A New Tiger Beetle Algorithm for Cybersecurity, Medical Image Segmentation and Other Global Problems Optimization
Article Sidebar
PDF
311 views
Main Article Content
Abstract
The tiger beetle is a fierce and cunning predator insect that uses deception to hunt its prey. The tiger beetle traps and hunts them by digging holes along the path of other insects. This study has used the tiger beetle's hunting strategy to create the tiger beetle optimization (TBO) algorithm. In this algorithm, each solution represents the position of a tiger beetle, with the optimal position being the prey's location. Using this method, the tiger beetles gradually converge to the optimal solution, creating holes around them and searching for them. We evaluate the TBO algorithm's search capability using a series of well-known mathematical test functions. Moreover, Among the sophisticated forms of malware are polymorphic viruses, which are adept at changing their behaviour while maintaining the same essential functions. Thus, a machine learning-based malware analysis system utilizing the power of the proposed TBO is introduced in this article. Compared to other optimization methods, the proposed algorithm has shown less error in finding the optimal solution when implemented and evaluated on different functions. The tiger beetle optimization algorithm has proven helpful in various applications, including image clustering and reservoir well placement, where it can identify damaged areas or tissues with greater accuracy. When diagnosing lung cancer, the proposed method has shown a sensitivity, validity, and accuracy of 88.63\%, 87.58\%, and 89.86\%, respectively, using EBT, WKNN, ESKNM, and QSVM methods.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Rezaei, S. M., Pishvaee, H. A., Khosravi, A. R., & Jafari, M. A. (2023). A survey on metaheuristic optimization algorithms for resource allocation in fog computing environments. IEEE Access, 11, 3864622. doi:10.1109/ACCESS.2023.3864622.
S. Mirjalili, J. S. Dong, A. S. Sadiq, and H. Faris, "Genetic algorithm: Theory, literature review, and application in image reconstruction", Spring Verlag. pp. 69-85, 2020.
F. Padillo, J. M. Luna, and S. Ventura, "A grammar-guided genetic programming algorithm for associative classification in Big Data". Cognitive Computation, vol. 11, no.3, pp. 331-346, 2019.
A. R. Verma, and B. Gupta, "A novel approach adaptive filtering method for electromyogram signal using Gray Wolf optimization algorithm", SN Applied Sciences, vol. 2, no.1, 16, 2020.
A. Got, A. Moussaoui, and D. Zouache, "A guided population archive whale optimization algorithm for solving multiobjective optimization problems", Expert Systems with Applications, vol. 141,112972, 2020.
V. Kumar, K. K. Kaleka, and Kaur, A, "Spiral-Inspired Spotted Hyena Optimizer and Its Application to Constraint Engineering Problems",Wireless Personal Communications, vol. 116, pp. 865-881, 2021.
R. Sihwail, K. Omar, K. A. Z. Ariffin, and M. Tubishat, "Improved harris hawks optimization using elite opposition-based learning and novel search mechanism for feature selection", IEEE Access, vol. 8, pp. 121127-121145, 2020.
V. Hayyolalam, and A. A. P. Kazem, "Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems", Engineering Applications of Artificial Intelligence, vol. 87, 103249, 2020.
A. Kaveh, and A. D. Eslamlou, "Water strider algorithm: A new metaheuristic and applications", Structures, vol. 25, pp. 520-541, 2020.
W. Al-Sorori, and A. M. Mohsen, "New Caledonian crow learning algorithm: A new metaheuristic algorithm for solving continuous optimization problems", Applied Soft Computing, vol 92, 106325, 2020.
S. Ripon, M. G. Sarowar, F. Qasim, and S. T. Cynthia, "An Efficient Classification of Tuberous Sclerosis Disease Using Nature Inspired PSO and ACO Based Optimized Neural Network", In Nature Inspired Computing for Data Science, vol. 871, pp. 1-28, 2020.
