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Reliability Optimization: A Particle Swarm Approach

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Advances in Reliability and System Engineering

Part of the book series: Management and Industrial Engineering ((MINEN))

Abstract

In recent years, substantial efforts related to the applications of Particle Swarm Optimization (PSO) to various areas in engineering problems have been carried out. This chapter briefly gives the details of PSO development and its applications to reliability optimization.

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References

  1. Afshinmanesh, F., Marandi, A., and Rahimi-Kian, A., A novel binary particle swarm optimization method using artificial immune system, in IEEE International Conference on Computer as a Tool, 2005, 217-220.

    Google Scholar 

  2. Alatas, B. and Akin, E., Multi-objective rule mining using a chaotic particle swarm optimization algorithm, Knowledge-Based Systems, 22, 2009, 455-460.

    Google Scholar 

  3. Al-kazemi, B. and Mohan, C. K., Multi-phase discrete particle swarm optimization, in Fourth International Workshop on Frontiers in Evolutionary Algorithms, 2002.

    Google Scholar 

  4. AlRashidi, M. R. and El-Hawary, M. E., Emission-economic dispatch using a novel constraint handling particle swarm optimization strategy, in Canadian Conference on Electrical and Computer Engineering, 2006, 664-669.

    Google Scholar 

  5. Altiparmak, F., Dengiz, B., and Smith, A. E., Reliability optimization of computer communication networks using genetic algorithms, in IEEE International Conference on Systems, Man, and Cybernetics,1998, 4676-4681.

    Google Scholar 

  6. Arumugam, M. S and Rao, M. V. C., On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems, Applied Soft Computing, 8, 2008, 324-336.

    Google Scholar 

  7. Ashrafi, N. and Berman, O., Optimization models for selection of programs, considering cost and reliability, IEEE Transactions on Reliability, 41, 1992, 281-287.

    Google Scholar 

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

    Google Scholar 

  9. Bala, R. and Aggarwal, K. K., A simple method for optimal redundancy allocation for complex networks, Microelectronics Reliability, 27, 1987, 835-837.

    Google Scholar 

  10. Banks, A., Vincent, J., and Anyakoha, C., A review of particle swarm optimization. Part I: Background and Development, Natural Computing, 6, 2007, 467-484.

    Google Scholar 

  11. Banks, A., Vincent, J., and Anyakoha, C., A review of particle swarm optimization. Part II: Hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications, Natural Computing, 7, 2008, 109-124.

    Google Scholar 

  12. Bartz-Beielstein, T., Limbourg, P., Mehnen, J., Schmitt, K., Parsopoulos, K. E., and Vrahatis, M. N., Particle swarm optimizers for Pareto optimization with enhanced archiving techniques, in Congress on Evolutionary Computation, 2003, 1780-1787.

    Google Scholar 

  13. Briza, A. C. and Naval Jr, P. C, Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data, Applied Soft Computing, 11, 2011, 1191-1201.

    Google Scholar 

  14. Cai, J., Ma, X., Li, Q., Li, L., and Peng, H., A multi-objective chaotic particle swarm optimization for environmental/economic dispatch, Energy Conversion and Management, 50, 2009, 1318-1325.

    Google Scholar 

  15. Cao, C. H., Li, W. H., Zhang, Y. J., and Yi, R. Q., The geometric constraint solving based on memory particle swarm algorithm, in International Conference on Machine Learning and Cybernetics, 2004, 2134-2139.

    Google Scholar 

  16. Carlisle, A. and Dozier, G., Adapting particle swarm optimization to dynamic environments, in International Conference on Artificial Intelligence, 2000, 429-434.

    Google Scholar 

  17. Chatterjee, A. and Siarry, P., Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization, Computers & Operations Research, 33, 2006, 859-871.

    Google Scholar 

  18. Chen, T. C., Penalty guided PSO for reliability design problems, in PRICAI 2006: Trends in Artificial Intelligence, 2006, 777-786.

    Google Scholar 

  19. Chern, M. S., On the computational complexity of reliability redundancy allocation in a series system, Operations Research Letters, 11, 1992, 309-315.

    Google Scholar 

  20. Clerc, M. and Kennedy, J., The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation, 6, 2002, 58-73.

    Google Scholar 

  21. Clow, B. and White, T. An evolutionary race: A comparison of genetic algorithms and particle swarm optimization used for training neural networks, in International Conference on Artificial Intelligence, 2004, 582-588.

