A new particle swarm optimization algorithm for noisy optimization problems

Taghiyeh, Sajjad; Xu, Jie

doi:10.1007/s11721-016-0125-2

Cited by 34 publications

(30 citation statements)

References 55 publications

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“…These two approaches were so general that they could easily be employed on the other reliability‐redundancy models rather than the problem of the overspeed protection system. As for a future line to improve this research, variants of particle swarm optimization, such as PSO‐sg that is proposed in Taghiyeh and Xu 40 could be used to solve the nonlinear optimization part of this article and compare the results. Researchers may also employ the forecasting methods such as MSIC algorithm 20 to predict the future values of the reliability.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy reliability‐redundancy allocation problem of the overspeed protection system

Taghiyeh

Mahmoudi

Fadaie

et al. 2020

Engineering Reports

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System reliability is defined as the probability of satisfactory performance of a system under stated conditions for a specified period of time. According to this definition, four parameters, including probability, satisfactory performance, specific conditions, and time should be exactly characterized to evaluate the system reliability accurately. However, due to the uncertainty involved in real situations, it is hardly possible to assess the aforementioned parameters precisely. In this article, two general and distinct approaches, including Zadeh's extension principle and modification of fuzzy parametric programming (FPP), are proposed to take into account such uncertainty in a famous reliability problem called the overspeed protection system. According to Zadeh's extension principle, a pair of nonlinear programming problems is formulated to compute α‐level cuts of fuzzy system reliability. The membership function of fuzzy system reliability can then be constructed analytically by numerating different values of α. This fuzzy system reliability presents flexibility for further system analysis. In the second approach, from a different point of view, a variant of FPP is improved that provides a crisp value as a system reliability measure. The rewarding point of the latter procedure is to handle the problem in a computationally easier way.

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Section: Discussionmentioning

confidence: 99%

Fuzzy reliability‐redundancy allocation problem of the overspeed protection system

Taghiyeh

Mahmoudi

Fadaie

et al. 2020

Engineering Reports

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“…When, in an n-dimensional search space, the total number of particles is m, each particle is assumed to be a potential solution. The particle is updated their speed and position by the formulas (1) and (2) in the solving iteration process [22] [23] [24] [25]. The improved algorithm proposed in this study is called CSAPSO algorithm.…”

Section: Improved Particle Swarm Optimization Algorithm Csapsomentioning

confidence: 99%

Water Quality Evaluation Using Back Propagation Artificial Neural Network Based on Self-Adaptive Particle Swarm Optimization Algorithm and Chaos Theory

Li¹,

Wu²,

Chen³

et al. 2017

CWEEE

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To overcome the shortcomings of the traditional methods of water quality evaluation, in this paper, a novel model combines particle swarm optimization (PSO), chaos theory, self-adaptive strategy and back propagation artificial neural network (BP ANN) that was proposed to evaluate the water quality of Weihe River in China. An improved PSO algorithm with a self-adaptive inertia weight and a chaotic learning factor tuned by logistic function was developed and used to optimize the network parameters of BP ANN. The values of average absolute deviation (AAD), root mean square error of prediction (RMSEP) and squared correlation coefficient are 0.0061, 0.0163 and 0.9903, respectively. Compared with other methods, such as BP ANN, and PSO BP ANN, the proposed model displays optimal prediction performance with high precision and good correlation. The results show that the proposed method has the good prediction ability for evaluating water quality. It is convenient, reliable and high precision, which provides good analysis and evaluation method for water quality.

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“…In [22], the design of a computing budget allocation scheme for PSO was explored. In [23]–[26], the OCBA allocation rule in [15] was directly applied to PSO and obtained some improvement in computational efficiency. In [27], the OCBA procedure in [15] was combined with PSO into a two-stage algorithm and applied to a wafer probe testing problem in semiconductor manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization

Lee

et al. 2017

IEEE Trans. Evol. Computat.

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Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort.

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A new particle swarm optimization algorithm for noisy optimization problems

Cited by 34 publications

References 55 publications

Fuzzy reliability‐redundancy allocation problem of the overspeed protection system

Fuzzy reliability‐redundancy allocation problem of the overspeed protection system

Water Quality Evaluation Using Back Propagation Artificial Neural Network Based on Self-Adaptive Particle Swarm Optimization Algorithm and Chaos Theory

Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization

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