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

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 (sa) 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.

Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

“…Hence, the higher the fitness is, the greater the swarm finds the threshold to segment the target from the background. Common swarm intelligence algorithms include the whale optimization algorithm [1,29], Harris hawks optimization [2], artificial neural networks [3,11,30], deep learning [4,12,21,38], gray wolf optimization [5,39], particle swarm optimization [7,23,40], differential evolution algorithm [9], cuckoo search algorithm [10], ant colony optimization [13,33], genetic algorithm [14,40], artificial bee colony algorithm [15,25], sparrow search algorithm [16], moth swarm algorithm (MSA) [24], emperor penguin optimization (EPO) [26], marine predators algorithm (MPA) [27], salp swarm algorithm (SSA) [27], firefly algorithm (FA) [28], Aptenodytes Forsteri optimization algorithm (AFOA) [32], artificial fish swarm algorithm (AFSA) [36], artificial plant community (APC) [41,42], krill swarm (KS) [43], immune system (IS) [44], naked mole-rat algorithm (NMRA) [45], attention mechanism [46], and so on.…”

Application of Improved Satin Bowerbird Optimizer in Image Segmentation

“…Through the evaluation indices such as PSNR, SSIM, this method enjoys certain advantages in medical image segmentation quality and calculation time, compared with the improved DE, moth flame optimization algorithm and other methods. Singh et al (2022) combine the tune swarm algorithm (TSA) and the naked mole rat algorithm (NMRA), and compare the data of CEC 2019 standard function and image segmentation, they show that this method is better than PSO, GA and others. According to the application of intelligent optimization algorithms in multilevel thresholding in recent years, we find this kind of method with numerous variations has been used widely in different fields.…”