Research Area:  Metaheuristic Computing

Abstract:

The jellyfish search optimizer (JSO) is one of the newest swarm intelligence algorithms which has been widely used to solve different real-world optimization problems. However, its most challenging task is to regulate the exploration and exploitation search to avoid problems in harmonic convergence or be trapped into local optima. In this paper, we propose a new variant of JSO named OJSO, based on orthogonal learning with the aim to improve the capability of global searching of the original algorithm. The orthogonal learning is a strategy for discovering more useful information from two recent solution vectors by predicting the best combination using limited trials instead of exhaustive trials via an orthogonal experimental design. To evaluate the effectiveness of our approach, 23 benchmark functions are used. The evaluation process leads us to conclude that the proposed algorithm strongly outperforms the original algorithm in all aspects except the execution time.

Keywords:  
jellyfish search optimizer
swarm intelligence algorithm
exploration
exploitation
harmonic convergence
orthogonal learning
benchmark function

Author(s) Name:  Ghaith Manita, Aymen Zermani

Publisher name:  Elsevier

DOI:  10.1016/j.procs.2021.08.072

Volume Information:  Volume 192, Pages 697-708

Paper Link:   https://www.sciencedirect.com/science/article/pii/S1877050921015593