Title: Alligator optimisation algorithm for solving unconstrainted optimisation problems

Authors: Weng-Hooi Tan; Junita Mohamad-Saleh

Addresses: School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, 11430, Penang, Malaysia ' School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, 11430, Penang, Malaysia

Abstract: Inspired by cooperative hunting skills and movement patterns of alligators in nature, this research paper proposes a novel bio-inspired meta-heuristic algorithm, named alligator optimisation (AgtrO) algorithm. Upon mathematical modelling, AgtrO emphasises two main phases: the hunting phase that mimics fishing, purse seining and catching mechanisms, and the relocating phase that mimics travelling and homing instinct mechanisms. The hunting phase discovers any promising global optimal area, towards tracking the true global optimal solution. Meanwhile, the relocating phase avoids local optima (traps) through local exploration and conducts in-depth investigations through local exploitation. The proposed AgtrO was tested on 23 classical optimisation benchmark functions and ten modern CEC-C06-2019 benchmark functions, in comparison with eight recently proposed state-of-the-art algorithms. Upon evaluation, AgtrO has been proved to outperform other algorithms in terms of global-best achievement, while being very competitive in terms of convergence speed.

Keywords: bio-inspired; metaheuristic; optimisation; classical benchmark; CEC benchmark.

DOI: 10.1504/IJBIC.2023.130025

International Journal of Bio-Inspired Computation, 2023 Vol.21 No.1, pp.11 - 25

Received: 29 Nov 2021
Accepted: 09 Jul 2022
Published online: 04 Apr 2023 *