Diagnosis of Parkinson's disease at its early stage is important in proper treatment of the patients so they can lead productive lives for as long as possible. Although many techniques have been ...proposed to diagnose the Parkinson's disease at an early stage but none of them are efficient. In this work, to improve the diagnosis of Parkinson's disease, we have introduced a novel improved and optimized version of crow search algorithm(OCSA). The proposed OCSA can be used in predicting the Parkinson's disease with an accuracy of 100% and help individual to have proper treatment at early stage. The performance of OCSA has been measured for 20 benchmark datasets and the results have been compared with the original chaotic crow search algorithm(CCSA). The experimental result reveals that the proposed nature-inspired algorithm finds an optimal subset of features, maximizing the accuracy and minimizing a number of features selected and is more stable.
In this paper, a novel swarm optimization approach, namely sparrow search algorithm (SSA), is proposed inspired by the group wisdom, foraging and anti-predation behaviours of sparrows. Experiments on ...19 benchmark functions are conducted to test the performance of the SSA and its performance is compared with other algorithms such as grey wolf optimizer (GWO), gravitational search algorithm (GSA), and particle swarm optimization (PSO). Simulation results show that the proposed SSA is superior over GWO, PSO and GSA in terms of accuracy, convergence speed, stability and robustness. Finally, the effectiveness of the proposed SSA is demonstrated in two practical engineering examples.
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.
•Deep base models are constructed to adaptively learn latent features from vibration signals.•Multiple diverse deep base models are acquired by variants of AEs and Bootstrap.•EWV with class-specific ...thresholds is designed to implement selective ensemble.•BAS algorithm is used to optimize the class-specific thresholds of the EWV.
Rolling bearing fault diagnosis is a meaningful yet challengeable task. To improve the performance of rolling bearing fault diagnosis, this paper proposes an enhanced selective ensemble deep learning method with beetle antennae search (BAS) algorithm. Firstly, multiple deep base models are constructed to automatically capture sensitive features from raw vibration signals. Secondly, to ensure the diversity of the base models, sparse autoencoder, denoising autoencoder and linear decoder are used to construct different deep autoencoders, respectively, and also Bootstrap is used to design distinctive training data subsets for each base model. Thirdly, an enhanced weighted voting (EWV) combination strategy with class-specific thresholds is proposed to implement selective ensemble learning. Finally, BAS algorithm is used to adaptively select the optimal class-specific thresholds. Experimental bearing data are used to verify the effectiveness of the proposed method. The results suggest that the proposed method can more accurately and robustly recognize different kind of faults than both the individual base models and other existing ensemble learning methods.
•A novel metaheuristic technique called crow search algorithm is proposed.•Crow search algorithm is used to efficiently solve engineering design problems.•Crow search algorithm works based on ...intelligent behaviors of crows.•Crow search algorithm produces promising results compared to other algorithms.
This paper proposes a novel metaheuristic optimizer, named crow search algorithm (CSA), based on the intelligent behavior of crows. CSA is a population-based technique which works based on this idea that crows store their excess food in hiding places and retrieve it when the food is needed. CSA is applied to optimize six constrained engineering design problems which have different natures of objective functions, constraints and decision variables. The results obtained by CSA are compared with the results of various algorithms. Simulation results reveal that using CSA may lead to finding promising results compared to the other algorithms.
•A hybrid algorithm, GWOCS, based on GWO and CS is proposed.•GWOCS is applied to extract the parameters of solar PV models.•A new OBL strategy aims to enhance the exploration ability of ...GWO.•Comprehensively experimental results demonstrate the competitive performance of GWOCS.
Quickly, accurately and reliably extract the parameters of solar photovoltaic (PV) model is very critical to simulate, evaluate and control the PV systems. During the past few years, many analytical, numerical and meta-heuristic algorithms have been suggested to extract the parameters of PV models based on the experimental data. However, extracting the parameters of PV models is still a great challenge. In this paper, a new hybrid algorithm based on grey wolf optimizer and cuckoo search (GWOCS) is developed to extract the parameters of different PV cell models with the experimental data under different operating conditions. In GWOCS, a new opposition learning strategy for the decision layer individuals (i.e., α, β, and δ) is proposed to enhance diversity of GWO. The main advantage of GWOCS is its ability to balance between exploration and exploitation. The performance of GWOCS is firstly tested on 10 complex benchmark functions. Then, the GWOCS is applied to extract the parameters of several solar PV cell models under different operating conditions. The comprehensively experimental results show the GWOCS is a promising candidate approach to extract the parameters of solar PV models.
•Improve the accuracy and speed of the short term wind speed forecasting by a hybrid forecasting model.•Propose a feature selection method based on entropy and mutual information technique.•Implement ...a deep learning time series prediction model based on LSTM.•Using the Wavelet Transform module eliminate fluctuation behaviors of wind speed.•Using the Crow Search Algorithm to optimize the LSTM structure and the number of input features.
In recent years, clean energies, such as wind power have been developed rapidly. Especially, wind power generation becomes a significant source of energy in some power grids. On the other hand, based on the uncertain and non-convex behavior of wind speed, wind power generation forecasting and scheduling may be very difficult. In this paper, to improve the accuracy of forecasting the short-term wind speed, a hybrid wind speed forecasting model has been proposed based on four modules: crow search algorithm (CSA), wavelet transform (WT), Feature selection (FS) based on entropy and mutual information (MI), and deep learning time series prediction based on Long Short Term Memory neural networks (LSTM). The proposed wind speed forecasting strategy is applied to real-life data from Sotavento that is located in the south-west of Europe, in Galicia, Spain, and Kerman that is located in the Middle East, in the southeast of Iran. The presented numerical results demonstrate the efficiency of the proposed method, compared to some other existing wind speed forecasting methods.
•A novel MPPT method based on an improved squirrel search algorithm (ISSA) is proposed.•Performance of the ISSA is compared with that of SSA, particle swarm optimization (PSO), and genetic algorithm ...(GA).•Simulation results demonstrate the effectiveness and the high tracking speed of the ISSA.•The feasibility of the proposed method is validated experimentally.•Average efficiency obtained is 99.48% with a tracking time of 0.66 s.
The partial shading condition (PSC) makes it challenging for the PV system to harvest maximum power via maximum power point tracking (MPPT). Various MPPT algorithms based on bio-inspired optimization methods were proposed in the literature. The mechanism employed by these algorithms varies from one to another, making them perform differently when tracking the GMPP. This paper introduces a novel MPPT technique based on the improved squirrel search algorithm (ISSA). The performance of the proposed ISSA improved the tracking time by 50% in comparison with the conventional SSA algorithm. Similarly, the proposed method has also been compared with popular Genetic algorithm (GA), and particle swarm optimization (PSO). The results proved the ability of the proposed algorithm in tracking the GMPP with faster convergence and fewer power oscillations in comparison. The feasibility and effectiveness of the proposed ISSA based MPPT have been validated experimentally, and the results clearly demonstrate its capability in tracking the GMPP with an average efficiency of 99.48% and average tracking time of 0.66 s.
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