment, consuming 0.716 s. Whereas, in the mountains, the consumption time was 14.984 s, with a mean of 109.314 s and an SD of 1.986. The experiments proved the superiority of the IHSO algorithm in terms of accuracy, running time, energy consumption, and stability.