S. Ebadinezhad, "DEACO: Adopting dynamic evaporation strategy to enhance ACO algorithm for the traveling salesman problem", Engineering Applications of Artificial Intelligence, vol. 92, 103649, 2020.
W. Zhao, L. Wang, and Z. Zhang, "Atom search optimization and its application to solve a hydrogeologic parameter estimation problem", Knowledge-Based Systems, vol. 163, pp. 283-304, 2019.
A. Shabani, B. Asgarian, M. Salido, and S. A. Gharebaghi, "Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems", Expert Systems with Applications, vol. 161, 113698, 2020.
J. Ye, Y. Yu, Y. Zhang, Y. Todo, and S. Gao, "Improved Teaching-Learning-based Optimization Algorithm with Advanced Learning Strategy",Presented at 12th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2020
S. H. S. Moosavi, and V. K. Bardsiri, "Poor and rich optimization algorithm: A new human-based and multi populations algorithm", Engineering Applications of Artificial Intelligence, vol. 86, pp. 165-181, 2019.
E. Fadakar, and M. Ebrahimi, "A new metaheuristic football game inspired algorithm", Presented at 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), 2016.
Ebadinezhad S (2020) DEACO: adopting dynamic evaporation strategy to enhance ACO algorithm for the traveling salesman problem. Eng Appl Artif Intel 92:103649
Yang K, You X, Liu S, Pan H (2020) A novel ant colony optimization based on game for traveling salesman problem. Appl Intell 50(12):4529–4542
Boussetta, M. S., Mekki, S., El-Sharkawi, M., & Younis, M. (2023). A hybrid metaheuristic algorithm for resource allocation in cloud computing environments. IEEE Access, 11, 10087-10099. doi:10.1109/ACCESS.2023.3802628.
Z. M. Elgamal, N. B. M. Yasin, M. Tubishat, M. Alswaitti, and S. Mirjalili, "An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field", IEEE Access, vol. 8, pp. 186638-186652, 2020.
ashaei, E., Pashaei, E. A fusion approach based on black hole algorithm and particle swarm optimization for image enhancement. Multimed Tools Appl 82, 297–325 (2023). https://doi.org/10.1007/s11042-022-13275-3
Saihood, A., Karshenas, H., & Nilchi, A. R. N. (2022). Deep fusion of gray level co-occurrence matrices for lung nodule classification. PLOSONE, 17(9), e0274516. doi:10.1371/journal.pone.0274516
X. Zhang, Y. Ding and Y. Liu, "A hybrid artificial bee colony algorithm for dynamic vehicle routing problem with time windows," in Journal of Intelligent Manufacturing, vol. 1, no. 1, pp. 273-287, 2022.
S. Mirjalili, "SCA: A sine cosine algorithm for solving optimization problems", Knowledge-Based Systems, vol. 96, pp. 120-133, 2016.
P. Mohindru, J. Kaur, and V. Akre, "Image Compression Using Hybrid Technique Combining VQ and Bacteria Foraging Optimization", International Journal of Advanced Research in Electrical Electronics and Instrumentation Engineering, vol. 9, no.1, pp. 2774-2780, 2020.
S. K. Baliarsingh, S. Vipsita, and B. Dash (2020). "A new optimal gene selection approach for cancer classification using enhanced Jaya-based forest optimization algorithm", Neural Computing and Applications, vol. 32, no. 12, pp. 8599-8616, 2020.
Z. Zhao, J. Zhao, K. Song, A. Hussain, Q. Du, Y. Dong, and X. Yang, X, "Joint DBN and Fuzzy C-Means unsupervised deep clustering for lung cancer patient stratification", Engineering Applications of Artificial Intelligence, vol. 91, 103571, 2020.
A. B. Mathews, and M. K. Jeyakumar, "Analysis of Lung Tumor Detection using Various Segmentation Techniques", Presented at International Conference on Inventive Computation Technologies (ICICT), pp. 454-458, 2020.
