Computational Intelligence in Reliability Engineering: by Gregory Levitin

By Gregory Levitin

This e-book covers the new purposes of computational intelligence strategies 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 diversified functions of a genetic set of rules in reliability engineering and to combos of this set of rules with different computational intelligence thoughts. Genetic algorithms are some of the most familiar metaheuristics, encouraged via the optimization method that exists in nature, the organic phenomenon of evolution.

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Additional resources for Computational Intelligence in Reliability Engineering: Evolutionary Techniques in Reliability Analysis and Optimization

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95456 Recent Advances in Optimal Reliability Allocation 27 Generally, if computational efficiency is of most concern to designer, linear approximation or heuristic methods can obtain competitive feasible solutions within a very short time (few seconds), as reported in [36, 129]. The proposed linear approximation [36] is also easy to implement with any LP software. But the main limitation of those reported approaches is that the constraints must be linear and separable. Due to their robustness and feasibility, meta-heuristic methods such as GA and recently developed TS and ACO could be successfully applied to almost all NP-hard reliability optimization problems.

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. , K and f k (y ) < f k (x) for at least one objective k. A solution is said to be Pareto optimal if it is not dominated by another solution in the feasible solution space.

1 Metaheuristics In many optimization problems, some decision variables have only discrete values. The term combinatorial optimization refers to finding optimal solutions to such problems. Finding an optimal solution for a combinatorial optimization problem is a daunting task due to the combinatorial explosion of possible solutions to the problem with the number of discrete decision variables. Classical approaches such as enumeration (implicit enumeration, brand-and-bound and dynamic programming), Lagrangian relaxation, decomposition, and cutting plane techniques or their combinations may not be computationally feasible or efficient to solve a combinatorial optimization problem of a practical size.

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