Title: Comparison of bald eagle search algorithm with benchmark meta-heuristic algorithms (PSO, ABC and GWO) applied to robot path planning

Authors: Ganapati Girish Kamat; S. Yogeswarr; Narahari; Vineeth Parashivamurthy

Addresses: Department of Mechanical Engineering, B.M.S. College of Engineering, Bangalore, 560019, India ' Department of Medical Electronics, B.M.S. College of Engineering, Bangalore, 560019, India ' Department of Mechanical Engineering, B.M.S. College of Engineering, Bangalore, 560019, India ' Department of Mechanical Engineering, B.M.S. College of Engineering, Bangalore, 560019, India

Abstract: This work focuses on comparing the latest swarm-intelligence-based algorithm, the bald eagle search (BES), with three well-known metaheuristic algorithms - particle swarm optimisation (PSO), artificial bee colony (ABC), and grey wolf optimisation (GWO) - for robotic path optimisation. MATLAB® simulations are utilised to generate optimised paths between arbitrarily chosen starting and ending points, with circular-shaped obstacles of varying sizes filling the area in between. Four different environments, varying in the number of obstacles (five, six, seven, or eight), are considered. The evaluation criteria are convergence and shortest-path metrics. Results show that the BES algorithm is a competitive alternative to the widely used PSO algorithm and, in some cases, even outperforms it. Overall, BES demonstrates promise for efficient and effective path optimisation in robotic applications.

Keywords: bald-eagle search; BES; particle swarm optimisation; PSO; grey wolf optimisation; GWO; artificial bee colony; ABC; swarm intelligence; path optimisation.

DOI: 10.1504/IJRIS.2025.148027

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.4, pp.226 - 237

Received: 31 May 2022
Accepted: 27 Apr 2023
Published online: 15 Aug 2025 *

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