By Yun-Chia Liang, Alice E. Smith (auth.), Dr. Gregory Levitin (eds.)
This quantity comprises chapters providing purposes of alternative metaheuristics (ant colony optimization, nice deluge set of rules, cross-entropy procedure and particle swarm optimization) in reliability engineering. it is usually chapters dedicated to mobile automata and aid vector machines and diversified purposes of synthetic neural networks, a strong adaptive method that may be used for studying, prediction and optimization. numerous chapters describe varied features of vague reliability and functions of fuzzy and imprecise set conception.
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Additional resources for Computational Intelligence in Reliability Engineering: New Metaheuristics, Neural and Fuzzy Techniques in Reliability
Thirdly, when Monte Carlo simulation is used to estimate the network reliability, the objective function becomes noisy (random). Finally, for highly reliable networks, sophisticated variance reduction techniques are required to estimate the reliability accurately. The literature on network planning is not extensive, and virtually all studies pertain to networks for which the system reliability can either be evaluated exactly, or sharp reliability bounds can be established. Colbourn and Harms  proposed a technique that provides progressive bounds that eventually converge to an exact reliability value.
Then, Sinclair (1993) applied GDA and RRT to 37 real examples of hydraulic turbine runner balancing problem, which is a special case of quadratic assignment problem. In this study, he compared their performance with that of SA, genetic algorithms and tabu search. For more information on RRT, the reader is referred to Sinclair (1993). The motivation for the GDA is as follows: Imagine the goal is to find the highest point in a country. Assume that it rains incessantly on this piece of land. The algorithm moves around on the uncovered land without getting its feet wet.
We mention two well-know techniques for establishing the structure function. The first is the modular decomposition technique. Often a system consists of combinations of series and parallel structures. The determination of the structure function for such systems can be handled in stages. The following example explains the procedure. Consider the upper-most system in Fig. 2. We can view the system as consisting of component 1 and modules 1* and 2*. This gives the second system in Fig. 2. Similarly, we can view this last system as a series system consisting of component 1 and module 1** (last system in Fig.