    Google Scholar 

  22. Coelho, J. P., Oliviera, P. M., and Cunha, J. B., Non-linear concentration control system design using a new adaptive PSO, in 5th Portugese Conference on Automatic Control, 2002.

    Google Scholar 

  23. Coelho, L. S., An efficient particle swarm approach for mixed-integer programming in reliability-redundancy optimization applications, Reliability Engineering & System Safety, 94, 2009, 830-837.

    Google Scholar 

  24. Coello, C. A.C. and Lechuga, M. S, MOPSO: A proposal for multiple objective particle swarm optimization, in Congress on Evolutionary Computation, 2002, 1051-1056.

    Google Scholar 

  25. Coello, C. A.C., Pulido, G. T., and Lechuga, M. S., Handling multiple objectives with particle swarm optimization, IEEE Transactions on Evolutionary Computation, 8, 2004, 256-279.

    Google Scholar 

  26. Coit, D. W. and Baheranwala, F., Solution of stochastic multi-objective system reliability design problems using genetic algorithms, in European Safety and Reliability Conference, 2005, 391-398.

    Google Scholar 

  27. Coit, D. W. and Konak, A., Multiple weighted objectives heuristic for the redundancy allocation problem, IEEE Transactions on Reliability, 55, 2006, 551-558.

    Google Scholar 

  28. Coit, D. W. and Liu, J. C., System reliability optimization with k-out-of-n subsystems, International Journal of Reliability Quality and Safety Engineering, 7, 2000, 129-142.

    Google Scholar 

  29. Coit, D. W. and Smith, A. E., Considering risk profiles in design optimization for series-parallel systems, in Annual Reliability and Maintainability Symposium,1997, 271-277.

    Google Scholar 

  30. Coit, D. W. and Smith, A. E., Reliability optimization of series-parallel systems using a genetic algorithm, IEEE Transactions on Reliability, 45, 1996a, 254-260.

    Google Scholar 

  31. Coit, D. W. and Smith, A. E., Penalty guided genetic search for reliability design optimization, Computers & Industrial Engineering, 30, 1996b, 895-904.

    Google Scholar 

  32. Coit, D. W., T. Jin, T., and Wattanapongsakorn, N., System optimization with component reliability estimation uncertainty: A multi-criteria approach, IEEE Transactions on Reliability, 53, 2004, 369-380.

    Google Scholar 

  33. De Carvalho, A. B., Pozo, A., and Vergilio, S. R., A symbolic fault-prediction model based on multiobjective particle swarm optimization, Journal of Systems and Software, 83, 2010, 868-882.

    Google Scholar 

  34. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 2002, 182-197.

    Google Scholar 

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

    Google Scholar 

  36. Deeter, D. L. and Smith, A. E., Heuristic optimization of network design considering all-terminal reliability, in Annual Reliability and Maintainability Symposium, 1997, 194-199.

    Google Scholar 

  37. Dhingra, A. K., Optimal apportionment of reliability and redundancy in series systems under multiple objectives, IEEE Transactions on Reliability, 41, 1992, 576-582.

    Google Scholar 

  38. Dian, P. R, Siti, M. S., and Siti, S. Y., Particle Swarm Optimization: Technique, System and Challenges, International Journal of Computer Applications, 14, 2011, 19-27.

    Google Scholar 

  39. Du, W. and Li, B., Multi-strategy ensemble particle swarm optimization for dynamic optimization, Information sciences, 178, 2008, 3096-3109.

    Google Scholar 

  40. Eberhart, R. and Shi, Y., Comparing inertia weights and constriction factors in particle swarm optimization, in IEEE Congress on Evolutionary Computation, 2000, 84-88.

    Google Scholar 

  41. Eberhart, R. and Shi, Y., Tracking and optimizing dynamic systems with particle swarms, in IEEE Congress on Evolutionary Computation, 2001, 94-100.

    Google Scholar 

  42. Eberhart, R., Simpson, P., and Dobbins, R., Computational intelligence PC tools. Academic Press Professional, Inc., USA, 1996.

