Advanced Metaheuristic Techniques for Mechanical Design Problems: Review

Elaziz, Mohamed Abd; Elsheikh, Ammar H.; Oliva, Diego; Abualigah, Laith; Lu, Songfeng; Ewees, Ahmed A.

doi:10.1007/s11831-021-09589-4

Cited by 74 publications

(27 citation statements)

References 132 publications

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“…The results evidently show that metaheuristic algorithms serve to optimize regression models satisfactorily. The absence of implementations in mechanical systems over the last few years may have been due to few comparative and standardized results in the literature, the fact that they have several parameters that take a long time to determine their values or that there may be stagnation in local optima [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

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Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Rodríguez-Abreo

Rodríguez‐Reséndiz

Montoya-Santiyanes

et al. 2021

Sensors

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Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

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Section: Discussionmentioning

confidence: 99%

“…The unbalance increases the amplitude of the rotational frequency mainly in the radial direction [ 23 ]. To conclude, we may say that there are very few mechanical applications where the GWO algorithm has been implemented, and even fewer in vibration analysis [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Rodríguez-Abreo

Rodríguez‐Reséndiz

Montoya-Santiyanes

et al. 2021

Sensors

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“…The model succeeded in obtaining reasonable accuracy using a limited number of samples and examples during the training process. The integration between AI models and the metaheuristic optimizers [ 95 , 96 ] has been also reported as an efficient tool to diagnose COVID-19 infections.…”

Section: Comparative Study and Discussionmentioning

confidence: 99%

“…More investigations should be carried out to develop hybrid AI models to predict and forecast the prevalence of this pandemic as well as detect the infected cases via image processing approaches. These hybrid models are composed of an AI model, such as ANN, ANFIS and RVFL integrated with metaheuristic optimizers [ 96 ] such as pigeon optimizer [ 97 ], gradient-based optimizer [ 98 ], parasitism-predation optimizer [ 99 ], ecosystem-based optimization [ 100 ], Hunger games algorithm [ 101 ], flower pollination algorithm [ 102 ] and political optimizer [ 103 ].…”

Section: Comparative Study and Discussionmentioning

confidence: 99%

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

Elsheikh

Saba

Panchal³

et al. 2021

Healthcare

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Since the discovery of COVID-19 at the end of 2019, a significant surge in forecasting publications has been recorded. Both statistical and artificial intelligence (AI) approaches have been reported; however, the AI approaches showed a better accuracy compared with the statistical approaches. This study presents a review on the applications of different AI approaches used in forecasting the spread of this pandemic. The fundamentals of the commonly used AI approaches in this context are briefly explained. Evaluation of the forecasting accuracy using different statistical measures is introduced. This review may assist researchers, experts and policy makers involved in managing the COVID-19 pandemic to develop more accurate forecasting models and enhanced strategies to control the spread of this pandemic. Additionally, this review study is highly significant as it provides more important information of AI applications in forecasting the prevalence of this pandemic.

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“…In the majority of real-world optimization problems, a large number of decision variables are interacted with together, which is a very time-consuming process for finding an exact solution [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Metaheuristic algorithms have been widely used in recent years to approximate near-optimal solutions for real-world problems in various applications such as discrete optimization [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], continuous optimization [ 18 , 19 , 20 , 21 , 22 ], and constrained engineering problems [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. Moreover, a novel research field has emerged in this area which successfully combines machine learning and swarm intelligence approaches to obtain outstanding results in different areas [ 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

Nadimi-Shahraki

Fatahi

Zamani

et al. 2021

Entropy

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Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO’s issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to find the best optimal solutions for mechanical engineering problems is evaluated with three problems from the latest test-suite CEC 2020. The experimental and statistical results demonstrate that the proposed I-MFO is significantly superior to the contender algorithms and it successfully upgrades the shortcomings of the canonical MFO.

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Advanced Metaheuristic Techniques for Mechanical Design Problems: Review

Cited by 74 publications

References 132 publications

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

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