Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

Ganga Negi; Anuj Kumar; Sangeeta Pant; Mangey Ram

doi:10.31181/dmame210402241n

Authors

Ganga Negi Department of Mathematics, Graphic Era Deemed to be University, Dehradun, India
Anuj Kumar Department of Mathematics, University of Petroleum and Energy Studies, Dehradun, India
Sangeeta Pant Department of Mathematics, University of Petroleum and Energy Studies, Dehradun, India
Mangey Ram Department of Mathematics, Computer Science and Engineering, Graphic Era, Dehradun, India

DOI:

https://doi.org/10.31181/dmame210402241n

Keywords:

Cost function, Metaheuristics, Reliability allocation problems, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Hybrid PSO-GWO algorithm (HPSOGWO).

Abstract

Reliability allocation to increase the total reliability has become a successful way to increase the efficiency of the complex industrial system designs. A lot of research in the past have tackled this problem to a great extent. This is evident from the different techniques developed so far to achieve the target. Stochastic metaheuristics like simulated annealing, Tabu search (TS), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Genetic Algorithm (GA), Grey wolf optimization technique (GWO) etc. have been used in recent years. This paper proposes a framework for implementing a hybrid PSO-GWO algorithm for solving some reliability allocation and optimization problems. A comparison of the results obtained is done with the results of other well-known methods like PSO, GWO, etc. The supremacy/competitiveness of the proposed framework is demonstrated from the numerical experiments. These results with regard to the time taken for the computation and quality of solution outperform the previously obtained results by the other well-known optimization methods.

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References

Ab Rashid, M.F.F. (2017). A hybrid Ant-Wolf Algorithm to optimize assembly sequence planning problem. Assembly Automation, 37(2), 238–248.

Abd‐Elazim, S. M., & Ali, E. S. (2015). A hybrid particle swarm optimization and bacterial foraging for power system stability enhancement. Complexity, 21(2), 245-255.

Abdullah, J. M. & Ahmed, T. (2019). Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process. IEEE Access, 7, 43473-43486.

Ahmed, A., Esmin, A. & Matwin, S. (2013). HPSOM: a hybrid particle swarm optimization algorithm with genetic mutation. International Journal of Innovative Computing, Information and Control, 9(5), 1919–1934.

Atiqullah, M. M., & Rao, S.S. (1993). Reliability optimization of communication networks using simulated annealing. Microelectronics Reliability, 33, 1303-1319.

dos Santos Coelho, L. (2009). An efficient particle swarm approach for mixed-integer programming in reliability–redundancy optimization applications. Reliability Engineering & System Safety, 94(4), 830-837.

Deep, K., & Deepti. D. (2009). Reliability Optimization of Complex Systems through C-SOMGA. Journal of Information and Computing Science, 4, 163-172.

Dorigo, M., & Gambardella, L.M. (1997). Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput, 1(1), 53–66.

Eberhart, R. & Kennedy, J. (1995). A new optimizer using particle swarm theory. MHS'95. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 39-43, doi: 10.1109/MHS.1995.494215.

Eiben, A. E. & Schippers, C.A. (1998). On evolutionary exploration and exploitation. Fundamental Informatics, 35(1–4), 35–50.

Fouad, M.M, Hafez, A.I., Hassanien, A.E. & Snasel, V. (2015). Grey wolves optimizer-based localization approach in WSNs. In: 11th international computer engineering conference (ICENCO). IEEE, 256–260.

Hassan, B. A., & Rashid, T. A. (2021). A multidisciplinary ensemble algorithm for clustering heterogeneous datasets. Neural Computing and Applications, 33(17), 10987-11010.

Hikita, M., Nakagawa, Y., Nakashima, K., & Narihisa, H. (1992). Reliability optimization of systems by a surrogate-constraints algorithm. IEEE Transactions on Reliability, 41, 473-480.

Hikita, M., Nakagawa, Y., Nakashima, K., & Yamato, K. (1986). Application of the surrogate constraints algorithm to optimal reliability design of systems. Microelectronics and reliability,26, 35-38.

Holden, N., & Freitas, A.A. (2008). A hybrid PSO/ACO algorithm for discovering classification rules in data mining. Journal of Artificial Evolution and Applications. Article ID 316145, 1-11.

