GA Genetic Algorithms

Abstract—In practice, network designs can be based on multiple choices of redundant configurations, and different available components which can be used to form links. More specifically, the reliability of a network system can be improved through redundancy allocation, or for a fixed network topology, by selection of highly reliable links between node pairs, yet with limited overall budgets, and other constraints as well. The choice of a preferred network system design requires the estimation of its reliability. However, the uncertainty associated with such estimates must also be considered in the decision process. Indeed, network system reliability is generally estimated from estimates of the reliability of lower-level components (nodes & links) affected by uncertainties. The propagation of the estimation uncertainty from the components degrades the accuracy of the system reliability estimation. This paper formulates a multiple-objective optimization approach aimed at maximizing the network reliability estimate, and minimizing its associated variance when component types, with uncertain reliability, and redundancy levels are the decision variables. In the proposed approach, Genetic Algorithms (GA) and Monte Carlo (MC) simulation are effectively combined to identify optimal network designs with respect to the stated objectives. A set of Pareto optimal solutions are obtained so that the decision-makers have the flexibility to choose the compromised solution which best satisfies their risk profiles. Sample networks are solved in the paper using the proposed approach. The results indicate that significantly different designs are obtained when the formulation incorporates estimation uncertainty into the optimal design problem objectives. Index Terms—Estimation uncertainties, genetic algorithms