Improving the Efficiency of Cellular Automata for Sewer Network Design Optimization Problems Using Adaptive Refinement

In the past few years, researchers have begun to investigate the existence of arbitrary uncertainties in the design optimization problems. Most traditional reliability-based design optimization (RBDO) methods transform the design space to the standard normal space for reliability analysis but may not work well when the random variables are arbitrarily distributed. It is because that the transformation to the standard normal space cannot be determined or the distribution type is unknown. The methods of Ensemble of Gaussian-based Reliability Analyses (EoGRA) and Ensemble of Gradient-based Transformed Reliability Analyses (EGTRA) have been developed to estimate the joint probability density function using the ensemble of kernel functions. EoGRA performs a series of Gaussian-based kernel reliability analyses and merged them together to compute the reliability of the design point. EGTRA transforms the design space to the single-variate design space toward the constraint gradient, where the kernel reliability analyses become much less costly. In this paper, a series of comprehensive investigations were performed to study the similarities and differences between EoGRA and EGTRA. The results showed that EGTRA performs accurate and effective reliability analyses for both linear and nonlinear problems. When the constraints are highly nonlinear, EGTRA may have little problem but still can be effective in terms of starting from deterministic optimal points. On the other hands, the sensitivity analyses of EoGRA may be ineffective when the random distribution is completely inside the feasible space or infeasible space. However, EoGRA can find acceptable design points when starting from deterministic optimal points. Moreover, EoGRA is capable of delivering estimated failure probability of each constraint during the optimization processes, which may be convenient for some applications.

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Optimal Multiculture Network Design for Maximizing Resilience in the Face of Multiple Correlated Failures

Applied Sciences ◽

10.3390/app9112256 ◽

2019 ◽

Vol 9 (11) ◽

pp. 2256

Author(s):

Yasmany Prieto ◽

Nicolás Boettcher ◽

Silvia Elena Restrepo ◽

Jorge E. Pezoa

Keyword(s):

Network Design ◽

Optimization Problems ◽

Current Data ◽

Sequential Optimization ◽

Node Degree ◽

Network Resilience ◽

Correlated Failures ◽

The Face ◽

Network Operation ◽

Network Topologies

Current data networks are highly homogeneous because of management, economic, and interoperability reasons. This technological homogeneity introduces shared risks, where correlated failures may entirely disrupt the network operation and impair multiple nodes. In this paper, we tackle the problem of improving the resilience of homogeneous networks, which are affected by correlated node failures, through optimal multiculture network design. Correlated failures regarded here are modeled by SRNG events. We propose three sequential optimization problems for maximizing the network resilience by selecting as different node technologies, which do not share risks, and placing such nodes in a given topology. Results show that in the 75% of real-world network topologies analyzed here, our optimal multiculture design yields networks whose probability that a pair of nodes, chosen at random, are connected is 1, i.e., its ATTR metric is 1. To do so, our method efficiently trades off the network heterogeneity, the number of nodes per technology, and their clustered location in the network. In the remaining 25% of the topologies, whose average node degree was less than 2, such probability was at least 0.7867. This means that both multiculture design and topology connectivity are necessary to achieve network resilience.

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Application of Three Bar Truss Problem among Engineering Design Optimization Problems using Artificial Atom Algorithm

2018 International Conference on Artificial Intelligence and Data Processing (IDAP) ◽

10.1109/idap.2018.8620762 ◽

2018 ◽

Author(s):

Ayse ERDOGAN YILDIRIM ◽

Ali KARCI

Keyword(s):

Design Optimization ◽

Engineering Design ◽

Optimization Problems ◽

Artificial Atom ◽

Engineering Design Optimization

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