This paper proposes a novel nature-inspired meta-heuristic optimizer, called Reptile Search Algorithm (RSA), motivated by the hunting behaviour of Crocodiles. Two main steps of Crocodile behaviour ...are implemented, such as encircling, which is performed by high walking or belly walking, and hunting, which is performed by hunting coordination or hunting cooperation. The mentioned search methods of the proposed RSA are unique compared to other existing algorithms. The performance of the proposed RSA is evaluated using twenty-three classical test functions, thirty CEC2017 test functions, ten CEC2019 test functions, and seven real-world engineering problems. The obtained results of the proposed RSA are compared to various existing optimization algorithms in the literature. The results of the tested three benchmark functions revealed that the proposed RSA achieved better results than the other competitive optimization algorithms. The results of the Friedman ranking test proved that the RSA is a significantly superior method than other comparative methods. Finally, the results of the examined engineering problems showed that the RSA obtained better results compared to other various methods. Source codes of RSA are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/101385-reptile-search-algorithm-rsa-a-nature-inspired-optimizer
•Developed a novel optimization algorithm inspired by hunting behaviour of Reptiles (RSA).•Tested RSA against classical, CEC2017, CEC2019 test functions and engineering problems.•Compared the RSA to other well-known optimization algorithms.•Demonstrated effectiveness and superiority of the proposed RSA.
Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for ...the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.
•A new method to solve global optimization and engineering problems called OBSCA.•The proposed method improves the SCA by using opposite-based learning.•We apply the OBSCA over mathematical benchmark ...functions.•We test OBSCA in engineering optimization problems.•Comparisons support the improvement on the performance of OBCSA.
Real life optimization problems require techniques that properly explore the search spaces to obtain the best solutions. In this sense, it is common that traditional optimization algorithms fail in local optimal values. The Sine Cosine Algorithms (SCA) has been recently proposed; it is a global optimization approach based on two trigonometric functions. SCA uses the sine and cosine functions to modify a set of candidate solutions; such operators create a balance between exploration and exploitation of the search space. However, like other similar approaches, SCA tends to be stuck into sub-optimal regions that it is reflected in the computational effort required to find the best values. This situation occurs due that the operators used for exploration do not work well to analyze the search space. This paper presents an improved version of SCA that considers the opposition based learning (OBL) as a mechanism for a better exploration of the search space generating more accurate solutions. OBL is a machine learning strategy commonly used to increase the performance of metaheuristic algorithms. OBL considers the opposite position of a solution in the search space. Based on the objective function value, the OBL selects the best element between the original solution and its opposite position; this task increases the accuracy of the optimization process. The hybridization of concepts from different fields is crucial in intelligent and expert systems; it helps to combine the advantages of algorithms to generate more efficient approaches. The proposed method is an example of this combination; it has been tested over several benchmark functions and engineering problems. Such results support the efficacy of the proposed approach to find the optimal solutions in complex search spaces.
•The exploration of whale optimization algorithm is enhanced.•The opposition-based learning is incorporated to the whale optimization algorithm.•The proposed method is tested over benchmark ...optimization functions.•The algorithm is used to estimate parameters of solar cells and photovoltaic panels.•Several comparisons and metrics support the experimental results.
Solar cells are considered as a clean source of energy, and their application includes industrial and domestic users. Most of the algorithms used to design solar cells are tested (and used) only for domestic implementations. However, it is necessary to have accurate mechanisms for solar cell design that can be used in both industrial and domestic energy systems. To achieve this goal, this article introduces an improved version of the whale optimization Algorithm that uses the opposition-based learning to enhance the exploration of the search space. This algorithm is applied to estimate the parameters of solar cells using three different diode models. Such models are the single diode model, the double diode model and the three diode model, each of them has different internal parameters that must be accurately estimated in order to have a good performance of the solar cells. The inclusion of the three diode model is due it represents a more accurate representation of the solar cells behavior in industrial applications. For experiments and comparisons, there are used similar approaches and datasets from solar cells and photovoltaic modules. Moreover, the proposed method has also been tested over different benchmark optimization functions to verify its exploration capabilities. The experiments and comparisons support the performance of the proposed approach in complex optimization problems.
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•Performance of passive still, active still, and condenser is studied.•Distilling systems are modeled by different artificial intelligence-based models.•Accumulated productivity of ...active still is improved by 53.21%.•Artificial neural network unified with Harris Hawks optimizer is the best model.•In the best model, R2 is 0.97 and 0.98 for active and passive stills, respectively.
In this paper, a new productivity prediction model of active solar still was developed depending on improving the performance of the traditional artificial neural networks using Harris Hawks Optimizer. This optimizer simulates the behavior of Harris Hawks to catch their prey, and this method is used to determine the optimal parameters of artificial neural networks. The proposed model, called Harris Hawks Optimizer – artificial neural network, is compared with two other models named support vector machine and traditional artificial neural network, in addition to the experimental-based behavior of the solar still. The models were applied to predict the yield of three different distillation systems, namely, passive solar still, active solar still, and active solar still integrated with a condenser. Experimentally, the productivity of the active distiller integrated with the condenser was increased by 53.21% at a fan speed of 1350 rpm. The performance of the models was assessed using different statistical criteria such as root mean square error, coefficient of determination, and others. Among the three models, Harris Hawks Optimizer – artificial neural network had the best accuracy in predicting the solar still yield compared with the real experimental results.
