By Gregory Levitin
This quantity includes chapters proposing purposes of alternative metaheuristics (ant colony optimization, nice deluge set of rules, cross-entropy process and particle swarm optimization) in reliability engineering. additionally it is chapters dedicated to mobile automata and help vector machines and diversified functions of synthetic neural networks, a robust adaptive method that may be used for studying, prediction and optimization. a number of chapters describe diverse facets of obscure reliability and purposes of fuzzy and obscure set conception.
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Additional info for Computational Intelligence in Reliability Engineering New Metaheuristics Neural and Fuzzy Techniques in Reliability
M. 1 Simulated Annealing (SA) SA is developed based on the principles of statistical mechanics. It found a number of applications in diverse disciplines such as science, engineering and economics in finding global solution to highly nonlinear constrained optimization problems and combinatorial optimization problems. SA is very much analogous to the physical process of annealing. Annealing refers to the physical thermal process of melting a solid by first heating it and then cooloing it slowly in order to allow the molecules in the material to attain the lowest energy level (stable or ground state).
Yeh et al.  proposed a method based on a Genetic Algorithm to optimize the k-node set reliability subject to a specified capacity constraint. Reichelt et al. used Applications of the Cross-Entropy Method in Reliability 39 a Genetic Algorithm in combination with a repair heuristic to minimize the network cost with specified network reliability constraints . We show that the Cross-Entropy (CE) method  provides an effective way to solve both the network reliability estimation and the network planning/improvement problems.
It was soon realized [27, 29] that it could be used not only for rare event simulations but for solving difficult combinatorial optimization problems as well. Moreover, the CE method is well-suited for solving noisy optimization problems. org. The rest of the chapter is organized as follows. 2. 3. 4, we present how the CE method can be applied to improve the Monte Carlo simulations. 5 using the CE method. 6, we show how to adapt the CE method to a generalized optimization problem in the context of network recovery and extension.