Research Area:  Metaheuristic Computing

Abstract:

This paper presents a Multi-threaded version of the Spotted Hyena Optimizer algorithm with thread-crossing techniques (MT-SHO) to improve the ability of the algorithm to explore the search space. The original algorithm is inspired by the hunting behavior of the spotted hyena. Along the different sections of the work, we explain in detail how the original algorithm simulates the spotted hyena’s behavior to optimize highly complex mathematical functions and how we handle the procedures and results of the multi-threaded version, with thread-crossing techniques that improve the ability to explore and exploit the search space by letting threads learn between them. We present the experiments used to determine the best value of the parameters used in the parallel version of the algorithm and to prove that our proposal obtains significantly good results we compare the results obtained by evaluating 24 benchmark functions with the results published for the original algorithm as well as other metaheuristic algorithms.

Keywords:  
Spotted Hyena Optimizer
Metaheuristic
Global optimization
Multi-threaded algorithms
Nature-inspired algorithms
Simulated annealing
Genetic algorithms

Author(s) Name:  Felix Martinez-Rios, Alfonso Murillo-Suarez

Publisher name:  ELSEVIER

DOI:  10.1016/j.procs.2021.01.026

Volume Information:  Volume 179

Paper Link:   https://www.sciencedirect.com/science/article/pii/S1877050921000296