Enhancement of Harris Hawks optimization applied in path planning for an indoor navigation mobile application

Abstract

The research focuses on the enhancement of Harris Hawks Optimization (HHO) algorithm in achieving an efficient path planning, specifically in indoor environments. It solves problems encountered in HHO; firstly, the exploration operator of HHO is modified to incorporate the survival of the fittest principle, this ensures a controlled diversity by using an Exponential Ranking selection method. The method aims to guide the algorithm to find a more optimal solution while allowing it to search for alternative paths. Secondly, Linear Path Strategy is used to reduce the number of nodes in the paths, therefore minimizing its length and simplifying trajectories. In addition, Linear Path Strategy aims to create smoother paths and avoid obstacles, improving the overall performance of the algorithm. Lastly, general multi-objective formula is defined to evaluate path length and travel time. Considering these factors established a balanced evaluation metric, which provided a detailed assessment of path quality for scenarios like indoor navigation. Comparative analysis was done, and results highlighted the effectiveness of EHHO in generating better paths, in comparison with Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO), and with the current HHO algorithm. It outperformed the algorithms compared in terms of path length, travel time, and efficiency of the execution, especially in a complex environment. In conclusion, EHHO algorithm showed promising results in providing shorter, faster, and more efficient routes, with potential applications across different domains that requires optimal path planning solutions.