Topological optimization models with communication network for multiple reliability goals. (English) Zbl 0997.90012

Summary: Network reliability models for determining optimal network topology have been presented and solved by many researchers. This paper presents some new types of topological optimization model for communication network with multiple reliability goals. A stochastic simulation-based genetic algorithm is also designed for solving the proposed models. Some numerical examples are finally presented to illustrate the effectiveness of the algorithm.


90B18 Communication networks in operations research
90C90 Applications of mathematical programming
90C15 Stochastic programming


Full Text: DOI


[1] Aggarwal, K. K.; Chopra, Y. C.; Bajwa, J. S., Topological layout of links for optimizing the \(s\) − \(t\) reliability in a computer communication system, Microelectronics and Reliability, 22, 3, 341-345 (1982)
[2] Aggarwal, K. K.; Chopra, Y. C.; Bajwa, J. S., Topological layout of links for optimizing the overall reliability in a computer communication system, Microelectronics and Reliability, 22, 3, 347-351 (1982)
[3] Chopra, Y. C.; Sohi, B. S.; Tiwari, R. K.; Aggarwal, K. K., Network topology for maximizing the terminal reliability in a computer communication network, Microelectronics and Reliability, 24, 911-913 (1984)
[4] Jan, R.-H.; Hwang, F.-J.; Chen, S.-T., Topological optimization of a communication network subject to a reliability constraint, IEEE Transactions on Reliability, 42, 63-70 (1993) · Zbl 0775.90163
[5] Ravi, V.; Murty, B. S.N.; Reddy, P. J., Nonequilibrium simulated annealing-algorithm applied to reliability optimization of complex systems, IEEE Transactions on Reliability, 46, 233-239 (1997)
[6] Painton, L.; Campbell, J., Genetic algorithms in optimization of system reliability, IEEE Transactions on Reliability, 44, 172-178 (1995)
[7] Kumar, A.; Pathak, R. M.; Gupta, Y. P., Genetic-algorithm-based reliability optimization for computer network expansion, IEEE Transactions On Reliability, 44, 63-72 (1995)
[8] Kumar, A.; Pathak, R. M.; Gupta, Y. P.; Parsaei, H. R., A genetic algorithm for distributed system topology design, Computers and Industrial Engineering, 28, 659-670 (1995)
[9] Dengiz, B.; Altiparmak, F.; Smith, A. E., Efficient optimization of all-terminal reliable networks, using an evolutionary approach, IEEE Transactions on Reliability, 46, 1, 18-26 (1997)
[10] Charnes, A.; Cooper, W. W., Chance-constrained programming, Management Science, 6, 1, 73-79 (1959) · Zbl 0995.90600
[11] Liu, B., Dependent-chance programming: A class of stochastic optimization, Computers Math. Applic., 34, 12, 89-104 (1997) · Zbl 0905.90127
[12] Holland, J. H., Adaptation in Natural and Artificial Systems (1975), University of Michigan Press: University of Michigan Press Ann Arbor, MI
[13] Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning (1989), Addison-Wesley · Zbl 0721.68056
[14] Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs (1996), Springer-Verlag: Springer-Verlag New York · Zbl 0841.68047
[15] Iwamura, K.; Liu, B., A genetic algorithm for chance constrained programming, Journal of Information and Optimization Sciences, 17, 2, 409-422 (1996) · Zbl 0873.90075
[16] Liu, B.; Iwamura, K., Chance constrained programming with fuzzy parameters, Fuzzy Sets and Systems, 94, 2, 227-237 (1998) · Zbl 0923.90141
[17] Liu, B.; Iwamura, K., A note on chance constrained programming with fuzzy coefficients, Fuzzy Sets and Systems, 100, 1-3, 229-233 (1998) · Zbl 0948.90156
[18] Liu, B., Dependent-chance goal programming and its genetic algorithm based approach, Mathl. Comput. Modelling, 24, 7, 43-52 (1996) · Zbl 0895.90165
[19] Liu, B., Dependent-chance programming in fuzzy environments, Fuzzy Sets and Systems, 109, 1, 95-104 (2000)
[20] Liu, B., Dependent-chance programming with fuzzy decisions, IEEE Transactions on Fuzzy Systems, 7, 3, 354-360 (1999)
[21] Liu, B., Uncertain Programming (1999), John Wiley and Sons: John Wiley and Sons New York
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