By Gregory Levitin
This ebook covers the hot functions of computational intelligence thoughts in reliability engineering. This quantity features 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 ideas. Genetic algorithms are essentially the most frequent 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
The population is ranked according to a dominance rule, and then solutions are selected based on their ranks. The ultimate goal in multiobjective optimization is to investigate the Pareto optimal set. For many multiobjective problems, however, the size of the Pareto optimal set is very large. In addition, it is usually impossible to prove global optimality in combinatorial optimization problems. Therefore, the output of a multiobjective metaheuristic is called the best-known Pareto set. An effective multiobjective metaheuristic should achieve the following three goals : • Final solutions should be as close as possible to the true Pareto front.
In this case, the problem is formulated as determining the optimal design configuration to maximize system reliability (R) and to minimize system cost (C) and weight (W) when there are multiple component choices available for each of several k-out-of-n:G subsystems. The mathematical formulation of the problem is given below. , one of the objectives, maximizing R, minimizing C, or minimizing W is used to identify the best candidate. The idea of alternating objectives in multiobjective optimization has been previously applied in the area of evolutionary 44 Sadan Kulturel-Konak et al.
One of the objectives, maximizing R, minimizing C, or minimizing W is used to identify the best candidate. The idea of alternating objectives in multiobjective optimization has been previously applied in the area of evolutionary 44 Sadan Kulturel-Konak et al. computation by Kursawe  and Schaffer . This idea is very general and easy to implement. It can accommodate two or more objectives. There are no weights to set and no scaling adjustment to be made. The procedure of MTS is given as follows: Step 1.