Extensive Review of Big Bang Big Crunch Optimization Algorithm
Author Affiliations
- 1Department of Electronics and Communication Engineering, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana 133207, India
- 2Department of Electronics and Communication Engineering, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana 133207, India
Res. J. Engineering Sci., Volume 13, Issue (3), Pages 35-41, September,26 (2024)
Abstract
An updated utilizing an optimization method known for its BB-BC method, produced. It is in accordance with the concepts of the big bang and big crunch, which is among the theory explaining the universe's development. Initially, Big Bang Big Crunch method was introduced address optimization problems with continuous solution spaces. Consequently, among the population-level optimization strategies, the Big Bang –Big Crunch strategies adjusted within this research to handle optimization issues. One of the all issues utilized in the literature, Alternate link to selection of good link by the help of optimization BB-BC dynamic, are utilized for evaluation the efficiency of the recommended methods. BB-BC method control parameter is examined for its influence on performance using the well-known small and medium distance by the link cost like a throughput, Delay, Jitter, Energy, PDR. The results obtained are shown in comparison. The binary version of the BB-BC approach solves by the help of optimization method successfully regarding the standard on the response, according to the experimental results.
References
- Camp, C. V. (2007)., Design of space trusses using big bang–big crunch optimization., Journal of Structural Engineering, 133(7), 999-1008.
- Erol, O. K., & Eksin, I. (2006)., A new optimization method: big bang–big crunch., Advances in engineering software, 37(2), 106-111.
- KAZEMZADEH, A. S., & Hasançebi, O. (2016)., Structural optimization using big bang-big crunch algorithm: a review.,
- Kaveh, A., & Zolghadr, A. (2012)., Truss optimization with natural frequency constraints using a hybridized CSS-BBBC algorithm with trap recognition capability., Computers & Structures, 102, 14-27.
- Kumar, S., Singh, A., & Walia, S. (2018)., Parallel Big Bang–Big Crunch global optimization algorithm: performance and its applications to routing in WMNs., Wireless Personal Communications, 100, 1601-1618.
- Qawqzeh, Y. K., Jaradat, G., Al-Yousef, A., Abu-Hamdah, A., Almarashdeh, I., Alsmadi, M., ... & Haddad, F. (2020)., Applying the big bang-big crunch metaheuristic to large-sized operational problems., International Journal of Electrical and Computer Engineering, 10(3), 2484.
- Chehouri, A., Younes, R., Ilinca, A., & Perron, J. (2015)., Review of performance optimization techniques applied to wind turbines., Applied Energy, 142, 361-388.
- Galambos, T. V. (Ed.). (1998)., Guide to stability design criteria for metal structures., John Wiley & Sons.
- Krehl, P. O. (2008)., History of shock waves, explosions and impact: a chronological and biographical reference., Springer Science & Business Media.
- Wu, T. L., Buesink, F., & Canavero, F. (2013)., Overview of signal integrity and EMC design technologies on PCB: Fundamentals and latest progress., IEEE transactions on electromagnetic compatibility, 55(4), 624-638.
- Meriläinen, A., Seppälä, A., Saari, K., Seitsonen, J., Ruokolainen, J., Puisto, S., ... & Ala-Nissila, T. (2013)., Influence of particle size and shape on turbulent heat transfer characteristics and pressure losses in water-based nanofluids., International journal of heat and mass transfer, 61, 439-448.
- Dhimish, M., & Silvestre, S. (2019)., Estimating the impact of azimuth-angle variations on photovoltaic annual energy production., Clean Energy, 3(1), 47-58.
- Dey, A., & Yodo, N. (2019)., A systematic survey of FDM process parameter optimization and their influence on part characteristics., Journal of Manufacturing and Materials Processing, 3(3), 64.
- Khaleel, M., Ahmed, A. A., & Alsharif, A. (2023)., Artificial Intelligence in Engineering., Brilliance: Research of Artificial Intelligence, 3(1), 32-42.
- Ghosh, S., Singh, A., & Kumar, S. (2023)., BBBC-U-Net: Optimizing U-Net for automated plant phenotyping using big bang big crunch global optimization algorithm., International Journal of Information Technology, 15(8), 4375-4387.
- Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022)., Interpretable machine learning: Fundamental principles and 10 grand challenges., Statistic Surveys, 16, 1-85.
- Sparks, E. R., Talwalkar, A., Haas, D., Franklin, M. J., Jordan, M. I., & Kraska, T. (2015)., Automating model search for large scale machine learning., In Proceedings of the Sixth ACM Symposium on Cloud Computing (pp. 368-380).
- Ren, Y., Zhang, L., & Suganthan, P. N. (2016)., Ensemble classification and regression-recent developments, applications and future directions., IEEE Computational intelligence magazine, 11(1), 41-53.
- Fernandez, S. A., Juan, A. A., de Armas Adrian, J., e Silva, D. G., & Terrén, D. R. (2018)., Metaheuristics in telecommunication systems: network design, routing, and allocation problems., IEEE Systems Journal, 12(4), 3948-3957.
- Yeh, S. P., Talwar, S., Lee, S. C., & Kim, H. (2008). WiMAX femtocells: a perspective on network architecture, capacity, and coverage. IEEE Communications Magazine, 46(10), 58-65., undefined, undefined
- Julian, D., et al. (2002)., QoS and fairness constrained convex optimization of resource allocation for wireless cellular and ad hoc networks., Proceedings. Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies. 2002. IEEE.
- Galić, M., & Klanšek, U. (2023)., Active BIM in Optimization-Supported Construction Project Management: Achievements, Challenges and Applications., The Future of Project Management: Adapting to Modern Needs, 70.
- Kim, C. O., Jun, J., Baek, J. K., Smith, R. L., & Kim, Y. D. (2005)., Adaptive inventory control models for supply chain management., The International Journal of Advanced Manufacturing Technology, 26, 1184-1192.
- Ypsilantis, P., & Zuidwijk, R. (2019)., Collaborative fleet deployment and routing for sustainable transport., Sustainability, 11(20), 5666.
- Mason, S. J., Ribera, P. M., Farris, J. A., & Kirk, R. G. (2003)., Integrating the warehousing and transportation functions of the supply chain., Transportation Research Part E: Logistics and Transportation Review, 39(2), 141-159.
- Candan, F., Dik, O. F., Kumbasar, T., Mahfouf, M., & Mihaylova, L. (2023)., Real-time interval type-2 fuzzy control of an unmanned aerial vehicle with flexible cable-connected payload., Algorithms, 16(6), 273.
- Sleiman, J. P., Farshidian, F., Minniti, M. V., & Hutter, M. (2021)., A unified mpc framework for whole-body dynamic locomotion and manipulation., IEEE Robotics and Automation Letters, 6(3), 4688-4695.