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The Teaching-Learning-Based Optimization (TLBO) algorithm is being extended to a broader range of applied optimization problems in the literature, mimicking the teaching-learning process. This paper proposes an Advanced Teaching-Learning-Based Optimization (Ad-TLBO) algorithm to enhance the efficiency and performance of the original version of TLBO in terms of accuracy, convergence rate, and reliability characteristics. The advancement is obtained by modifying the initialization, search approach, and structure of the two main phases of this algorithm in four steps to improve exploration and exploitation capability. Efficiency comparisons are shown in four challenges with various benchmark... 

Importance measures are integral parts of risk assessment for risk-informed decision making. Because the parameters of a risk model, such as the component failure rates, are functions of time and a perturbation (change) in their values can occur during the mission time, time dependence must be considered in the evaluation of the importance measures. In this paper, it is shown that the change in system performance at time t, and consequently the importance of the parameters at time t, depends on the parameters perturbation time and their value functions during the system mission time. We consider a nonhomogeneous continuous time Markov model of a series-parallel system to propose the... 

This research proposes a novel approach for the robust optimization of the design of hydrogen peroxide propulsion control systems using the efficient and advanced Teaching-Learning-Based Optimization (TLBO) method. This study adopts a robust design optimization (RDO) formulation that considers both epistemic and aleatory uncertainties, including sparse points and interval data, and uses the Johnson distribution family for uncertainty representation. The maximum likelihood estimation method is applied to determine the distribution parameters, also considering interval data with a nested optimization technique. A novel advanced TLBO method with high accuracy and convergence rate is employed to... 

Importance measures (IMs) are used for risk-informed decision making in system operations, safety, and maintenance. Traditionally, they are computed within fault tree (FT) analysis. Although FT analysis is a powerful tool to study the reliability and structural characteristics of systems, Bayesian networks (BNs) have shown explicit advantages in modeling and analytical capabilities. In this paper, the traditional definitions of IMs are extended to BNs in order to have more capability in terms of system risk modeling and analysis. Implementation results on a case study illustrate the capability of finding the most important components in a system. © 2018 World Scientific Publishing Company