An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation

Houssein, Essam H.; Helmy, Bahaa El-din; Elngar, Ahmed A.; AbdElminaam, Diaa Salama; Shaban, Hassan

doi:10.1109/access.2021.3072336

Cited by 83 publications

(26 citation statements)

References 64 publications

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“…As an instance, Houssein et al [69] enhanced the performance of the Archimedes optimization process by employing the local escaping operator (LEO) operator (AOA). The tunicate swarm algorithm (TSA) was enhanced by Houssein et al [70] by the addition of a local escape operator (LEO) operator, which increased the swarm agent convergence rate and improved the local search efficiency. Marine Predators Algorithm (MPA) was enhanced by M.Oszust [71] with the help of a Local Escaping Operator so that it could achieve global optimization.…”

Section: Related Workmentioning

confidence: 99%

Comparing SSALEO as a Scalable Large Scale Global Optimization Algorithm to High-Performance Algorithms for Real-World Constrained Optimization Benchmark

et al. 2022

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The Salp Swarm Algorithm (SSA) outperforms well-known algorithms such as particle swarm optimizers and grey wolf optimizers in complex optimization challenges. However, like most meta-heuristic algorithms, SSA suffers from slow convergence and stagnation in the best local solution. In this study, a Salp swarm algorithm (SSA) is combined with a local escaping operator (LEO) to overcome some inherent limitations of the original SSA. SSALEO is a novel search technique that accounts for population diversity, the imbalance between exploitation and exploration, and the SSA algorithm's premature convergence. By implementing LEO in SSALEO, the search slowdown in SSA is eliminated, and the local search efficiency of swarm agents is improved. The proposed SSALEO method is tested using the CEC 2017 benchmark with 50 and 100 decision variables, seven CEC2008lsgo test functions with 200, 500, and 1000 decision variables, and its performance was compared to other metaheuristic algorithms (MAs) and advanced algorithms, including seven Salp swarm variants. The comparisons show that SSA greatly benefits from LEO by enhancing the quality and accelerating its solutions' convergence rate. The SSALEO was then assessed using a benchmark set of seven well-known constrained design challenges in various engineering domains defined in the CEC 2020 conference benchmark. Friedman and Wilcoxon rank-sum statistical tests are also used to examine the results. ACCORDING TO EXPERIMENTAL DATA AND STATISTICAL TESTS, the SSALEO algorithm is very competitive and often superior to the algorithms used in the studies. Further, the proposed approach can be viewed as a special LSGO optimizer whose performance exceeds that of specialized state-of-the-art algorithms like CMA-ES and SHADE.

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Section: Related Workmentioning

confidence: 99%

Comparing SSALEO as a Scalable Large Scale Global Optimization Algorithm to High-Performance Algorithms for Real-World Constrained Optimization Benchmark

et al. 2022

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“…An arithmetical technique of jet propulsion is innovative in 3 limits [34]: follows the location of maximal qualified agent, remaining nearby the optimum agent, and avoids conflicts among the exploration agents. For avoiding inter agent conflicts while looking for an optimum position, the novel agent position is assessed as follows: For better exploration and exploitation [37], TSA requires time complexity for jet propulsion and swarm behavior. As a result, the TSA algorithm's overall time complexity is defined as: TSA = O (Max iterations * n* d * N) (28) To find the most optimal food source for tunicate, n, d, and N are used to define population size, jet propulsion, and swarm behavior, respectively.…”

Section: Hyber-parameters Tuning Using Tsamentioning

confidence: 99%

A Novel Tunicate Swarm Algorithm With Hybrid Deep Learning Enabled Attack Detection for Secure IoT Environment

Taher

Elhoseny

Hassan³

et al. 2022

IEEE Access

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The Internet of Things (IoT) paradigm has matured and expanded rapidly across many disciplines. Despite these advancements, IoT networks continue to face an increasing security threat as a result of the constant and rapid changes in the network environment. In order to address these vulnerabilities, the Fog system is equipped with a robust environment that provides additional tools to beef up data security. However, numerous attacks are persistently evolving in IoT and fog environments as a result of the development of several breaches. To improve the efficiency of intrusion detection in the Internet of Things (IoT), this research introduced a novel tunicate swarm algorithm that combines a long-short-term memory-recurrent neural network. The presented model accomplishes this goal by first undergoing data pre-processing to transform the input data into a usable format. Additionally, attacks in the IoT ecosystem can be identified using a model built on long-short-term memory recurrent neural networks. There is a strong correlation between the number of parameters and the model's capability and complexity in ANN models. It is critical to keep track of the number of parameters in each model layer to avoid over-or under-fitting. One way to prevent this from happening is to modify the number of layers in your data structure. The tunicate swarm algorithm is used to fine-tune the hyper-parameter values in the Long Short-Term Memory-Recurrent Neural Network model to improve how well it can find things. TSA was used to solve several problems that couldn't be solved with traditional optimization methods. It also improved performance and shortened the time it took for the algorithm to converge. A series of tests were done on benchmark datasets. Compared to related models, the proposed TSA-LSTMRNN model achieved 92.67, 87.11, and 98.73 for accuracy, recall, and precision, respectively, which indicate the superiority of the proposed model.

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“…Multiple challenges have been widely resolved by the segmentation method. The major purpose of segmentation is to split an image into many homogeneous segments/regions with common features such as color, texture, contrast, brightness, size, and form depending upon certain thresholding value(s) [ 11 ]. Often, it is extensively employed for distinguishing background and foreground as the primary phase for interpreting and identifying images.…”

Section: Introductionmentioning

confidence: 99%

Design of Metaheuristic Optimization‐Based Vascular Segmentation Techniques for Photoacoustic Images

Vaiyapuri

Dutta²,

Sikkandar

et al. 2022

Contrast Media & Molecular Imaging

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Biomedical imaging technologies are designed to offer functional, anatomical, and molecular details related to the internal organs. Photoacoustic imaging (PAI) is becoming familiar among researchers and industrialists. The PAI is found useful in several applications of brain and cancer imaging such as prostate cancer, breast cancer, and ovarian cancer. At the same time, the vessel images hold important medical details which offer strategies for a qualified diagnosis. Recently developed image processing techniques can be employed to segment vessels. Since vessel segmentation on PAI is a difficult process, this paper employs metaheuristic optimization-based vascular segmentation techniques for PAI. The proposed model involves two distinct kinds of vessel segmentation approaches such as Shannon’s entropy function (SEF) and multilevel Otsu thresholding (MLOT). Moreover, the threshold value and entropy function in the segmentation process are optimized using three metaheuristics such as the cuckoo search (CS), equilibrium optimizer (EO), and harmony search (HS) algorithms. A detailed experimental analysis is made on benchmark PAI dataset, and the results are inspected under varying aspects. The obtained results pointed out the supremacy of the presented model with a higher accuracy of 98.71%.

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An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation

Cited by 83 publications

References 64 publications

Comparing SSALEO as a Scalable Large Scale Global Optimization Algorithm to High-Performance Algorithms for Real-World Constrained Optimization Benchmark

Comparing SSALEO as a Scalable Large Scale Global Optimization Algorithm to High-Performance Algorithms for Real-World Constrained Optimization Benchmark

A Novel Tunicate Swarm Algorithm With Hybrid Deep Learning Enabled Attack Detection for Secure IoT Environment

Design of Metaheuristic Optimization‐Based Vascular Segmentation Techniques for Photoacoustic Images

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