The development of different solar energy (SE) systems becomes one of the most important solutions to the problem of the rapid increase in energy demand. This may be achieved by optimizing the ...performance of solar-based devices under some operating conditions. Intelligent system-based techniques are used to optimize the performance of such systems. In present review, an attempt has been made to scrutinize the applications of artificial neural network (ANN) as an intelligent system-based method for optimizing and the prediction of different SE devices’ performance, like solar collectors, solar assisted heat pumps, solar air and water heaters, photovoltaic/thermal (PV/T) systems, solar stills, solar cookers, and solar dryers. The commonly used artificial neural network types and architectures in literature, such as multilayer perceptron neural network, a neural network using wavelet transform, Elman neural network, and radial basis function, are also briefly discussed. Different statistical criteria that used to assess the performance of artificial neural network in modeling SE systems have been introduced. Previous studies have reported that artificial neural network is a useful technique to predict and optimize the performance of different solar energy devices. Important conclusions and suggestions for future research are also presented.
It is necessary to study different aspects of renewable energy generation, including wind energy. Wind power is one of the most important green and renewable energy resources. The estimation of wind ...energy generation is a critical task that has received wide attention in recent years. Different machine learning models have been developed for this task. In this paper, we present an efficient forecasting model using naturally inspired optimization algorithms. We present an optimized dendritic neural regression (DNR) model for wind energy prediction. A new variant of the seagull optimization algorithm (SOA) is developed using the search operators of the Aquila optimizer (AO). The main idea is to apply the operators of the AO as a local search in the traditional SOA, which boosts the SOA’s search capability. The new method, called SOAAO, is employed to train and optimize the DNR parameters. We used four wind speed datasets to assess the performance of the presented time-series prediction model, called DNR-SOAAO, using different performance indicators. We also assessed the quality of the SOAAO with extensive comparisons to the original versions of the SOA and AO, as well as several other optimization methods. The developed model achieved excellent results in the evaluation. For example, the SOAAO achieved high R2 results of 0.95, 0.96, 0.95, and 0.91 on the four datasets.
The Arithmetic Optimization Algorithm Abualigah, Laith; Diabat, Ali; Mirjalili, Seyedali ...
Computer methods in applied mechanics and engineering,
04/2021, Letnik:
376
Journal Article
Recenzirano
Odprti dostop
This work proposes a new meta-heuristic method called Arithmetic Optimization Algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including ...(Multiplication (M), Division (D), Subtraction (S), and Addition (A)). AOA is mathematically modeled and implemented to perform the optimization processes in a wide range of search spaces. The performance of AOA is checked on twenty-nine benchmark functions and several real-world engineering design problems to showcase its applicability. The analysis of performance, convergence behaviors, and the computational complexity of the proposed AOA have been evaluated by different scenarios. Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms. Source codes of AOA are publicly available at and .
This paper improves the performance of the recently-proposed Whale Optimization Algorithm (WOA). WOA is a meta-heuristic that simulates the foraging behavior of humpback whales. There are several ...improvements in the literature for this algorithm of which chaotic maps and Opposition-Based Learning (OBL) are proved to be the most effective. In the former method, however, there are many chaotic maps that make it difficult to choose the best one for a given optimization algorithm. In the latter method, OBL should be applied to a portion of solutions in the population, which is normally obtained manually, which is time-consuming. This work proposed a hyper-heuristic to alleviate these drawbacks by automatically choosing a chaotic map and a portion of the population using the Differential Evolution (DE) algorithm. The proposed algorithm, which called DEWCO, has high ability to improve the exploration and local optima avoidance of WOA. In order to investigate the performance of the proposed DEWCO algorithm, several experiments are conducted on 35 standard CEC2005 functions and using seven algorithms. The experimental results show the superior performance of the proposed DEWCO algorithm to determine the optimal solutions of the test function problems.
Human activity recognition (HAR) applications have received much attention due to their necessary implementations in various domains, including Industry 5.0 applications such as smart homes, ...e-health, and various Internet of Things applications. Deep learning (DL) techniques have shown impressive performance in different classification tasks, including HAR. Accordingly, in this article, we develop a comprehensive HAR system based on a novel DL architecture called Multi-ResAtt (multilevel residual network with attention). This model incorporates initial blocks and residual modules aligned in parallel. Multi-ResAtt learns data representations on the inertial measurement units level. Multi-ResAtt integrates a recurrent neural network with attention to extract time-series features and perform activity recognition. We consider complex human activities collected from wearable sensors to evaluate the Multi-ResAtt using three public datasets, Opportunity; UniMiB-SHAR; and PAMAP2. Additionally, we compared the proposed Multi-ResAtt to several DL models and existing HAR systems, and it achieved significant performance.
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