By Gregory Levitin
This publication covers the new functions of computational intelligence suggestions in reliability engineering. This quantity encompasses a survey of the contributions made to the optimum reliability layout literature within the resent years and chapters dedicated to varied functions of a genetic set of rules in reliability engineering and to mixtures of this set of rules with different computational intelligence concepts. Genetic algorithms are the most commonly used metaheuristics, encouraged via the optimization process that exists in nature, the organic phenomenon of evolution.
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Extra info for Computational Intelligence in Reliability Engineering: Evolutionary Techniques in Reliability Analysis and Optimization
Fig. 1. Basic principle of metaheuristics. The intelligence of a metaheuristic is usually embedded into its selection mechanism. For example, TS uses the objective function value and a long term memory to determine the new current solution. The new solution creation operator can be random or systematic. For example, in GA, a 40 Sadan Kulturel-Konak et al. crossover operator randomly recombines multiple solutions to create new solutions and a mutation operator randomly perturbs solutions. In SA and TS, on the other hand, a move operator systematically generates a set of neighborhood solutions from the current solution.
The model prohibits any mixture of component types within subsystems. Both the lifetime and the cost of redundancy components are considered as random variables, so stochastic simulation is used to estimate the system performance, including the mean lifetime, percentile lifetime and reliability. To speed up the solution process, these simulation results become the training data for training a neural network to approximate the system performance. The trained neural network is finally embedded into a genetic algorithm to form a hybrid intelligent algorithm for solving the proposed model.
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