    Google Scholar 

  43. Engelbrecht, A. P. Fundamentals of computational swarm intelligence, Jhon Wiley & Sons Ltd., 2005.

    Google Scholar 

  44. Engelbrecht, A. P. and van Loggerenberg, Enhancing the NichePSO, in IEEE Congress on Evolutionary Computation, 2007, 2297-2302.

    Google Scholar 

  45. Fan, S. and Chiu, Y., A decreasing inertia weight particle swarm optimizer, Engineering Optimization, 39, 2007, 203-228.

    Google Scholar 

  46. Feng, Y., Teng, G. F., Wang, A. X., and Yao, Y. M., Chaotic inertia weight in particle swarm optimization, in International Conference on Innovative Computing, Information and Control, 2007, 475-475.

    Google Scholar 

  47. Feng, Y., Yao, Y. M., and Wang, A. X., Comparing with chaotic inertia weights in particle swarm optimization, in Conference on Machine Learning and Cybernetics, International, 2007, 329-333.

    Google Scholar 

  48. Fieldsend, J. E. and Singh, S., A Multi-objective algorithm based upon particle swarm optimisation, an efficient data structure and turbulence., Workshop on Computational Intelligence, Birmingham, UK, 2002, 37–44,

    Google Scholar 

  49. Fieldsend, J. E., Multi-objective particle swarm optimization methods, Department of Computer Science, University of Exeter, 2004.

    Google Scholar 

  50. Goh, C. K., Tan, K. C., Liu, D. S., and Chiam, S. C., A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design, European Journal of Operational Research, 202, 2010, 42-54.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  53. Hodgson, R. J. W. Particle swarm optimization applied to the atomic cluster optimization problem, in Genetic and evolutionary computation conference, 2002, 68–73.

    Google Scholar 

  54. Hu, X. and Eberhart, R., Adaptive particle swarm optimization: Detection and response to dynamic systems, in Congress on Evolutionary Computation, 2002a, 1666-1670.

    Google Scholar 

  55. Hu, X. and Eberhart, R., Multiobjective optimization using dynamic neighborhood particle swarm optimization, in Congress on Evolutionary Computation, 2002b, 1677-1681.

    Google Scholar 

  56. Hu, X. and Eberhart, R., Solving constrained nonlinear optimization problems with particle swarm optimization, in World Multiconference on Systemics, Cybernetics and Informatics, 2002c, 203–206.

    Google Scholar 

  57. Hu, X. and Eberhart, R., Tracking dynamic systems with PSO: Where’s the cheese, in the Workshop on Particle Swarm Optimization, Indianapolis, 2001, 80–83.

    Google Scholar 

  58. Hu, X., Y. Shi, and R. Eberhart, Recent advances in particle swarm, in IEEE Congress on Evolutionary Computation, 2004, 90-97.

    Google Scholar 

  59. Huang, H. Z., Qu, J., and Zuo, M. J., A new method of system reliability multi-objective optimization using genetic algorithms, in Annual Reliability and Maintainability Symposium, 2006, 278-283.

    Google Scholar 

  60. Jiao, B., Lian, Z., and Gu, X., A dynamic inertia weight particle swarm optimization algorithm, Chaos, Solitons & Fractals, 37, 2008, 698-705.

    Google Scholar 

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

    Google Scholar 

  62. Kim, J. H. and Yum, B. J., A heuristic method for solving redundancy optimization problems in complex systems, IEEE Transactions on Reliability, 42, 1993, 572-578.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  65. Knowles, J. D. and Corne, D. W., Approximating the nondominated front using the Pareto archived evolution strategy, Evolutionary computation, 8, 2000, 149-172.

    Google Scholar 

  66. Kulturel-Konak, S., Smith, A. E., and Coit, D. W., Efficiently solving the redundancy allocation problem using tabu search, IIE transactions, 35, 2003, 515-526.

    Google Scholar 

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

    Google Scholar 

  68. Kumar, A. & Singh, S.B. (2008). Reliability analysis of an n-unit parallel standby system under imperfect switching using copula, Computer Modelling and New Technologies, 12(1), 2008, 47-55.

    Google Scholar 

  69. Kuo, W. and Wan, R., Recent advances in optimal reliability allocation, IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 37, 2007, 1-36.

    Google Scholar 

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

    Google Scholar 

  71. Laskari, E. C., Parsopoulos, K. E., and Vrahatis, M. N., Particle swarm optimization for integer programming, in IEEE Congress on Evolutionary Computation, 2002, 1582-1587.

