ADVANCED OPTIMIZATION ALGORITHMS TO BOOST THE RELIABILITY OF COMPLEX SYSTEMS VIA COMPONENT SENSITIVITY ANALYSIS
Ключевые слова:
Reliability theory, water distribution network, Golden Jackal Optimization, Pelican Optimization Algorithm, Birnbaum importance, minimal pathsАннотация
Maintenance resources are a critical factor in ensuring reliable water supply in municipal networks, particularly in terms of their distribution among the individual pipeline components. Uniform allocation inflicts unnecessary cost or reliability objectives are not achieved because of the neglect of differentiating between pipes. This paper presents a framework to guide two recently developed nature-inspired metaheuristics, the Golden Jackal Optimization (GJO) and the Pelican Optimization Algorithm (POA) to cost-efficient reliability allocation by incorporating the exact minimal-path enumeration, Birnbaum structural importance ranking . The case studied is a directed pipeline network from Pyigyitagon Township, Mandalay, Myanmar. The Birnbaum importance indices determine a priority value for each pipe segment and contribute to the formation of a fitness function which penalises deviations from the target, costs, and the violation of the priority ordering. Under the same conditions, experiments reveal that GJO is able to converge significantly faster, with POA offering more accurate tracking of the target and a lower cost allocation.
Библиографические ссылки
Adedeji, K. B., Hamam, Y., Abe, B. T., & Abu-Mahfouz, A. M. (2017). Towards achieving a reliable leakage detection and localization algorithm for application in water piping networks: An overview. IEEE Access, 5, 20272-20285. doi: 10.1109/ACCESS.2017.2752802
Hasan, A. H., Alsharify, F. H. A., & Al-Khafaji, Z. (2026, February). An innovative copula-evolutionary strategy for constrained multi-objective optimization. In AIP Conference Proceedings (Vol. 3393, No. 1, p. 070031). AIP Publishing LLC. https://doi.org/10.1063/5.0317929
Rausand, M., Barros, A., & Hoyland, A. (2020). System Reliability Theory: Models, Statistical Methods, and Applications.
Ahmed, A. H., Abd Alsharify, F. H., & Al-Khafaji, Z. (2025). Maximizing reliability in the age of complexity: a novel optimization approach. Baghdad Science Journal, 22(3), 997-1004. DOI: https://doi.org/10.21123/bsj.2024.9894
Mirjalili, S. (2019). Evolutionary algorithms and neural networks. Studies in computational intelligence, 780(1), 43-53.
Alridha, A., & Al-Jilawi, A. S. (2021, March). Mathematical programming computational for solving NP-hardness problem. In Journal of Physics: Conference Series (Vol. 1818, No. 1, p. 012137). IOP Publishing. DOI 10.1088/1742-6596/1818/1/012137
Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). ieee. DOI: 10.1109/ICNN.1995.488968
Sulaiman, H. K., Hassan, A. M., Hammood, H. K., Alridha, A. H., & Al-Khafaji, Z. (2025, March). An evaluation of the reliability optimization problem for the electromagnetic system of an airplane using a comparison of PSO and GA. In AIP Conference Proceedings (Vol. 3264, No. 1, p. 050010). AIP Publishing LLC. https://doi.org/10.1063/5.0259100
Sastry, K., Goldberg, D. E., & Kendall, G. (2013). Genetic algorithms. In Search methodologies: Introductory tutorials in optimization and decision support techniques (pp. 93-117). Boston, MA: Springer US.
Abd Alsharify, F. H., Abdullah, G., Razzak, A. S. A. A., & Al-Khafaji, Z. (2023, July). Solving bi-objective reliability optimization problem of mixed system by firefly algorithm. In 2023 6th International Conference on Engineering Technology and its Applications (IICETA) (pp. 827-830). IEEE. DOI: 10.1109/IICETA57613.2023.10351435
Hameed, A. H., Salman, A. M., & Mousa, E. A. (2023). Numerical optimization software for solving stochastic optimal control. J. Interdiscip. Math, 26(5), 889-895. DOI : 10.47974/JIM-1525
Salman, A. M., Alridha, A., & Hussain, A. H. (2021, March). Some topics on convex optimization. In Journal of Physics: Conference Series (Vol. 1818, No. 1, p. 012171). IOP Publishing. DOI 10.1088/1742-6596/1818/1/012171
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Ahmed, H. A., & Hasan, A. H. (2025). Mathematical Optimization Strategies for address Production Mixing Challenges. Iraqi Journal of Science, 2446-2454. DOI: https://doi.org/10.24996/ijs.2025.66.6.21
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Hameed, A., & Al-Jilawi, A. S. (2022, October). K-cluster combinatorial optimization problems is NP_Hardness problem in graph clustering. In AIP Conference Proceedings (Vol. 2398, No. 1, p. 060034). AIP Publishing LLC. https://doi.org/10.1063/5.0093394
Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 376, 113609. https://doi.org/10.1016/j.cma.2020.113609
Hasan, A. H., & Salman, A. M. (2024, May). Exploring optimization algorithms for challenging multidimensional optimization problems: A comparative approach. In AIP Conference Proceedings (Vol. 3097, No. 1, p. 080025). AIP Publishing LLC. https://doi.org/10.1063/5.0209464
Chopra, N., & Ansari, M. M. (2022). Golden jackal optimization: A novel nature-inspired optimizer for engineering applications. Expert Systems with Applications, 198, 116924. https://doi.org/10.1016/j.eswa.2022.116924
Trojovský, P., & Dehghani, M. (2022). Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications. Sensors, 22(3), 855. https://doi.org/10.3390/s22030855
BIRNBAUM, Z. (1969). On the importance of different components in a multicomponent system. Multivariate Analysis, 11.
Kuo, W., & Zhu, X. (2012). Importance measures in reliability, risk, and optimization: principles and applications. John Wiley & Sons.
Lagaros, N. D., Kournoutos, M., Kallioras, N. A., & Nordas, A. N. (2023). Constraint handling techniques for metaheuristics: a state-of-the-art review and new variants. Optimization and Engineering, 24(4), 2251-2298. https://doi.org/10.1007/s11081-022-09782-9
Kyi, M. T., & Naing, L. L. (2018). Application of Ford-Fulkerson algorithm to maximum flow in water distribution pipeline network. International Journal of Scientific and Research Publications, 8(12), 306-310. http://dx.doi.org/10.29322/IJSRP.8.12.2018.p8441
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