How to cite this paper
Abu-Hashem, M & Shambour, M. (2024). An improved black widow optimization (IBWO) algorithm for solving global optimization problems.International Journal of Industrial Engineering Computations , 15(3), 705-720.
Refrences
Abdel-Basset, M., Abdel-Fatah, L., & Sangaiah, A. K. (2018). Metaheuristic Algorithms: A Comprehensive Review. Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, 185–231. https://doi.org/10.1016/B978-0-12-813314-9.00010-4
Abu-Hashem, M. A., Shehab, M., Shambour, M. K. Y., Daoud, M. S., & Abualigah, L. (2024). Improved Black Widow Optimization: An investigation into enhancing cloud task scheduling efficiency. Sustainable Computing: Informatics and Systems, 41, 100949. https://doi.org/10.1016/J.SUSCOM.2023.100949
Alrajhi, H. (2020). A New Virtual Synchronous Machine Control Structure for Voltage Source Converter in High Voltage Direct Current Applications. Journal of Umm Al-Qura University for Engineering and Architecture, 11(1), 1–5. https://doi.org/10.6084/m9.figshare.14399324
Al-Wesabi, F. N., Obayya, M., Hamza, M. A., Alzahrani, J. S., Gupta, D., & Kumar, S. (2022). Energy Aware Resource Optimization using Unified Metaheuristic Optimization Algorithm Allocation for Cloud Computing Environment. Sustainable Computing: Informatics and Systems, 35, 100686. https://doi.org/10.1016/J.SUSCOM.2022.100686
Basalamah, S., Khan, S. D., Felemban, E., Naseer, A., & Rehman, F. U. (2023). Deep learning framework for congestion detection at public places via learning from synthetic data. Journal of King Saud University - Computer and Information Sciences, 35(1), 102–114. https://doi.org/10.1016/J.JKSUCI.2022.11.005
Crepinsek, M., Liu, S. H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms. ACM Computing Surveys (CSUR), 45(3). https://doi.org/10.1145/2480741.2480752
Hayyolalam, V., & Pourhaji Kazem, A. A. (2020). Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87, 103249. https://doi.org/10.1016/J.ENGAPPAI.2019.103249
Houssein, E. H., Helmy, B. E. din, Oliva, D., Elngar, A. A., & Shaban, H. (2021). A novel Black Widow Optimization algorithm for multilevel thresholding image segmentation. Expert Systems with Applications, 167, 114159. https://doi.org/10.1016/J.ESWA.2020.114159
Hu, G., Du, B., & Wang, X. (2023). An improved black widow optimization algorithm for surfaces conversion. Applied Intelligence, 53(6), 6629–6670. https://doi.org/10.1007/S10489-022-03715-W/METRICS
Hu, G., Du, B., Wang, X., & Wei, G. (2022). An enhanced black widow optimization algorithm for feature selection. Knowledge-Based Systems, 235, 107638. https://doi.org/10.1016/J.KNOSYS.2021.107638
Hu, G., Zhu, X., Wei, G., & Chang, C. Ter. (2021). An improved marine predators algorithm for shape optimization of developable Ball surfaces. Engineering Applications of Artificial Intelligence, 105, 104417. https://doi.org/10.1016/J.ENGAPPAI.2021.104417
Hussain, A., & Muhammad, Y. S. (2020). Trade-off between exploration and exploitation with genetic algorithm using a novel selection operator. Complex and Intelligent Systems, 6(1), 1–14. https://doi.org/10.1007/S40747-019-0102-7/FIGURES/5
