Reliability analysis based improved directional simulation using Harris Hawks optimization algorithm for engineering systems. (May 2022)

• Record Type: Journal Article
• Authors: Jafari-Asl, Jafar; Ben Seghier, Mohamed El Amine; Ohadi, Sima; Correia, José; Barroso, João
• Is Part Of: Engineering failure analysis. Volume 135(2022)
• Publication Date: 2022-05
• Subjects: Directional Simulation -- Harris Hawks Optimization -- Highly nonlinear performance functions -- Reliability index -- Meta-heuristic algorithms; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2022.106148
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: An improved directional simulation is developed using meta -heuristic algorithms. Harris Hawks Optimization is integrated to determine the sensitivity vector. Efficiency of reliability analysis is conducted against several reliability methods. Results indicate a strong adaptation for solving highly nonlinear functions. The proposed method estimate the reliability index with low computational cost. Abstract: In this paper, a new framework for accurate reliability analysis is proposed based on improving the directional simulation by using metaheuristic algorithms. Usually for highly nonlinear and complex performance functions, finding the unit vector direction requires very high calculations or impossible practically. Hence, the novel improved version incorporates the Harris Hawks Optimization algorithm, where the unit vector of direction is formulated as a constrained optimization problem and estimated using metaheuristic algorithms. Given that metaheuristic algorithms have been introduced to solve unconstrained problems, the penalty function method is used to convert the constrained problem into an unconstrained problem. The applicability of the proposed framework is firstly tested on five highly nonlinear benchmark functions and then applied to solve four high-dimensional engineering problems. The performance of six simulations-based reliability analysis methods and the first-order reliability method were compared with the proposed method. Besides the feasibility of other metaheuristic algorithms were investigated. The results show high-performance abilities of the improved version of the directional simulation for solving highly nonlinear engineering problems.