Hu, X., & Eberhart, R. (2002), Adaptive particle swarm optimization: Detection and response to dynamic systems. In Congress on Evolutionary Computation, 2, 1666-1670.

Ibrahim, G.J., Rashid, T. A., & Akinsolu, M. O. (2020). An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment. Journal of parallel and distributed computing, 143.

Jayabarathi, T., Raghunathan, T., Adarsh, B.R., & Suganthan, P. N. (2016). Economic dispatch using hybrid grey wolf optimizer. Energy, 111, 630–641.

Kennedy, J., & Eberhart, R. (1997). A discrete binary version of the particle swarm algorithm, in IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, 5, 4104-4108.

Kishor, A., Yadav, S. P., & Kumar, S. (2007). Application of a Multi-objective Genetic Algorithm to solve Reliability Optimization Problem, In International Conference on Computational Intelligence and Multimedia Applications, 458-462.

Kishor, A., Yadav, S. P., & Kumar, S. (2009). A Multi-objective Genetic Algorithm for Reliability Optimization Problem. International Journal of Performability Engineering, 5, 227–234.

Kumar, A., Pant, S., & Ram, M. (2017). System Reliability Optimization Using Grey Wolf Optimizer Algorithm. Quality and Reliability Engineering International, 33(7), 1327-1335.

Kumar, A., Pant, S., & Ram, M. (2019a). Grey wolf-optimizer approach to the reliability‐cost optimization of residual heat removal system of a nuclear power plant safety system. Quality and Reliability Engineering international, 35 (7), 2228-2239.

Kumar, A., Pant, S., & Ram, M. (2019b). Multi-objective grey wolf optimizer approach to the reliability-cost optimization of life support system in space capsule. International Journal of System Assurance Engineering and management, 10 (2), 276-284.

Kumar, A., Pant, S., & Singh, S. B. (2016). Reliability Optimization of Complex System by Using Cuckoos Search algorithm, Mathematical Concepts and Applications in Mechanical Engineering and Mechatronics, IGI Global, 95-112

Kuo, W., & Prasad, V.R. (2000). An annotated overview of system-reliability optimization. IEEE Transactions on Reliability, 49, 176-187.

Li, L., Xue, B., Niu, B., Tan, L., & Wang, J. (2008). A novel PSO-DE based hybrid algorithm for global optimization in Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence, vol. 5227 of Lecture Notes in Computer Science, 785–793, Springer, Berlin, Germany.

Majety, S. R. V., Dawande, M., & Rajgopal, J. (1999). Optimal reliability allocation with discrete cost-reliability data for components. Operations Research, 47, 899-906.

Mirjalili, S. M., & Hashim, S. Z .M. (2010). A new hybrid PSOGSA algorithm for function optimization, in Proceedings of the International Conference on Computer and Information Application (ICCIA ’10), pp. 374–377, Tianjin, China.

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Adv Eng. Soft, 69, 46–61.

Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. S. (2016). Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Sys App, 47, 106–119.

Misra, K. B., & Sharma, U. (1991a). An efficient approach for multiple criteria redundancy optimization problems. Microelectronics Reliability, 31, 303-321.

Misra, K. B., & Sharma, U. (1991b). Multicriteria optimization for combined reliability and redundancy allocation in systems employing mixed redundancies. Microelectronics Reliability, 31, 323-335.

Mohammed, H. M., Abdul, Z. K., Rashid, T. A., Alsadoon, A., & Bacanin, N. (2021). A new K-means grey wolf algorithm for engineering, World Journal of Engineering ISSN: 1708-5284

Mohammed, H. M., Umar, S. U., Tarik A., & Rashid, A. (2019). Systematic and Meta-Analysis Survey of Whale Optimization Algorithm. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2019/8718571.

Mohammed, H., & Rashid, T. (2020). A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design. Neural Computing and Applications, 32, 14701–14718.

Mohan, C., & Shanker. K. (1987). Reliability optimization of complex systems using random search technique. Microelectronics Reliability, 28, 513-518.

Mosavi, M, R., Khishe, M., & Ghamgosar, A. (2016). Classification of sonar data set using neural network trained by grey wolf optimization. Neural Net World, 26(4), 393.