    Google Scholar 

  72. Lei, K., Qiu, Y., and He, Y., A new adaptive well-chosen inertia weight strategy to automatically harmonize global and local search ability in particle swarm optimization, in International Symposium on Systems and Control in Aerospace and Astronautics, 2006, 977-980.

    Google Scholar 

  73. Leong, W. F. and Yen, G. G., PSO-based multiobjective optimization with dynamic population size and adaptive local archives, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 38, 2008, 1270-1293.

    Google Scholar 

  74. Levitin, G., Hu, X., and Dai, Y. S., Particle Swarm Optimization in Reliability Engineering, Intelligence in Reliability Engineering, 2007, 83-112.

    Google Scholar 

  75. Li, D. and Haimes, Y. Y., A decomposition method for optimization of large-system reliability, IEEE Transactions on Reliability, 41, 1992, 183-188.

    Google Scholar 

  76. Li, X. and Deb, K., Comparing lbest PSO niching algorithms using different position update rules, in IEEE Congress on Evolutionary Computation ,2010, 1-8.

    Google Scholar 

  77. Li, X., A non-dominated sorting particle swarm optimizer for multiobjective optimization, in Genetic and Evolutionary Computation, 2003, 198-198.

    Google Scholar 

  78. Liang, Y. C. and Chen, Y. C., Redundancy allocation of series-parallel systems using a variable neighborhood search algorithm, Reliability Engineering & System Safety, 92, 2007, 323-331.

    Google Scholar 

  79. Liu, D., Tan, K. C., Goh, C. K., and Ho, W. K., A multiobjective memetic algorithm based on particle swarm optimization, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 37, 2007, 42-50.

    Google Scholar 

  80. Liu, X., Liu, H., and Duan, H., Particle swarm optimization based on dynamic niche technology with applications to conceptual design, Advances in Engineering Software, 38, 2007, 668-676.

    Google Scholar 

  81. Luus, R., Optimization of system reliability by a new nonlinear integer programming procedure, IEEE Transactions on Reliability, 24, 1975, 14-16.

    Google Scholar 

  82. Mahapatra, G. S. and Roy, T. K., Fuzzy multi-objective mathematical programming on reliability optimization model, Applied mathematics and computation, 174, 2006, 643-659.

    Google Scholar 

  83. Mahapatra, G.S., Reliability optimization of entropy based series-parallel system using global criterion method, Intelligent Information Management, 1, 2009, 145-149.

    Google Scholar 

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

    Google Scholar 

  85. Marseguerra, M., E. Zio, E., Podofillini, L., and Coit, D. W, Optimal design of reliable network systems in presence of uncertainty, IEEE Transactions on Reliability,, 54, 2005, 243-253.

    Google Scholar 

  86. Marseguerra, M., Zio, E., and Bosi, F., Direct Monte Carlo availability assessment of a nuclear safety system with time-dependent failure characteristics, International Conference on Mathematical Methods in Reliability, 2002, 429-432.

    Google Scholar 

  87. Misra, K. B. and Sharma, U., An efficient algorithm to solve integer-programming problems arising in system-reliability design, IEEE Transactions on Reliability, 40, 1991a, 81-91.

    Google Scholar 

  88. Misra, K. B. and Sharma, U., An efficient approach for multiple criteria redundancy optimization problems, Microelectronics Reliability, 31, 1991b, 303-321.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  91. Moore, J. and Chapman, R., Application of Particle Swarm to Multi-Objective Optimization: Department of Comput. Sci. Software Eng., Auburn University, 1999.

    Google Scholar 

  92. Mostaghim, S. and Teich, J., Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO), in IEEE Swarm Intelligence Symposium, 2003, 26-33.

    Google Scholar 

  93. Munoz, H. and Pierre, E., Interval arithmetic optimization technique for system reliability with redundancy, in International Conference on Probabilistic Methods Applied to Power Systems, 2004, 227-231.

    Google Scholar 

  94. Nickabadi, A., Ebadzadeh, M. M., and Safabakhsh, R., A novel particle swarm optimization algorithm with adaptive inertia weight, Applied Soft Computing, 11, 2011, 3658-3670.

