Quality Control

Computational Intelligence In Reliability Engineering by Gregory Levitin, Gregory Levitin

By Gregory Levitin, Gregory Levitin

This e-book covers the hot purposes of computational intelligence ideas in reliability engineering. This quantity incorporates a survey of the contributions made to the optimum reliability layout literature within the resent years and chapters dedicated to varied purposes of a genetic set of rules in reliability engineering and to combos of this set of rules with different computational intelligence ideas. Genetic algorithms are some of the most known metaheuristics, encouraged through the optimization strategy that exists in nature, the organic phenomenon of evolution.

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There are K conflicting objective functions, and each objective function can be minimization or maximization type. -K. com © Springer-Verlag Berlin Heidelberg 2007 38 Sadan Kulturel-Konak et al. with respect to the other objectives. Therefore, a perfect multiobjective solution x that simultaneously optimizes each objective function is almost impossible. A reasonable approach to a multiobjective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution.

IEEE Transactions on Reliability R26: 148-152 [124] Wattanapongsakorn N, Levitan SP (2004) Reliability optimization models for embedded systems with multiple applications. IEEE Transactions on Reliability 53: 406-416 [125] Wolfram S (1984) Cellular automata as models of complexity. Nature, 3H [126] Yalaoui A, Chu C, Châtelet E (2005) Reliability allocation problem in a series-parallel system. Reliability Engineering and System Safety 90: 55-61 [127] Yalaoui A, Châtelet E, Chu C (2005) A new dynamic programming method for reliability & redundancy allocation in a parallel-series system.

2 Hybrid Genetic Algorithm GA is a population-based directed random search technique inspired by the principles of evolution. Though it provides only heuristic solutions, it can be effectively applied to almost all complex combinatorial problems, and, thus, it has been employed in a large number of references as shown in Table 1. Ref [29] provides a state-of-the-art survey of GA-based reliability design. To improve computational efficiency, or to avoid premature convergence, numerous researchers have been inspired to seek effective combinations of GAs with heuristic algorithms, simulation annealing methods, neural network techniques, steepest decent methods or other local search methods.

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