W. Xie, C. Xing, J. Wang, S. Guo, M. W. Guo, and L. F. Zhu, "Hybrid Henry Gas Solubility Optimization Algorithm Based on the Harris Hawk Optimization", IEEE Access, vol. 8, pp. 144665-144692, 2020.
A. Krishna, P. S. Rao, and C. Z. Basha, "Computerized Classification of CT Lung Images using CNN with Watershed Segmentation", Presented at Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 18-21, 2020.
X. Xu, C. Wang, J. Guo, Y. Gan, J. Wang, H. Bai, and Z. Yi, Z, "MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale costsensitive neural networks", Medical Image Analysis, vol. 65, 101772, 2020.
M. A. Khan, S. Rubab, A. Kashif, M. I. Sharif, N. Muhammad, J. H. Shah, and S. C. Satapathy, "Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection", Pattern Recognition Letters, vol. 129, pp. 77-85, 2020.
N. Dhanachandra, and Y. J. Chanu,"An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm", Multimedia Tools and Applications, vol. 79, pp.18839-18858, 2020.
J. Arora, and M. Tushir, "Hybrid KFCM-PSO Clustering Technique for Image Segmentation", Presented at Proceedings of International Conference on Artificial Intelligence and Applications, pp. 443-451, 2020.
X. Chu, F. Cai, D. Gao, L. Li, J. Cui, S. X. Xu, and Q. Qin, "An artificial bee colony algorithm with adaptive heterogeneous competition for global optimization problems", Applied Soft Computing, vol. 93, 106391, 2020.
V. K. Kamboj, A. Nandi, A. Bhadoria, and S. Sehgal, "An intensify Harris Hawks optimizer for numerical and engineering optimization problems", Applied Soft Computing, vol. 89, 106018, 2020.
J. Jiang, R. Jiang, X. Meng, K. Li, "SCGSA: A sine chaotic gravitational search algorithm for continuous optimization problems", Expert Systems with Applications, vol. 144, 113118, 2020.
Z. K. Feng, W. J. Niu, and S. Liu, "Cooperation search algorithm: A novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems", Applied Soft Computing, vol. 98, 106734, 2020.
R. Chai, A. Savvaris, A. Tsourdos, and S. Chai, "Overview of Trajectory Optimization Techniques. In Design of Trajectory Optimization Approach for Space Maneuver Vehicle Skip Entry Problems", Springer Singapore, pp. 7-25, 2020.
N. Abiwinanda, M. Hanif, S. T. Hesaputra, A. Handayani, and T. R. Mengko, (2019). "Brain tumour classification using convolutional neural network", Presented at World Congress on Medical Physics and Biomedical Engineering, Springer Singapore, pp. 183-189, 2018.
Li, Ping, S. Wang, T. Li, J. Lu, Y. HuangFu, and D. Wang. "A large-scale CT and PET/CT dataset for lung cancer diagnosis [dataset]." The cancer imaging archive (2020).
Siemanowski J, Heydt C, Merkelbach-Bruse S. Predictive molecular pathology of lung cancer in Germany with focus on gene fusion testing: Methods and quality assurance. Cancer Cytopathol. 2020 Sep;128(9):611-621. doi: 10.1002/cncy.22293. PMID: 32885916.
Xue J, Shen B. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization [Internet]. Vol. 79, Journal of Supercomputing. Springer US; 2023. 7305–7336 p. Available from: https://doi.org/10.1007/s11227-022-04959-6
Naderi M, Khamehchi E. Well placement optimization using metaheuristic bat algorithm. J Pet Sci Eng [Internet]. 2017;150:348–54. Available from: http://dx.doi.org/10.1016/j.petrol.2016.12.028
Raji S, Dehnamaki A, Somee B, Mahdiani MR. A new approach in well placement optimization using metaheuristic algorithms. J Pet Sci Eng [Internet]. 2022;215:110640. Available from: https://www.sciencedirect.com/science/article/pii/S0920410522005113
M. M. Abubakar, A. Z. Umar and M. Abubakar, "Personal Data and Privacy Protection Regulations: State of compliance with Nigeria Data Protection Regulations (NDPR) in Ministries, Departments, and Agencies (MDAs)," 2022 5th Information Technology for Education and Development (ITED), Abuja, Nigeria, 2022, pp. 1-6, doi: 10.1109/ITED56637.2022.10051182.
