Computational Intelligence in Reliability Engineering: by Gregory Levitin

By Gregory Levitin

This e-book covers the new functions of computational intelligence recommendations 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 various functions of a genetic set of rules in reliability engineering and to combos of this set of rules with different computational intelligence options. Genetic algorithms are the most familiar metaheuristics, encouraged by means of the optimization method that exists in nature, the organic phenomenon of evolution.

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

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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. IEEE Transactions on Reliability 54: 254-261 [128] Yang JE, Hwang MJ, Sung TY, Jin Y (1999) Application of genetic algorithm for reliability allocation in nuclear power plants. Reliability Engineering and System Safety 65: 229-238 [129] You PS, Chen TC (2005) An efficient heuristic for series-parallel redundant reliability problems.

Multiobjective TS were proposed in [1], [3], [4], [25], and [27]. In Kulturel-Konak et al. [35], a new multiobjective TS approach was proposed, called the multinomial TS, to solve the multiobjective series-parallel system redundancy allocation problem. 1 Multiobjective System Redundancy Allocation Problem In this section, a multinomial TS (MTS) approach of Kulturel-Konak et al. [35] is presented to solve the multiobjective series-parallel system redundancy Multiobjective Metaheuristic Approaches to Reliability Optimization 43 allocation problem (RAP).

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 [62]: • Final solutions should be as close as possible to the true Pareto front. 42 Sadan Kulturel-Konak et al. • Final solutions should be uniformly distributed and diverse over the final Pareto front. • The extreme ends of the true Pareto front should be investigated.

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