An improved black widow optimization (IBWO) algorithm for solving global optimization problems

Abstract: One of the primary goals of optimization approaches is to strike a balance between exploitation and exploration strategies, thereby enhancing the efficiency of the search process. To improve this balance, considerable research efforts have been directed towards refining these strategies. This paper introduces a novel exploration approach for the Black Widow Optimization (BWO) algorithm, termed Improved BWO (IBWO), aimed at achieving a robust equilibrium between global and local search strategies. The proposed approach tracks and remembers the effective research areas during the research iteration and uses them to direct the subsequent research process toward the most promising areas of the search space. Consequently, this method facilitates convergence towards optimal global solutions, leading to the generation of higher-quality solutions. To evaluate its performance, IBWO is compared with five optimization techniques, including BWO, GA, PSO, ABC, and BBO, across 39 benchmark functions. Simulation results demonstrate that IBWO consistently maintains precision in performance, achieving superior fitness values in 87.2%, 74.4%, and 69.2% of total trials across three distinct simulation settings. These outcomes underscore the efficacy of IBWO in effectively leveraging prior search space information to enhance the balance between exploitation and exploration capabilities. The proposed IBWO has broad applicability, addressing real-world optimization challenges in pilgrim crowd management and transportation during Hajj, supply chain logistics, and energy distribution optimization.

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