Reliability optimization using hybrid genetic and particle swarm optimization algorithm

Abstract

Redundancy-allocation problem i.e. RAP is among the reliability optimization problems which make use of non-linear programming method to improve the reliability of complex system. The objective of this research paper is reliability optimization through the application of Genetic Algorithm i.e. GA and Hybrid Genetic & Particle Swarm Optimization (H-GAPSO) on a RAP. Certain shortcomings have been seen when results are obtained by application of single algorithms. In order to get rid of these shortcomings, HGA-PSO is introduced where attractive properties of GA and PSO are combined. This hybrid method makes use of iterative process of GA after obtaining initial best population from PSO. Comparative Analysis of results of GA and H-GAPSO is done with respect to reliability and computation (CPU) time and it is observed that H-GAPSO improved system reliability up to maximum by 63.10%. MATLprogramming has been used for computation of results from GA and HGA-PSO algorithms.

Critical Success Factors for 5G Technology Adaptation in Supply Chains

“…Hence, we may infer that our model provides a reasonably stable outcome, as it is less sensitive to the changes in the external conditions (for instance, variations in the values of q and λ). used in several previous studies [77][78][79][80][81][82][83]. We use the score values of the success factors obtained by using the q-ROFWNA for both the PIPRECIA-S and LBWA methods.…”

“…To check the reliability of the result, we perform a comparative analysis (see Table 11) with other methods, such as the simplified-pivot-pairwise-relative-criteria-importanceassessment (PIPRECIA-S) method [75] and the level-based weight-assessment (LBWA) model [76]. The comparative analysis is a useful way to check the reliability, as used in several previous studies [77][78][79][80][81][82][83]. We use the score values of the success factors obtained by using the q-ROFWNA for both the PIPRECIA-S and LBWA methods.…”

Optimizing and reliability analysis by firefly and genetic algorithms for a quadcopter

“…In this case, like FA in the environment of MATLAB version 2020, with a system by CPU Intel CORE i7 5500U 2 Core 3.40 GHz and RAM 8 GB 1600 MHz in windows8, we run the program 20 times and report the results. In the optimization of our problem, for algorithms with a large amount of data, the genetic algorithm is efficient., the following 12 parameters are considered as variables [26]: 𝑛1 = 𝑥(1), 𝑟1 = 𝑥(7), 𝑛2 = 𝑥(2), 𝑟2 = 𝑥(8), 𝑛3 = 𝑥(3), 𝑟3 = 𝑥(9), 𝑛4 = 𝑥(4), 𝑟4 = 𝑥(10), 𝑛5 = 𝑥(5), 𝑟5 = 𝑥(11), 𝑛6 = 𝑥(6), 𝑟6 = 𝑥 (12).…”

“…Additionally, in 2022, Tripti Dahiya et al tried to improve the reliability of a system by combining two optimization algorithms, PSO and GA, in an article called Reliability optimization using hybrid genetic and particle swarm optimization algorithm [12]. Kazem Imani et.…”