Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization

Kong, Xiangyong; Gao, Liqun; Ouyang, Haibin; Li, Steven

doi:10.1016/j.ress.2015.07.019

Cited by 31 publications

(16 citation statements)

References 52 publications

(73 reference statements)

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“…Ardakan and Hamadani 10 and Ardakan et al 55 also developed a GA and NSGA-II, respectively for scenario 2. In the case of scenario 4, Tavakolli-Moghaddam et al, 48 Chambari et al, 49 Kong et al, 50 Safari, 51 and Zaretalab et al 52 respectively proposed a GA, simulated annealing (SA), simplified particle swarm optimization (SPSO), NSGA-II and knowledge-based archive multi-objective simulated annealing (KBAMOSA) algorithm. Coit 2,47 using integer programming found the optimal solution under scenarios 4 and 6.…”

Section: Illustrative Examplementioning

confidence: 99%

“…The convenient lower bound on subsystem reliability derived by Coit 2 has been used as a term of objective function instead of the exact value of subsystem reliability in numerous studies. The reader is referred to the works of Coit, 47 Tavakkoli-Moghaddam et al, 48 Chambari et al, 49 Kong et al, 50 Chambari et al, 1 Safari 51 , Zaretalab et al, 52 Sadjadi and Soltani, 53 and Soltani et al 54 Moreover, applying the argument presented by Coit, 2 Ardakan and Hamadani 10 and Ardakan et al 55 derived a lower bound for the reliability equation of a subsystem with mixed redundancy and imperfect switching mechanism. Feizabadi and Jahromi 56 and Jahromi and Feizabadi 57 extended these derivations to the case in which the type of active components can be different from the type of standby components.…”

Section: Introductionmentioning

confidence: 99%

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Reliability optimization for non-repairable series-parallel systems with a choice of redundancy strategies and heterogeneous components: Erlang time-to-failure distribution

Sadeghi

Roghanian

Shahriari

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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The redundancy allocation problem (RAP) of non-repairable series-parallel systems considering cold standby components and imperfect switching mechanism has been traditionally formulated with the objective of maximizing a lower bound on system reliability instead of exact system reliability. This objective function has been considered due to the difficulty of determining a closed-form expression for the system reliability equation. But, the solution that maximizes the lower bound for system reliability does not necessarily maximize exact system reliability and thus, the obtained system reliability may be far from the optimal reliability. This article attempts to overcome the mentioned drawback. Under the assumption that component time-to-failure is distributed according to an Erlang distribution and switch time-to-failure is exponentially distributed, a closed-form expression for the subsystem cold standby reliability equation is derived by solving an integrodifference equation. A semi-analytical expression is also derived for the reliability equation of a subsystem with mixed redundancy strategy. The accuracy and the correctness of the derived equations are validated analytically. Using these equations, the RAP of non-repairable series-parallel systems with a choice of redundancy strategies is formulated. The proposed mathematical model maximizes exact system reliability at mission time given system design constraints. Unlike most of the previous formulations, the possibility of using heterogeneous components in each subsystem is provided so that the active components can be of one type and the standby ones of the other. The results of an illustrative example demonstrate the high performance of the proposed model in determining optimal design configuration and increasing system reliability.

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Section: Illustrative Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliability optimization for non-repairable series-parallel systems with a choice of redundancy strategies and heterogeneous components: Erlang time-to-failure distribution

Sadeghi

Roghanian

Shahriari

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

show abstract

“…Fang et al 22 used the nondominated sorting binary differential evolution (NSBDE) algorithm to optimize the system resilience and cost. Kong et al 23 used a simplified version of particle swarm optimization (SPSO) to optimize the system reliability with multiple strategy choices. Abouei Ardakan et al 24 implemented a modified version of the genetic algorithm (MVGA) by considering the standby strategy in the optimization of the system reliability.…”

Section: Introductionmentioning

confidence: 99%

An adaptive particle swarm optimization method for multi-objective system reliability optimization

Mellal

Zio

2019

Proceedings of the IMechE

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Multi-objective system reliability optimization has attracted the attention of several researchers, due to its importance in industry. In practice, the optimization regards multiple objectives, for example, maximize the reliability, minimize the cost, weight, and volume. In this article, an adaptive particle swarm optimization is presented for multi-objective system reliability optimization. The approach uses a Lévy flight for some particles of the swarm, for avoiding local optima and insuring diversity in the exploration of the search space. The multi-objective problem is converted to a single-objective problem by resorting to the weighted-sum method and a penalty function is implemented to handle the constraints. Nine numerical case studies are presented as benchmark problems for comparison; the results show that the proposed approach has superior performance than a standard particle swarm optimization.

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“…Several researchers proposed simplified and improved particle swarm optimization (PSO) algorithm in the field of reliability analysis. 12,13 Unlike nonhomogeneous Poisson process based software reliability growth model (NHPP SRGM), 14 a general advantage of soft computing approaches such as ANN and PSO is that they do not impose any assumption for software failure data and rely only on the characteristics of the failure data itself. 15,16 This article presents a prior best Particle swarm optimization (pPSO), a variant of PSO, which is investigated in this article.…”

Section: Introductionmentioning

confidence: 99%

A neuro-particle swarm optimization logistic model fitting algorithm for software reliability analysis

Mahapatra¹,

Mahapatra²

2019

Proceedings of the IMechE

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This article develops a particle swarm optimization algorithm based on a feed-forward neural network architecture to fit software reliability growth models. We employ adaptive inertia weight within the proposed particle swarm optimization in consideration of learning algorithm. The dynamic adaptive nature of proposed prior best particle swarm optimization prevents the algorithm from becoming trapped in local optima. These neuro-prior best particle swarm optimization algorithms were applied to a popular flexible logistic growth curve as the [Formula: see text] model based on the weights derived by the artificial neural network learning algorithm. We propose the prior best particle swarm optimization algorithm to train the network for application to three different software failure data sets. The new search strategy improves the rate of convergence because it retains information on the prior particle, thereby enabling better predictions. The results are verified through testing approaching of constant, modified, and linear inertia weight. We assess the fitness of each particle according to the normalized root mean squared error which updates the best particle and velocity to accelerate convergence to an optimal solution. Experimental results demonstrate that the proposed [Formula: see text] model based prior best Particle Swarm Optimization based on Neural Network (pPSONN) improves predictive quality over the [Formula: see text], [Formula: see text], and existing model.

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Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization

Cited by 31 publications

References 52 publications

Reliability optimization for non-repairable series-parallel systems with a choice of redundancy strategies and heterogeneous components: Erlang time-to-failure distribution

Reliability optimization for non-repairable series-parallel systems with a choice of redundancy strategies and heterogeneous components: Erlang time-to-failure distribution

An adaptive particle swarm optimization method for multi-objective system reliability optimization

A neuro-particle swarm optimization logistic model fitting algorithm for software reliability analysis

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