Grey wolf optimizer and hybrid PSO‐GWO for reliability optimization and redundancy allocation problem

Bhandari, Ashok Singh; Kumar, Akshay; Ram, Mangey

doi:10.1002/qre.3265

Cited by 13 publications

(6 citation statements)

References 46 publications

(99 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental results indicated that it was a high-performing algorithm in global optimization. With the same goal, a recently developed metaheuristic optimization algorithm called hybrid PSO-GWO [50] has been proposed to improve exploitation and exploration ability.…”

Section: Related Workmentioning

confidence: 99%

Improved GWO and its application in parameter optimization of Elman neural network

et al. 2023

View full text Add to dashboard Cite

Traditional neural networks used gradient descent methods to train the network structure, which cannot handle complex optimization problems. We proposed an improved grey wolf optimizer (SGWO) to explore a better network structure. GWO was improved by using circle population initialization, information interaction mechanism and adaptive position update to enhance the search performance of the algorithm. SGWO was applied to optimize Elman network structure, and a new prediction method (SGWO-Elman) was proposed. The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.

show abstract

Section: Related Workmentioning

confidence: 99%

Improved GWO and its application in parameter optimization of Elman neural network

et al. 2023

View full text Add to dashboard Cite

show abstract

“…However, considering that the switch is imperfect, that is, switch reliability is less than 1.0, Coit [3] showed that the optimal redundancy strategy should be employed depending on the condition of the redundant design, such as the reliability and redundancy level of the switch and components. Since then, many studies, such as [3][4][5][6][7][8][9][10][11][12][13], have presented the reliability optimization problem of employing either an active or cold standby (hereafter referred to as standby) redundancy strategy for each subsystem. Moreover, the reliability optimization problem has been applied in various real-world industrial fields such as spaceflight [14], manufacturing systems [15], and sliding window systems [16].…”

Section: Introductionmentioning

confidence: 99%

Lifetime Distribution for a Mixed Redundant System with Imperfect Switch and Components Having Phase–Type Time-to-Failure Distribution

Baek,

Kim

2024

Mathematics

View full text Add to dashboard Cite

Recently, a mixed redundancy was introduced among the redundant design strategies to achieve a more reliable system within the equivalent resources. This study deals with a lifetime distribution for a mixed redundant system with an imperfect fault detector/switch. The lifetime distribution model was formulated using a structured continuous Markov chain (CTMC) and considers the time-to-failure (TTF) distribution of a component as a phase-type distribution (PHD). The model’s versatility and practicality are enhanced because the PHD can represent diverse degradation patterns of the components exposed to varied operating environments. The model provides accurate reliability for a mixed redundant system by advancing the approximate reliability function suggested in previous studies. Furthermore, the model would be useful in system design and management because it provides information such as the nth moment of the system’s lifetime distribution. In numerical experiments on some examples, the mixed redundancy was confirmed to devise a more reliable system than the existing active and standby redundancies, and the improvement effect increased as the number of redundant components decreased. The optimal structure for maximizing the expected lifetime of the system changes depends on the reliability of the components and fault detector/switch.

show abstract

“…And therefore, the performance of the GA-PSO algorithm is enhanced obviously. Bhandari et al [26] develop a new metaheuristic optimization algorithm called hybrid HPSGWO, which effectively combines the exploitation ability of PSO and the exploration ability of GWO (PSO) to search for optimal solutions. The experimental results prove that the proposed HPSGWO algorithm obtains a better numerical result in numerical problems and the reliability-redundancy optimization problem.…”

Section: Introductionmentioning

confidence: 99%

An adaptive particle swarm optimization with information interaction mechanism

Liu,

Wei,

Zhang

2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

The Particle Swarm Optimization (PSO) algorithm is easy to implement owing to its simple framework, and has been successfully applied to many optimization problems. However, the standard PSO easily falls into the local optimum and has weak search ability. To enhance the optimization ability of the algorithm, this paper proposes an adaptive particle swarm optimization with information interaction mechanism (APSOIIM). First, a chaotic sequence strategy was used to produce uniformly distributed particles and enhance their convergence speed at the initialization stage of the algorithm. Then, an interaction information mechanism is introduced to enhance the diversity of the population with the progress of the search, which can effectively interact with the best information of neighboring particles to maintain the balance between exploration and exploitation. Besides, the convergence was proven to verify the robustness and efficiency of the proposed APSOIIM algorithm. Finally, the proposed APSOIIM was applied to solve the CEC2014 benchmark functions and CEC2017 benchmark functions as well as famous engineering optimization problems. The experimental results show that the proposed APSOIIM has significant advantages over the compared algorithms.

show abstract

Grey wolf optimizer and hybrid PSO‐GWO for reliability optimization and redundancy allocation problem

Cited by 13 publications

References 46 publications

Improved GWO and its application in parameter optimization of Elman neural network

Improved GWO and its application in parameter optimization of Elman neural network

Lifetime Distribution for a Mixed Redundant System with Imperfect Switch and Components Having Phase–Type Time-to-Failure Distribution

An adaptive particle swarm optimization with information interaction mechanism

Contact Info

Product

Resources

About