Slime mould algorithm: A new method for stochastic optimization

Li, Shimin; Chen, Huiling; Wang, Mingjing; Heidari, Ali Asghar; Mirjalili, Seyedali

doi:10.1016/j.future.2020.03.055

Cited by 2,209 publications

(1,126 citation statements)

References 83 publications

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“…In this subsection, the results of FO-MPA are compared against most popular and recent feature selection algorithms, such as Whale Optimization Algorithm (WOA) 49 , Henry Gas Solubility optimization (HGSO) 50 , Sine cosine Algorithm (SCA), Slime Mould Algorithm (SMA) 51 , Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) 52 , Harris Hawks Optimization (HHO) 53 , Genetic Algorithm (GA), and basic MPA. In this paper, each feature selection algorithm were exposed to select the produced feature vector from Inception aiming at selecting only the most relevant features.…”

Section: Resultsmentioning

confidence: 99%

COVID-19 image classification using deep features and fractional-order marine predators algorithm

Sahlol

Yousri

Ewees

et al. 2020

Sci Rep

211

141

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Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. Although convolutional neural networks (CNNs) is considered the current state-of-the-art image classification technique, it needs massive computational cost for deployment and training. In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. By achieving 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for dataset 1 and dataset 2, respectively, the proposed approach outperforms several CNNs and all recent works on COVID-19 images.

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Section: Resultsmentioning

confidence: 99%

COVID-19 image classification using deep features and fractional-order marine predators algorithm

Sahlol

Yousri

Ewees

et al. 2020

Sci Rep

211

141

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“…Inspired by the oscillation mode and food search patterns of slime mould in nature, Li et al [56] proposed a Slime Mould Algorithm (SMA). It incorporates three types of movements in cascade as well as in conjugation with oscillated search parameters for position updating.…”

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

2020

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In this research, we propose an enhanced Grey Wolf Optimizer (GWO) for designing the evolving Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) networks for time series analysis. To overcome the probability of stagnation at local optima and a slow convergence rate of the classical GWO algorithm, the newly proposed variant incorporates four distinctive search mechanisms. They comprise a nonlinear exploration scheme for dynamic search territory adjustment, a chaotic leadership dispatching strategy among the dominant wolves, a rectified spiral local exploitation action, as well as probability distribution-based leader enhancement. The evolving CNN-LSTM models are subsequently devised using the proposed GWO variant, where the network topology and learning hyperparameters are optimized for time series prediction and classification tasks. Evaluated using a number of benchmark problems, the proposed GWO-optimized CNN-LSTM models produce statistically significant results over those from several classical search methods and advanced GWO and Particle Swarm Optimization variants. Comparing with the baseline methods, the CNN-LSTM networks devised by the proposed GWO variant offer better representational capacities to not only capture the vital feature interactions, but also encapsulate the sophisticated dependencies in complex temporal contexts for undertaking time-series tasks.

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“…Some wellknown and creative methods are Genetic algorithm (GA) [30], particle swarm optimization (PSO) [31], [32], bacterial foraging optimization (BFO) [33], [34], teaching-learning based optimizer (TLBO) [35], [36], gray wolf optimizer (GWO) [20], [37], moth-flame optimization (MFO) [38], [39], grasshopper optimization algorithm (GOA) [40], [41], whale optimization algorithm (WOA) [42], [43], fruit fly optimization algorithm (FOA) [44], [45]. Slime mould algorithm (SMA) [46]. Due to its efficiency and effectiveness, these MEAs have been used in many problems, such as image segmentation in medical diagnosis, engineering design [47], feature selection [48], [49], workflow scheduling, and biopharmaceutical industry [39], [50]- [52].…”

Section: Proposed Sglhhomentioning

confidence: 99%

Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

et al. 2020

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Phthalic acid esters (PAEs) are organic pollutants and synthetic compounds and have adverse effects on human health. In this study, we investigated whether Di-2-Ethylhexyl phthalate (DEHP), one of many PAEs, has adverse effects on rats. Adult male Sprague-Dawley rats were treated daily by oral gavage with vehicle (corn oil) or DEHP at a dose of 3000 mg/kg/day for 15 days. The results showed that DEHP caused hepatotoxicity in rats. When compared with the control group, relative liver weights, and serum alanine, aminotransferase levels significantly increased after DEHP exposure. Hepatocyte swelling and degeneration were also found in DEHP-exposed rats. This study proposes an effective intelligence framework for the prediction of DEHP poisoning. The framework is designed by integrating an enhanced Harris hawks optimization (HHO) with a support vector machine (SVM), which is called SGLHHO-SVM. The core characteristic of the developed methodology is the SGLHHO algorithm that integrates the levy mechanism and two core operators abstracted from the salp swarm algorithm and grey wolf optimizer to enhance and restore the search capabilities of the HHO. The presented SGLHHO approach is used to tackle the key parameter pair optimization of the SVM, and it is also utilized to grab the optimal feature subset. Regarding the optimal feature subset and the pair parameter simultaneously, SGLHHO-SVM can autonomously predict the DEHP poisoning. The developed SGLHHO was conducted on 23 benchmark problems and compared with other state-of-the-art and competitive methods. The results demonstrate that the designed SGLHHO performs superior to other competitors on most benchmark problems. Furthermore, the proposed SGLHHO-SVM is also compared with other machine learning algorithms on a real-life DEHP sampled data. Statistical results verify the proposal can show better predictive property and higher stability on all for metrics. Therefore, the SGLHHO-SVM may be served as a potential computer-aided tool for the prediction of DEHP poisoning. INDEX TERMS Di-2-Ethylhexyl phthalate; Hepatotoxicity; Support vector machine; Harris hawks optimization; Salp swarm algorithm; Grey wolf optimizer.

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Slime mould algorithm: A new method for stochastic optimization

Cited by 2,209 publications

References 83 publications

COVID-19 image classification using deep features and fractional-order marine predators algorithm

COVID-19 image classification using deep features and fractional-order marine predators algorithm

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

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