By Gregory Levitin
This quantity includes chapters providing functions of other metaheuristics (ant colony optimization, nice deluge set of rules, cross-entropy approach and particle swarm optimization) in reliability engineering. it is also chapters dedicated to mobile automata and aid vector machines and various functions of synthetic neural networks, a robust adaptive procedure that may be used for studying, prediction and optimization. numerous chapters describe varied points of obscure reliability and purposes of fuzzy and imprecise set conception.
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Extra resources for Computational Intelligence in Reliability Engineering New Metaheuristics Neural and Fuzzy Techniques in Reliability
1 Introduction Optimization of reliability of complex systems is an extremely important issue in the field of reliability engineering. Over the past three decades, reliability optimization problems have been formulated as non-linear programming problems within either single-objective or multi-objective environment. Tillman et al.. (1980) provides an excellent overview of a variety of optimization techniques applied to solve these problems. However, he reviewed the application of only derivative-based optimization techniques, as metaheuristics were not applied to the reliability optimization problems by that time.
3 (3) Monte Carlo Simulation The evaluation of network reliability in general is a #P-complete problem. , via (2), is not feasible, estimation via Monte Carlo simulation becomes a viable option. The easiest way to estimate the reliability r (or unreliability r ) is to use CMC simulation, that is, let X(1),…,X(N) be independent identically distributed random vectors with the same distribution as X. Then rˆCMC = 1 N N ∑ϕ ( X ) i =1 (i ) is an unbiased estimator of r. Its sample variance is given by 46 Dirk P.
This information is missing for other algorithms. 823562 with 20,100 function evaluations. 9 is obtained in both cases. Thus, for this problem, ACO obtained a marginal improvement over MGDA in terms of accuracy and also consumed far less function evaluations. , 1980) in terms of both accuracy and speed. Table 1. Results of Problem 1- Case (i) Solution R1 R2 R3 R4 RS CS FE* Ravi (2004) MGDA ACO Shelokar et al. (2002) INESA Ravi et al. 8332 NA SA Ravi et al. 903 NA Tillman et al. 570892 with 54,140 function evaluations.
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