    Google Scholar 

  95. Nickabadi, A., Ebadzadeh, M. M., and Safabakhsh, R., DNPSO: A dynamic niching particle swarm optimizer for multi-modal optimization, in. IEEE Congress on Evolutionary Computation, 2008, 26-32.

    Google Scholar 

  96. Onishi, J., Kimura, S., James, R. J.W., and Nakagawa, Y., Solving the redundancy allocation problem with a mix of components using the improved surrogate constraint method, IEEE Transactions on Reliability, 56, 2007, 94-101.

    Google Scholar 

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

    Google Scholar 

  98. Pandey, M. K., Tiwari, M. K., and Zuo, M. J., Interactive enhanced particle swarm optimization: A multi-objective reliability application, in Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 221, 177-191, 2007.

    Google Scholar 

  99. Panigrahi, B. K., Ravikumar Pandi, V., and Das, S., Adaptive particle swarm optimization approach for static and dynamic economic load dispatch, Energy conversion and management, 49, 2008, 1407-1415.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  102. Pant, S., Kumar, A., Kishor, A., Anand, D., and Singh, S.B., Application of a Multi-Objective Particle Swarm optimization Technique to Solve Reliability Optimization Problem, In the proceeding of IEEE Int. Conf. on Next generation Computing Technologies, 2015, 1004-1007.

    Google Scholar 

  103. Parsopoulos, K. E. and Vrahatis, M. N., Particle swarm optimization method for constrained optimization problems, Intelligent technologies–theory and application: New trends in intelligent technologies, 2002a, 214–220.

    Google Scholar 

  104. Parsopoulos, K. E. and Vrahatis, M. N., Recent approaches to global optimization problems through particle swarm optimization, Natural computing, 1, 2002b, 235-306.

    Google Scholar 

  105. Parsopoulos, K. E. and Vrahatis, M. N., Unified particle swarm optimization for tackling operations research problems, in IEEE Swarm Intelligence Symposium, 2005, 53-59.

    Google Scholar 

  106. Parsopoulos, K. E., Tasoulis, D. K., and Vrahatis, M. N., Multiobjective optimization using parallel vector evaluated particle swarm optimization, in International conference on artificial intelligence and applications, 2004, 2, 823-828.

    Google Scholar 

  107. Prasad, R. and Raghavachari, M., Optimal allocation of interchangeable components in a series-parallel system, IEEE Transactions on Reliability, 47,1998, 255-260.

    Google Scholar 

  108. Prasad,V. R. and Kuo, W., Reliability optimization of coherent systems, IEEE Transactions on Reliability, 49, 2000, 323-330.

    Google Scholar 

  109. Pulido, G. T. and Coello C.A.C., Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer, in Genetic and Evolutionary Computation Conference , 2004, 225-237.

    Google Scholar 

  110. Qin, Z., Yu, F., Shi, Z., and Wang, Y., Adaptive inertia weight particle swarm optimization, in International conference on Artificial Intelligence and Soft Computing, 2006, 450-459.

    Google Scholar 

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

    Google Scholar 

  112. Raquel, C. R. and Naval Jr, P. C., An effective use of crowding distance in multiobjective particle swarm optimization, in Genetic and evolutionary computation conference, 2005, 257-264.

    Google Scholar 

  113. Ravi, V., Modified great deluge algorithm versus other metaheuristics in reliability optimization, Computational Intelligence in Reliability Engineering, 40, 2007, 21-36.

    Google Scholar 

  114. Ravi, V., Murty, B. S. N., and J. Reddy, Nonequilibrium simulated-annealing algorithm applied to reliability optimization of complex systems, IEEE Transactions on Reliability, 46, 1997, 233-239.

    Google Scholar 

  115. Ravi, V., Optimization of complex system reliability by a modified great deluge algorithm, Asia-Pacific Journal of Operational Research, 21, 2004, 487–497.

    Google Scholar 

  116. Ravi, V., Reddy, P. J., and Zimmermann, H. J., Fuzzy global optimization of complex system reliability, IEEE Transactions on Fuzzy Systems, 8, 2000, 241-248.

    Google Scholar 

  117. Ray, T. and Liew, K. M., A swarm metaphor for multiobjective design optimization, Engineering Optimization, 34, 2002, 141-153.

    Google Scholar 

  118. Reddy, M. J. and Kumar, D. N., An efficient multi-objective optimization algorithm based on swarm intelligence for engineering design, Engineering Optimization, 39, 2007, 49-68.

