期刊文献+

A New Algorithm for the Weighted Reliability of Networks 被引量:1

A New Algorithm for the Weighted Reliability of Networks
原文传递
导出
摘要 The weighted reliability of network is defined as the sum of the multiplication of the probability of each network state by its normalized weighting factor.Under a certain state, when the capacity from source s to sink t is larger than the given required capacity C r, then the normalized weighting factor is 1, otherwise, it is the ratio of the capacity to the required capacity C r. This paper proposes a new algorithm for the weighted reliability of networks, puts forward the concept of saturated state of capacity, and suggests a recursive formula for expanding the minimal paths to be the sum of qualifying subsets. In the new algorithm, the expansions of the minimal paths dont create the irrelevant qualifying subsets, thus decreasing the unnecessary expanding calculation. Compared with the current algorithms, this algorithm has the advantage of a small amount of computations for computer implementation. The weighted reliability of network is defined as the sum of the multiplication of the probability of each network state by its normalized weighting factor.Under a certain state, when the capacity from source s to sink t is larger than the given required capacity C r, then the normalized weighting factor is 1, otherwise, it is the ratio of the capacity to the required capacity C r. This paper proposes a new algorithm for the weighted reliability of networks, puts forward the concept of saturated state of capacity, and suggests a recursive formula for expanding the minimal paths to be the sum of qualifying subsets. In the new algorithm, the expansions of the minimal paths dont create the irrelevant qualifying subsets, thus decreasing the unnecessary expanding calculation. Compared with the current algorithms, this algorithm has the advantage of a small amount of computations for computer implementation.
出处 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2000年第3期38-43,47,共7页 中国邮电高校学报(英文版)
关键词 weighted reliability capacity minimal paths network flow weighted reliability capacity minimal paths network flow
  • 相关文献

同被引文献16

  • 1KENNEDY J, EBERHART R C. Particle swarm optimization [A]. Proceedings of the 1995 IEEE International Conference on Neural Networks, Vol 4[C]. Perth(Australia), 1995. Piscataway (NJ,USA): IEEE, 1995. 1942-1948.
  • 2EBERHART R C, KENNEDY J. A new optimizer using particle swarm theory [A]. Proceedings of the Sixth International Symposium on Micro Machine and Human Science[C]. Nagoya(Japan), 1995. Piscataway(NJ,USA): IEEE, 1995. 39-43.
  • 3KENNEDY J. The particle swarm: social adaptation of knowledge[A]. Proceedings of IEEE International Conference on Evolutionary Computation[C]. Indianapolis(IN,USA), 1997. Piscataway (NJ,USA): IEEE, 1997. 303-308.
  • 4KENNEDY J, MENDES R. Population structure and particle swarm performance[A]. Proceedings of the 2002 Congress on Evolutionary Computation, Vol 2[C]. Honolulu(HI,USA), 2002. Piscataway (NJ): IEEE,2002.1671-1676.
  • 5SHI Y, EBERHART R C. A modified particle swarm optimizer [A]. Proceedings of the IEEE International Conference on Evolutionary Computation[C]. Anchorage(AK,USA), 1998. Piscataway (NJ,USA): IEEE, 1998. 69-73.
  • 6CLERC M. The swarm and the queen:towards a deterministic and adaptive particle swarm optimization[A]. Proceedings of the Congress of Evolutionary Computation, Vol 3[C]. Washington (DC,USA),1999. Piscataway(NJ,USA): IEEE, 1999. 1951-1957.
  • 7EBERHART R C, SHI Y. Comparing inertia weights and constriction factors in particle swarm optimization[A]. Proceedings of the Congress of Evolutionary Computation, Vol 1 [C]. San Deogo(CA,USA), 2000. Piscataway (NJ,USA):IEEE, 2000. 84-88.
  • 8MENDES R, KENNEDY J, NEVES J. Watch thy neighbor or how the swarm can learn from its environment[A]. Proceedings of the 2003 IEEE of Swarm Intelligence Symposium[C]. Indianapolis(IN,USA), 2003. Piscataway (NJ,USA):IEEE, 2003. 88-94
  • 9KENNEDY J, MENDES R. Neighborhood topologies in fully-informed and best-of-neighborhood particle swarms [A]. Proceedings of the 2003 IEEE International Workshop on Soft Computing in Industrial Applications[C]. Binghamtton(NY,USA), 2003. Piscataway (NJ,USA):IEEE, 2003. 45-50.
  • 10ANGELINE P J. Evolutionary optimization versus particle swarm optimization:philosophy and performance difference [A]. Proceedings of 7th Annual Conference on Evolutionary Programming [C]. San Diego(CA,USA), 1998. Berlin(Germany): Springer-Verlay, 1998. 601-610.

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部