Jabbar, A., & Ku-Mahamud, K. R. (2021). Hybrid Black Widow Optimization and Variable Neighborhood Descent Algorithm for Traveling Salesman Problem. International Journal of Systematic Innovation, 6(5), 32–43. https://doi.org/10.6977.ijosi.202109_6(5).0004
K. R, S., & Ananthapadmanabha, T. (2021). Improved black widow-bear smell search algorithm (IBWBSA) for optimal planning and operation of distributed generators in distribution system. Journal of Engineering, Design and Technology. https://doi.org/10.1108/JEDT-09-2020-0362
Khajehzadeh, M., Taha, M.R., El-Shafie, A. & Eslami, M. (2011). (PDF) A Survey on Meta-Heuristic Global Optimization Algorithms. Research Journal of Applied Sciences, Engineering and Technology, 3(6). https://www.researchgate.net/publication/230996870_A_Survey_on_Meta-Heuristic_Global_Optimization_Algorithms
Kalra, M., & Singh, S. (2015). A review of metaheuristic scheduling techniques in cloud computing. Egyptian Informatics Journal, 16(3), 275–295. https://doi.org/10.1016/J.EIJ.2015.07.001
Khalilpourazari, S., Hashemi Doulabi, H., Özyüksel Çiftçioğlu, A., & Weber, G. W. (2021). Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic. Expert Systems with Applications, 177, 114920. https://doi.org/10.1016/J.ESWA.2021.114920
Khan, E. A., & Shambour, M. K. (2023a). An optimized solution for the transportation scheduling of pilgrims in Hajj using harmony search algorithm. Journal of Engineering Research, 11(2), 100038. https://doi.org/10.1016/J.JER.2023.100038
Khan, E. A., & Shambour, M. K. (2023b). Pilgrims Services Optimization During Hajj Mega Event Utilizing Heuristic Algorithms. 2023 24th International Arab Conference on Information Technology, ACIT 2023. https://doi.org/10.1109/ACIT58888.2023.10453671
Loganathan, A., & Ahmad, N. S. (2023). A systematic review on recent advances in autonomous mobile robot navigation. Engineering Science and Technology, an International Journal, 40, 101343. https://doi.org/10.1016/J.JESTCH.2023.101343
Malibari, A. A., Alotaibi, S. S., Alshahrani, R., Dhahbi, S., Alabdan, R., Al-wesabi, F. N., & Hilal, A. M. (2022). A novel metaheuristics with deep learning enabled intrusion detection system for secured smart environment. Sustainable Energy Technologies and Assessments, 52, 102312. https://doi.org/10.1016/J.SETA.2022.102312
Rahmanifard, H., & Plaksina, T. (2019). Application of artificial intelligence techniques in the petroleum industry: a review. Artificial Intelligence Review, 52(4), 2295–2318. https://doi.org/10.1007/S10462-018-9612-8/FIGURES/9
Shambour, M. K., & Khan, E. (2019). A Heuristic Approach for Distributing Pilgrims over Mina Tents. JKAU: Eng. Sci, 30(2), 11–23. https://doi.org/10.4197/Eng
Shambour, M. K. Y. (2018). VIBRANT SEARCH MECHANISM FOR NUMERICAL OPTIMIZATION FUNCTIONS. Journal of Information and Communication Technology, 17(4), 679–702. https://doi.org/10.32890/JICT2018.17.4.8276
Shambour, M. K., & Abu-Hashem, M. A. (2023). Optimizing airport slot scheduling problem using optimization algorithms. Soft Computing, 27(12), 7939-7955.