    Google Scholar 

  119. Reibman, A. L. and Veeraraghavan, M., Reliability modeling: An overview for system designers, Computer, 24, 1991, 49-57.

    Google Scholar 

  120. Reklaitis, G. V., Ravindran, A. and Ragsdell, K. M., Engineering optimization, methods and applications. John Wiley & Sons, 1983.

    Google Scholar 

  121. Reyes-Sierra, M. and Coello, C. A.C., Multi-objective particle swarm optimizers: A survey of the state-of-the-art, International Journal of Computational Intelligence Research, 2, 2006, 287-308.

    Google Scholar 

  122. Saber, A. Y., Senjyu, T., Yona, A., and Funabashi, T., Unit commitment computation by fuzzy adaptive particle swarm optimisation, Generation, Transmission & Distribution, IET, 1, 2007, 456-465.

    Google Scholar 

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

    Google Scholar 

  124. Sakawa, M., Optimal reliability-design of a series-parallel system by a large-scale multiobjective optimization method, IEEE Transactions on Reliability, 30, 1981, 173-174.

    Google Scholar 

  125. Salazar, D, E., Rocco, S., and Claudio, M., Solving advanced multi-objective robust designs by means of multiple objective evolutionary algorithms (MOEA): A reliability application, Reliability Engineering & System Safety, 92, 2007, 697-706.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  128. Shi, Y. and Eberhart, R., A modified particle swarm optimizer, in IEEE World Congress on Evolutionary Computational, 1998, 69-73.

    Google Scholar 

  129. Shi, Y. and Eberhart, R., Empirical study of particle swarm optimization, in Congress on Evolutionary Computation, 3, 1999a, 1945- 1950.

    Google Scholar 

  130. Shi, Y. and Eberhart, R., Experimental study of particle swarm optimization, in World Multiconf. Systematica, Cybernatics and Informatics, 2000.

    Google Scholar 

  131. Shi, Y. and Eberhart, R., Fuzzy adaptive particle swarm optimization, in Congress on Evolutionary Computation, 2001, 101-106.

    Google Scholar 

  132. Shi, Y. and Eberhart, R., Parameter selection in particle swarm optimization, in Annual Conference on Evolutionary Programming, 1998b, 25-27.

    Google Scholar 

  133. Sierra, M. R. and Coello, C. A.C., Improving PSO-based multi-objective optimization using crowding, mutation and e-dominance, in International Conference on Evolutionary Multi-Criterion Optimization, 2005, 505-519.

    Google Scholar 

  134. Sivasubramani, S. and Swarup, K., Multiagent based particle swarm optimization approach to economic dispatch with security constraints, in International Conference on Power Systems, 2009, 1-6.

    Google Scholar 

  135. Sun, C., Liang, H., Li, L., and Liu, D., Clustering with a Weighted Sum Validity Function Using a Niching PSO Algorithm, in IEEE International Conference on, Networking, Sensing and Control, 2007, 368-373.

    Google Scholar 

  136. Sun, H., Han, J. J. and Levendel, H., A generic availability model for clustered computing systems, in Pacific Rim International Symposium on Dependable Computing, 2001, 241-248.

    Google Scholar 

  137. Sun, L. and Gao, X., Improved chaos-particle swarm optimization algorithm for geometric constraint solving, in International Conference on Computer Science and Software Engineering, 2008, 992-995.

    Google Scholar 

  138. Suresh, K., Ghosh, S., Kundu, D., Sen, A., Das, S., and Abraham, A., Inertia-adaptive particle swarm optimizer for improved global search, in International Conference on Intelligent Systems Design and Applications, 2008, 253-258.

    Google Scholar 

  139. Taboada, H. and Coit, D. W., Data clustering of solutions for multiple objective system reliability optimization problems, Quality Technology & Quantitative Management Journal, 4, 2007, 35-54.

    Google Scholar 

  140. Tillman, F. A., Hwang, C. L., and Kuo,W., Optimization of systems reliability, Marcel Dekker Inc., 1980.

    Google Scholar 

  141. Tripathi, P. K., Bandyopadhyay, S., and Pal, S. K., Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients, Information Sciences, 177, , 2007, 5033-5049.

