By Gregory Levitin
This quantity comprises chapters featuring functions of other metaheuristics (ant colony optimization, nice deluge set of rules, cross-entropy approach and particle swarm optimization) in reliability engineering. it's also chapters dedicated to mobile automata and help vector machines and varied purposes of synthetic neural networks, a robust adaptive strategy that may be used for studying, prediction and optimization. numerous chapters describe diverse points of obscure reliability and functions of fuzzy and obscure set concept.
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Additional resources for Computational Intelligence in Reliability Engineering New Metaheuristics Neural and Fuzzy Techniques in Reliability
McGraw-Hill, pp 63-76 22. Gen M, Ida K, Tsujimura Y, Kim CE (1993) Large-scale 0-1 fuzzy goal programming and its application to reliability optimization problem. Computers and Industrial Engineering 24(4):539-549 23. Ghare PM, Taylor RE (1969) Optimal redundancy for reliability in series systems. Operations Research 17:838-847 24. Kulturel-Konak S, Coit DW, Smith AE (2003) Efficiently solving the redundancy allocation problem using tabu search. IIE Transactions 35(6):515-526 25. Kuo W, Prasad VR (2000) An annotated overview of system-reliability optimization.
55 . i =1 Table 1 shows a simulation result with 105 samples. With a network failure probability of about two in a million, the CMC with 105 samples cannot estimate r accurately, while the CMC with IS (CMC-IS) delivers a much better result. This simple example demonstrates how Importance Sampling can help improve estimate accuracy. Table 1. 22e-00 However, the question of how we should tilt the parameters still remains open and that is where the CE technique can help.
Later, Ravi et al. (1997) developed an improved version of non-equilibrium simulated annealing called INESA and applied it to solve a variety of reliability optimization problems. Further, Ravi et al. (2000) first formulated various complex system reliability optimization problems with single and multi objectives as fuzzy global optimization problems. They also developed and applied the non-combinatorial version of another meta-heuristic viz. threshold accepting to solve these problems. Threshold accepting (Dueck and Sheurer, 1990) is a faster variation of the simulated annealing and often leads to superior optimal solutions than does the simulated annealing.