Muhammed, D.A., Saeed, S.A.M., & Rashid, T.A. (2020). Improved Fitness-Dependent Optimizer Algorithm. IEEE, 8.

Mukherjee, A., Barma, P.S., Dutta, J., Panigrahi, G., Kar, S., & Maiti, M. (2021). A multi-objective antlion optimizer for the ring tree problem with secondary sub-depots, https://link.springer.com/article/10.1007/s12351-021-00623-8

Negi, G., Kumar, A., Pant, S., & Ram, M. (2020). GWO: a review and applications, International Journal of System Assurance Engineering and management, https://doi.org/10.1007/s13198-020-00995-8.

Padhye, N., Branke, J. & Mostaghim, S. (2009). Empirical comparison of MOPSO methods-guide selection and diversity preservation, in IEEE Congress on Evolutionary Computation, 2516-2523.

Pant, S. & Singh, S.B. (2011). Particle Swarm Optimization to Reliability Optimization in Complex System, In the proceeding of IEEE Int. Conf. on Quality and Reliability, Bangkok, Thailand, 211-215.

Pant, S, Anand, D., Kishor, A., & Singh, S.B. (2015). A Particle Swarm Algorithm for Optimization of Complex System Reliability. International Journal of Performability Engineering, 11(1), 33-42

Pant, S., Kumar, A., & Ram, M. (2019). Solution of Nonlinear Systems of Equations via Metaheuristics. International Journal of Mathematical, Engineering and Management Sciences, 4 (5), 1108-1126.

Pant, S., Kumar, A. & Ram, M. (2017). Flower Pollination Algorithm Development: A State of Art Review. International Journal of System Assurance Engineering and Management, Springer, 8 (2), 1858-1866.

Pham, H., Pham, H. K, & Amari, S. V. (1995). A general model for evaluating the reliability of telecommunications systems. Commun Reliab Maintain Support-Int J; 2:4–13.

Rahman, C. M., & Rashid, T. A. (2021). A new evolutionary algorithm: Learner performance based behavior algorithm. Egyptian Informatics Journal, 22(2), 213-223.

Ramírez-Rosado, I. J. & Bernal-Agustín, J. L. (2001). Reliability and costs optimization for distribution networks expansion using an evolutionary algorithm. IEEE Transactions on Power Systems, 16, 111-118.

Rashid, T. A., Abbas, D. K., & Turel, Y. K. (2019). A multi hidden recurrent neural network with a modified grey wolf optimizer. PloS one, 14(3), e0213237.

Sakawa, M. (1978). Multi-objective reliability and redundancy optimization of a series-parallel system by the Surrogate Worth Trade-off method. Microelectronics and Reliability, 17, 465-467.

Salazar, D., Rocco, C. M., & Galván, B. J. (2006). Optimization of constrained multiple-objective reliability problems using evolutionary algorithms. Reliability Engineering & System Safety, 91, 1057-1070.

Shamsaldin, A. S., Rashid, T. A., Al-Rashid Agha, R. A., Al-Salihi, N. K., & Mohammadi, M. (2019). Donkey and smuggler optimization algorithm: A collaborative working approach to path finding. Journal of Computational Design and Engineering, 6(4), 562-583.

Shelokar, P. S, Jayaraman, V. K., & Kulkarni, B. D. (2002). Ant algorithm for single and multi-objective reliability optimization problems. Quality and Reliability Engineering International, 18, 497-514.

Singh, N. & Singh, S. B. (2017). Hybrid algorithm of Particle swarm optimization and grey wolf optimizer for improving convergence performance. Journal of Applied Mathematics, Article ID 20304889, 1-16.

Tawhid, M. A. & Ali, A. F. (2017). A Hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function. Memetic Computing, 9(4), 347–359.

Uniyal, N. Pant, S., & Kumar, A. (2020). An Overview of Few Nature Inspired Optimization Techniques and Its Reliability Applications. International Journal of Mathematical, Engineering and Management Sciences, 5 (4), 732-743.

Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on Evolutionary computation; 1, 67-82.

Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee.

Zhao, J. H., Liu, Z., & Dao, M. T. (2007). Reliability optimization using multiobjective ant colony system approaches. Reliability Engineering & System Safety, 92(1), 109-120.