Bearing defect identification by swarm decomposition considering permutation entropy measure and opposition-based slime mould algorithm

Vashishtha, Govind; Chauhan, Sumika; Singh, Manpreet; Kumar, Rajesh

doi:10.1016/j.measurement.2021.109389

Cited by 66 publications

(24 citation statements)

References 45 publications

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“…It is derived by hybridizing the SMA with a modified version of the OBL and Orthogonal learning (OL) strategies [ 84 ]. An intelligent defect detection method has been proposed using the extreme learning machine (ELM) model [ 85 ]. This method is intended to discover flaws in taper roller bearings.…”

Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

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Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.

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Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

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“…Another example is the minimization of passband, stopband, and transition band errors for the design of a two-channel quadrature mirror filter bank [27]. Interested readers can refer to [28,29,30] to see more engineering applications of such optimization methods. ■ distance between the normal node and the cluster head minimizing the energy consumption of sensors 2021 [11] In summary, the most important differences between this study and the above research are as follows:…”

Section: -Related Workmentioning

confidence: 99%

Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods

Abbasi-khazaei

Rezvani

2022

Soft Comput

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One of the most important concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the Virtual Machine (VM) placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of IoT tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the subproblems of energy consumption minimization and scheduling fall into the category of NP-hard issues. In this study, we propose a multi-objective VM placement to joint minimizing energy costs and scheduling. After presenting a modified memetic algorithm, we compare its performance with baseline methods as well as stateof-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, SLA violations, and the total response time of IoT requests.

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“…In continuation, the effect of process parameters on the SR, production time (PT), and build volume of printed products are tabulated in Table 1. Some of the researchers have implemented AI algorithms in different research areas for various applications (Chauhan and Vashishtha, 2021; Chauhan et al , 2021; Vashishtha and Kumar, 2021 a , 2021 b , 2022; Vashishtha et al , 2021 Guleria et al 2022 b ).…”

Section: Introductionmentioning

confidence: 99%

Parametric optimization of FDM using the ANN-based whale optimization algorithm

Kumar

Gupta

Singh

2022

AIEDAM

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Surface roughness (SR) is one of the major parameters used to govern the quality of the fused deposition modeling (FDM)-printed products, and the FDM process parameters can be easily regulated in order to obtain a good surface finish. The surface quality of the product produced by the FDM is generally affected by the staircase effect that needs to be managed. Also, the production time (PT) to fabricate the product and volume percentage error (VPE) should be minimized to make the FDM process more efficient. The aim of this paper is to accomplish these three objectives with the use of the parametric optimization technique integrating the artificial neural network (ANN) and the whale optimization algorithm (WOA). The FDM parameters which have been taken into consideration are layer thickness, nozzle temperature, printing speed, and raster width. Experimentation has been conducted on printed samples to examine the impact of the input parameters on SR, VPE, and PT according to Taguchi's L27 orthogonal array. The ANN model has been built up using the experimental data, which was further used as an objective function in the WOA with an aim to minimize output responses. The robustness of the proposed method has been validated on the optimal combinations of FDM process parameters.

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Bearing defect identification by swarm decomposition considering permutation entropy measure and opposition-based slime mould algorithm

Cited by 66 publications

References 45 publications

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods

Parametric optimization of FDM using the ANN-based whale optimization algorithm

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