Shehab, M., Shambour, M. K. Y., Abu Hashem, M. A., Al Hamad, H. A., Shannaq, F., Mizher, M., Jaradat, G., Sh. Daoud, M., & Abualigah, L. (2024). A survey and recent advances in black widow optimization: variants and applications. Neural Computing and Applications, 1–21. https://doi.org/10.1007/S00521-024-09535-Y/METRICS
Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702–713. https://doi.org/10.1109/TEVC.2008.919004
Yang, X. S., Deb, S., Hanne, T., & He, X. (2019). Attraction and diffusion in nature-inspired optimization algorithms. Neural Computing and Applications, 31(7), 1987–1994. https://doi.org/10.1007/S00521-015-1925-9/METRICS
Zhang, X. T., Xu, B., Zhang, W., Zhang, J., & Ji, X. F. (2020). Dynamic Neighborhood-Based Particle Swarm Optimization for Multimodal Problems. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/6675996
Abu-Hashem, M. A., Shehab, M., Shambour, M. K. Y., Daoud, M. S., & Abualigah, L. (2024). Improved Black Widow Optimization: An investigation into enhancing cloud task scheduling efficiency. Sustainable Computing: Informatics and Systems, 41, 100949. https://doi.org/10.1016/J.SUSCOM.2023.100949
Alrajhi, H. (2020). A New Virtual Synchronous Machine Control Structure for Voltage Source Converter in High Voltage Direct Current Applications. Journal of Umm Al-Qura University for Engineering and Architecture, 11(1), 1–5. https://doi.org/10.6084/m9.figshare.14399324
Al-Wesabi, F. N., Obayya, M., Hamza, M. A., Alzahrani, J. S., Gupta, D., & Kumar, S. (2022). Energy Aware Resource Optimization using Unified Metaheuristic Optimization Algorithm Allocation for Cloud Computing Environment. Sustainable Computing: Informatics and Systems, 35, 100686. https://doi.org/10.1016/J.SUSCOM.2022.100686
Basalamah, S., Khan, S. D., Felemban, E., Naseer, A., & Rehman, F. U. (2023). Deep learning framework for congestion detection at public places via learning from synthetic data. Journal of King Saud University - Computer and Information Sciences, 35(1), 102–114. https://doi.org/10.1016/J.JKSUCI.2022.11.005
Crepinsek, M., Liu, S. H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms. ACM Computing Surveys (CSUR), 45(3). https://doi.org/10.1145/2480741.2480752
Hayyolalam, V., & Pourhaji Kazem, A. A. (2020). Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87, 103249. https://doi.org/10.1016/J.ENGAPPAI.2019.103249
Houssein, E. H., Helmy, B. E. din, Oliva, D., Elngar, A. A., & Shaban, H. (2021). A novel Black Widow Optimization algorithm for multilevel thresholding image segmentation. Expert Systems with Applications, 167, 114159. https://doi.org/10.1016/J.ESWA.2020.114159
Hu, G., Du, B., & Wang, X. (2023). An improved black widow optimization algorithm for surfaces conversion. Applied Intelligence, 53(6), 6629–6670. https://doi.org/10.1007/S10489-022-03715-W/METRICS
Hu, G., Du, B., Wang, X., & Wei, G. (2022). An enhanced black widow optimization algorithm for feature selection. Knowledge-Based Systems, 235, 107638. https://doi.org/10.1016/J.KNOSYS.2021.107638
Hu, G., Zhu, X., Wei, G., & Chang, C. Ter. (2021). An improved marine predators algorithm for shape optimization of developable Ball surfaces. Engineering Applications of Artificial Intelligence, 105, 104417. https://doi.org/10.1016/J.ENGAPPAI.2021.104417
Hussain, A., & Muhammad, Y. S. (2020). Trade-off between exploration and exploitation with genetic algorithm using a novel selection operator. Complex and Intelligent Systems, 6(1), 1–14. https://doi.org/10.1007/S40747-019-0102-7/FIGURES/5
Jabbar, A., & Ku-Mahamud, K. R. (2021). Hybrid Black Widow Optimization and Variable Neighborhood Descent Algorithm for Traveling Salesman Problem. International Journal of Systematic Innovation, 6(5), 32–43. https://doi.org/10.6977.ijosi.202109_6(5).0004
