Abstract
Reliability allocation to increase the total reliability has become a successful way to increase the efficiency of the complex industrial system designs. A lot of research in the past have tackled this problem to a great extent. This is evident from the different techniques developed so far to achieve the target. Stochastic metaheuristics like simulated annealing, Tabu search (TS), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Genetic Algorithm (GA), Grey wolf optimization technique (GWO) etc. have been used in recent years. This paper proposes a framework for implementing a hybrid PSO-GWO algorithm for solving some reliability allocation and optimization problems. A comparison of the results obtained is done with the results of other well-known methods like PSO, GWO, etc. The supremacy/competitiveness of the proposed framework is demonstrated from the numerical experiments. These results with regard to the time taken for the computation and quality of solution outperform the previously obtained results by the other well-known optimization methods.
Highlights
The present-day real-world problems of engineering have reached to an advanced level that have motivated the researchers to find ways to increase the efficiency of complex systems
This paper proposes a framework for implementing hybrid Particle Swarm Optimization (PSO)-Grey wolf optimization technique (GWO) algorithm (HPSOGWO) for solving reliability allocation and optimization problems of Complex bridge system and Life support system in space capsule
The technique can serve the solutions to the various reliability allocation problems (RAPs) and reliability-redundancy allocation problem (RRAPs) with the use of a proper penalty function
Summary
The present-day real-world problems of engineering have reached to an advanced level that have motivated the researchers to find ways to increase the efficiency of complex systems. Nowadays there has been lot of research in the field of reliability optimization considering its wide applications in real life and industry (Atiqullah & Rao, 1993; Pham et al, 1995; Eiben & Schippers, 1998; Kishor et al, 2009; Jayabarathi et al, 2016;) Such improvisations increase the efficiency and give better results for stochastic nonlinear optimization problems (Ramírez-Rosado & Bernal-Agustín, 2001). Among the metaheuristic techniques for RAP Ant colony technique applied by Shelokar et al (2002); NSGA 2 by Kishore et al (2007, 2009); PSO by Pant et al (2011); CSA by Kumar et al (2016) These optimization techniques yield solutions for problems of convex nature and monotonicity
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Decision Making: Applications in Management and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