Z. Mao, HuiLi, Z. Huang, Y. Tian, X. Zhao and H. Zhang, "Ghostwriting-Federal Learning Key Technology Research for Big Data Privacy Protection," 2022 4th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Shanghai, China, 2022, pp. 387-390, doi: 10.1109/MLBDBI58171.2022.00080. keywords: Differential privacy;Analytical models;Machine learning algorithms;Publishing;Data protection;Big Data;Data models;Federated learning algorithms;big data privacy protection;anomalous data;defense mechanisms,
S. Sivakumar, S. Saminathan, R. Ranjana, M. Mohan and P. K. Pareek, "Malware Detection Using The Machine Learning Based Modified Partial Swarm Optimization Approach," 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC), Dharwad, India, 2023, pp. 1-5, doi: 10.1109/ICAISC58445.2023.10199796. keywords: Machine learning algorithms;Machine learning;Benchmark testing;Feature extraction;Malware;Particle swarm optimization;Principal component analysis;Machine Learning;Malware Detection;Particle Swarm Optimization (PSO);Optimal Solutions,
J. Lande, M. Maheswari, S. M. Kamali, R. Dineshkumar and P. M. S, "Hybrid Optimization Based Long Short-Term Memory for Android Malware Detection," 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT), Bengaluru, India, 2023, pp. 1-5, doi: 10.1109/EASCT59475.2023.10393408. keywords: Measurement;Ant colony optimization;Feature extraction; Malware;Libraries;Cleaning;Whale optimization algorithms;Ant Lion Optimization;Long Short-Term Memory;Malware Detection;NumPy and Particle Swarm Optimization,
A. Jain, K. Tripathi, A. Jatain and Manju, "Anomaly Detection in the Cloud Environment with Clustering Optimization Model for Attack Detection in IDs," 2023 International Conference on IoT, Communication and Automation Technology (ICICAT), Gorakhpur, India, 2023, pp. 1-5, doi: 10.1109/ICICAT57735.2023.10263676. keywords: Cloud computing;Intrusion detection;Estimation;Real-time systems;Data models;Bayes methods;Task analysis;Intrusion Detection System (IDS);Bayesian Network;Deep Learning;Optimization;Malicious Activities,
P. Kotian and R. Sonkusare, "Detection of Malware in Cloud Environment using Deep Neural Network," 2021 6th International Conference for Convergence in Technology (I2CT), Maharashtra, India, 2021, pp. 1-5, doi: 10.1109/I2CT51068.2021.9417901. keywords: Deep learning;Cloud computing;Portable computers;Standards organizations;Standardization;Filtering algorithms;Malware;Convolutional Neural Networks;Malware detection;Deep Learning;Security,
L. P. Khan, "Obfuscated Malware Detection Using Artificial Neural Network (ANN)," 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India, 2023, pp. 1-5, doi: 10.1109/ICECCT56650.2023.10179639. keywords: Electric potential;Costs;Computer hacking;Computational modeling;Organizations;Artificial neural networks;Learning (artificial intelligence);Malware;ANN;accuracy;precision;obfuscated;machine learning,
A. Jain, K. Tripathi, A. Jatain and Manju, "Anomaly Detection in the Cloud Environment with Clustering Optimization Model for Attack Detection in IDs," 2023 International Conference on IoT, Communication and Automation Technology (ICICAT), Gorakhpur, India, 2023, pp. 1-5, doi: 10.1109/ICICAT57735.2023.10263676. keywords: Cloud computing;Intrusion detection;Estimation;Real-time systems;Data models;Bayes methods;Task analysis;Intrusion Detection System (IDS);Bayesian Network;Deep Learning;Optimization;Malicious Activities.