K. R, S., & Ananthapadmanabha, T. (2021). Improved black widow-bear smell search algorithm (IBWBSA) for optimal planning and operation of distributed generators in distribution system. Journal of Engineering, Design and Technology. https://doi.org/10.1108/JEDT-09-2020-0362
Khajehzadeh, M., Taha, M.R., El-Shafie, A. & Eslami, M. (2011). (PDF) A Survey on Meta-Heuristic Global Optimization Algorithms. Research Journal of Applied Sciences, Engineering and Technology, 3(6). https://www.researchgate.net/publication/230996870_A_Survey_on_Meta-Heuristic_Global_Optimization_Algorithms
Kalra, M., & Singh, S. (2015). A review of metaheuristic scheduling techniques in cloud computing. Egyptian Informatics Journal, 16(3), 275–295. https://doi.org/10.1016/J.EIJ.2015.07.001
Khalilpourazari, S., Hashemi Doulabi, H., Özyüksel Çiftçioğlu, A., & Weber, G. W. (2021). Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic. Expert Systems with Applications, 177, 114920. https://doi.org/10.1016/J.ESWA.2021.114920
Khan, E. A., & Shambour, M. K. (2023a). An optimized solution for the transportation scheduling of pilgrims in Hajj using harmony search algorithm. Journal of Engineering Research, 11(2), 100038. https://doi.org/10.1016/J.JER.2023.100038
Khan, E. A., & Shambour, M. K. (2023b). Pilgrims Services Optimization During Hajj Mega Event Utilizing Heuristic Algorithms. 2023 24th International Arab Conference on Information Technology, ACIT 2023. https://doi.org/10.1109/ACIT58888.2023.10453671
Loganathan, A., & Ahmad, N. S. (2023). A systematic review on recent advances in autonomous mobile robot navigation. Engineering Science and Technology, an International Journal, 40, 101343. https://doi.org/10.1016/J.JESTCH.2023.101343
Malibari, A. A., Alotaibi, S. S., Alshahrani, R., Dhahbi, S., Alabdan, R., Al-wesabi, F. N., & Hilal, A. M. (2022). A novel metaheuristics with deep learning enabled intrusion detection system for secured smart environment. Sustainable Energy Technologies and Assessments, 52, 102312. https://doi.org/10.1016/J.SETA.2022.102312
Rahmanifard, H., & Plaksina, T. (2019). Application of artificial intelligence techniques in the petroleum industry: a review. Artificial Intelligence Review, 52(4), 2295–2318. https://doi.org/10.1007/S10462-018-9612-8/FIGURES/9
Shambour, M. K., & Khan, E. (2019). A Heuristic Approach for Distributing Pilgrims over Mina Tents. JKAU: Eng. Sci, 30(2), 11–23. https://doi.org/10.4197/Eng
Shambour, M. K. Y. (2018). VIBRANT SEARCH MECHANISM FOR NUMERICAL OPTIMIZATION FUNCTIONS. Journal of Information and Communication Technology, 17(4), 679–702. https://doi.org/10.32890/JICT2018.17.4.8276
Shambour, M. K., & Abu-Hashem, M. A. (2023). Optimizing airport slot scheduling problem using optimization algorithms. Soft Computing, 27(12), 7939-7955.
Shehab, M., Shambour, M. K. Y., Abu Hashem, M. A., Al Hamad, H. A., Shannaq, F., Mizher, M., Jaradat, G., Sh. Daoud, M., & Abualigah, L. (2024). A survey and recent advances in black widow optimization: variants and applications. Neural Computing and Applications, 1–21. https://doi.org/10.1007/S00521-024-09535-Y/METRICS
Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702–713. https://doi.org/10.1109/TEVC.2008.919004
Yang, X. S., Deb, S., Hanne, T., & He, X. (2019). Attraction and diffusion in nature-inspired optimization algorithms. Neural Computing and Applications, 31(7), 1987–1994. https://doi.org/10.1007/S00521-015-1925-9/METRICS
Zhang, X. T., Xu, B., Zhang, W., Zhang, J., & Ji, X. F. (2020). Dynamic Neighborhood-Based Particle Swarm Optimization for Multimodal Problems. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/6675996