A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer

Salih, Sinan Q.; Alsewari, AbdulRahman A.

doi:10.1007/s00521-019-04575-1

Cited by 97 publications

(49 citation statements)

References 59 publications

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“…In addition, inspecting longer data span of historical data information is highly recommended to be devoted as it is a limitation of the current research. On the algorithmic level, the proposed model can be extended in a way that other parameters in the ELM network can be optimized and such parameters include the number of hidden nodes in the hidden layer, the transfer functions, and the activation functions [51][52][53].…”

Section: Discussionmentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

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e capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. e classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. is current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. e results are evaluated based on several statistical measures and graphical presentations. e river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.

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Section: Discussionmentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

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“…The prediction of the applied ANN and SVR models were demonstrated a kind of limitation based on the prediction results. Hence, the predictability performance of those model can be enhanced through the integration with some nature inspired optimization algorithms such as firefly, particle swarm optimization, genetic algorithm, nomadic people optimizer [53]- [57].…”

Section: Results and Disccusionmentioning

confidence: 99%

Reinforced concrete confinement coefficient estimation using soft computing models

Mohana¹

2019

PEN

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Infrastructure vulnerability toward seismic lateral loading within high seismicity has received massive attention by structural engineers and designers. This is for the purpose to provide a reliable alternative material that strengthening the bending and shear of slabs, columns and reinforced concrete (RC). Despite the utilized approaches of strengthening the concrete structure based on fiber reinforced polymers (FRP) is considerably new technique, exploring more reliable and robust methodologies is the motive of scholars for better structural behaviour understanding. In the current research, two soft computing models called artificial neural network (ANN) and support vector regression (SVR) are applied to predict lateral confinement coefficient (K s). The models are developed based on gathered dataset from open source researches for the lateral confinement coefficient of columns wrapped with carbon FRP (CFRP) and their corresponding parameters including column width, length and thickness (b, h and t mm), column radius (r mm), compressive strength of concrete () and elastic modulus (E FRP). Results indicated the superiority of the ANN model for predicting K s over the SVR model. The application of the soft computing showed an optimistic approach for the structural lateral confinement coefficient determination.

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“…From the Table 4 the most critical drawback of meta-heuristic methods is parameter tuning. So the most recommended method, is the parameter-free one [51,52].…”

Section: Most Common Constraintsmentioning

confidence: 99%

Meta-heuristic optimization methods applied to renewable distributed generation planning: A review

et al. 2021

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Due to its proven efficiency and computational speed, the most recent developed meta-heuristic optimization methods are widely used to better integrate renewable distributed generation (RDG) into the electricity grid. The main objective of this paper is to obtain a better knowledge of current trends in meta-heuristics applied to optimally integrate RDGs to the distribution network. This is a review of well known meta-heuristic approaches, used to solve the problem of optimal renewable distributed generation allocation planning (ORDGAP). In this context, some research gaps were mentioned, and recommendations were proposed to expand the scope of research in this field.

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A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer

Cited by 97 publications

References 59 publications

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Reinforced concrete confinement coefficient estimation using soft computing models

Meta-heuristic optimization methods applied to renewable distributed generation planning: A review

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