Open Access

ARTICLE

A Novel Hybrid Tunicate Swarm Naked Mole-Rat Algorithm for Image Segmentation and Numerical Optimization

by Supreet Singh^1,2, Nitin Mittal¹, Urvinder Singh², Rohit Salgotra², Atef Zaguia³, Dilbag Singh^4,*

1 Department of Electronics & Communication Engineering, Chandigarh University, Mohali, 140413, India
2 Department of Electronics & Communication Engineering, TIET, Patiala, 147004, India
3 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
4 School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, Korea

* Corresponding Author: Dilbag Singh. Email:

(This article belongs to the Special Issue: Applications of Intelligent Systems in Computer Vision)

Computers, Materials & Continua 2022, 71(2), 3445-3462. https://doi.org/10.32604/cmc.2022.023004

Received 25 August 2021; Accepted 20 October 2021; Issue published 07 December 2021

Abstract

This paper provides a new optimization algorithm named as tunicate swarm naked mole-rat algorithm (TSNMRA) which uses hybridization concept of tunicate swarm algorithm (TSA) and naked mole-rat algorithm (NMRA). This newly developed algorithm uses the characteristics of both algorithms (TSA and NMRA) and enhance the exploration abilities of NMRA. Apart from the hybridization concept, important parameter of NMRA such as mating factor is made to be self-adaptive with the help of simulated annealing mutation operator and there is no need to define its value manually. For evaluating the working capabilities of proposed TSNMRA, it is tested for 100-digit challenge (CEC 2019) test problems and real multi-level image segmentation problem. From the results obtained for CEC 2019 test problems, it can be seen that proposed TSNMRA performs well as compared to original TSA and NMRA. In case of image segmentation problem, comparison of TSNMRA is performed with multi-threshold electro magnetism-like optimization (MTEMO), particle swarm optimization (PSO), genetic algorithm (GA), bacterial foraging (BF) and found superior results for TSNMRA.

---

Keywords

---

Citations