A Modified Jellyfish Search Optimizer With Orthogonal Learning Strategy

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 in all aspects except the execution time. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://crativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.

Automated summary

The jellyfish search optimizer (JSO) has been widely used to solve real-world optimization problems, but faces challenges in exploration and exploitation search. This paper introduces a new variant, OJSO, based on orthogonal learning to enhance global searching capability. Evaluation shows that the proposed algorithm outperforms the original algorithm in all aspects except execution time.