    Google Scholar 

  142. Twum, S. B., Multicriteria optimisation in design for reliability, Ph.D. Thesis, University of Birmingham, 2009.

    Google Scholar 

  143. Vinod, G., Kushwaha, H. S., Verma, A. K., and Srividya, A., Optimisation of ISI interval using genetic algorithms for risk informed in-service inspection, Reliability Engineering & System Safety, 86, 2004, 307-316.

    Google Scholar 

  144. Wang, J., Liu, D., and Shang, H., Hill valley function based niching particle swarm optimization for multimodal functions, in International Conference on Artificial Intelligence and Computational Intelligence, 2009, 139-144.

    Google Scholar 

  145. Wattanapongsakorn, N. and Levitan, S. P., Reliability optimization models for embedded systems with multiple applications, IEEE Transactions on Reliability, 53, 2004, 406-416.

    Google Scholar 

  146. Wattanapongsakorn, N. and Levitan, S., Reliability optimization models for fault-tolerant distributed systems, in Reliability and Maintainability Symposium, 2001,193-199.

    Google Scholar 

  147. Wattanapongskorn, N. and Coit, D. W, Fault-tolerant embedded system design and optimization considering reliability estimation uncertainty, Reliability Engineering & System Safety, 92, 2007, 395-407.

    Google Scholar 

  148. Xu, Z., Kuo, W., and Lin, H. H., Optimization limits in improving system reliability, IEEE Transactions on Reliability, 39, 1990, 51-60.

    Google Scholar 

  149. Yalaoui, A., Châtelet, E., and Chu, C., A new dynamic programming method for reliability & redundancy allocation in a parallel-series system, IEEE Transactions on Reliability, 54, 2005, 254-261.

    Google Scholar 

  150. Yamachi, H., Tsujimura, Y., Kambayashi, Y., and Yamamoto, H., Multi-objective genetic algorithm for solving N-version program design problem, Reliability Engineering & System Safety, 91, 2006, 1083-1094.

    Google Scholar 

  151. Yang, X., Yuan, J., Yuan, J., and Mao, H., A modified particle swarm optimizer with dynamic adaptation, Applied Mathematics and Computation, 189, 2007, 1205-1213.

    Google Scholar 

  152. Yeh, W. C., A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems, Expert Systems with Applications, 36, 2009, 9192-9200.

    Google Scholar 

  153. You, P. S. and Chen, T. C., An efficient heuristic for series-parallel redundant reliability problems, Computers & Operations research, 32, 2005, 2117-2127.

    Google Scholar 

  154. Zafiropoulos, E. P. and Dialynas, E. N., Methodology for the optimal component selection of electronic devices under reliability and cost constraints, Quality and Reliability Engineering International, 23, 2007, 885-897.

    Google Scholar 

  155. Zavala, A. E.M., Diharce, E. R.V., and Aguirre, A. H., Particle evolutionary swarm for design reliability optimization, in Evolutionary multi-criterion optimization. Third international conference, EMO 2005. Lecture notes in computer science, Coello Coello CA, Aguirre AH, Zitzler E (eds) , Springer, Guanajuato, Mexico, 3410, 2005, 856-869.

    Google Scholar 

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

    Google Scholar 

  157. Zhao, S. Z., Liang, J. J., Suganthan, P. N., and Tasgetiren, M. F., Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization, in IEEE Congress on Evolutionary Computation, 2008, 3845-3852.

    Google Scholar 

  158. Zheng, Y., Ma, L., Zhang, L. and Qian, J., On the convergence analysis and parameter selection in particle swarm optimization, in International Conference on Machine Learning and Cybernetics, 2003b, 1802-1807.

    Google Scholar 

  159. Zheng, Y., Ma, L., Zhang, L., and Qian, J., Empirical study of particle swarm optimizer with an increasing inertia weight, in IEEE Congress on Evolutionary Computation, 2003a, 221-226.

    Google Scholar 

  160. Zou, D., Wu, J., Gao, L., and Wang, X., A modified particle swarm optimization algorithm for reliability problems, in IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2010, 1098-1105.

    Google Scholar 

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Pant, S., Kumar, A., Ram, M. (2017). Reliability Optimization: A Particle Swarm Approach. In: Ram, M., Davim, J. (eds) Advances in Reliability and System Engineering. Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-48875-2_7

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