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[Display omitted] • An improved Harris hawks optimization (EHHO) is proposed to deal with feature selection task. • By adding the hierarchy mechanism, the algorithm can achieve better convergence speed and accuracy. • EHHO performed well on classic test sets of CEC. • EHHO can obtain better performance than the original HHO with less features. The main feature selection methods include filter, wrapper-based, and embedded methods. Because of its characteristics, the wrapper method must include a swarm intelligence algorithm, and its performance in feature selection is closely related to the algorithm's quality. Therefore, it is essential to choose and design a suitable algorithm to improve the performance of the feature selection method based on the wrapper. Harris hawks optimization (HHO) is a superb optimization approach that has just been introduced. It has a high convergence rate and a powerful global search capability but it has an unsatisfactory optimization effect on high dimensional problems or complex problems. Therefore, we introduced a hierarchy to improve HHO's ability to deal with complex problems and feature selection. To make the algorithm obtain good accuracy with fewer features and run faster in feature selection, we improved HHO and named it EHHO. On 30 UCI datasets, the improved HHO (EHHO) can achieve very high classification accuracy with less running time and fewer features. We first conducted extensive experiments on 23 classical benchmark functions and compared EHHO with many state-of-the-art metaheuristic algorithms. Then we transform EHHO into binary EHHO (bEHHO) through the conversion function and verify the algorithm's ability in feature extraction on 30 UCI data sets. Experiments on 23 benchmark functions show that EHHO has better convergence speed and minimum convergence than other peers. At the same time, compared with HHO, EHHO can significantly improve the weakness of HHO in dealing with complex functions. Moreover, on 30 datasets in the UCI repository, the performance of bEHHO is better than other comparative optimization algorithms. Compared with the original bHHO, bEHHO can achieve excellent classification accuracy with fewer features and is also better than bHHO in running time. [ABSTRACT FROM AUTHOR]

Image segmentation methods have received widespread attention in face image recognition, which can divide each pixel in the image into different regions and effectively distinguish the face region from the background for further recognition. Threshold segmentation, a common image segmentation method, suffers from the problem that the computational complexity shows exponential growth with the increase in the segmentation threshold level. Therefore, in order to improve the segmentation quality and obtain the segmentation thresholds more efficiently, a multi-threshold image segmentation framework based on a meta-heuristic optimization technique combined with Kapur's entropy is proposed in this study. A meta-heuristic optimization method based on an improved grey wolf optimizer variant is proposed to optimize the 2D Kapur's entropy of the greyscale and nonlocal mean 2D histograms generated by image computation. In order to verify the advancement of the method, experiments compared with the state-of-the-art method on IEEE CEC2020 and face image segmentation public dataset were conducted in this paper. The proposed method has achieved better results than other methods in various tests at 18 thresholds with an average feature similarity of 0.8792, an average structural similarity of 0.8532, and an average peak signal-to-noise ratio of 24.9 dB. It can be used as an effective tool for face segmentation. [ABSTRACT FROM AUTHOR]

The sine cosine algorithm (SCA) was proposed for solving optimization tasks, of which the way to obtain the optimal solution is mainly through the continuous iteration of the sine and cosine update formulas. However, SCA also faces low population diversity and stagnation of locally optimal solutions. Hence, we try to eliminate these problems by proposing an enhanced version of SCA, named ESCA_PSO. ESCA_PSO is proposed based on hybrid SCA and particle swarm optimization (PSO) by incorporating multiple mutation strategies into the original SCA_PSO. To validate the effect of ESCA_PSO in handling global optimization problems, ESCA_PSO was compared with quality algorithms on various types of benchmark functions. In addition, the proposed ESCA_PSO was employed to tune the best parameters of support vector machines for dealing with medical diagnosis tasks. The results prove the efficiency of the proposed algorithms in solving optimization problems. [ABSTRACT FROM AUTHOR]

The artificial bee colony optimization (ABC) algorithm operates efficiently and converges well but still suffers from the problem of easily falling into local optimum, and there is room for improving the convergence speed. For this reason, this paper proposes a dynamic mechanism-assisted ABC algorithm (EABC), which contains a dynamic approximation strategy for the optimal solution and a periodic variable food source number strategy. The dynamic approximation of the optimal solution strategy improves the swarm position update formulation and increases the pre-convergence speed of the ABC algorithm. Utilizing a periodic variable food source number scheme allows for more rapid algorithm convergence while simultaneously producing higher variability and diminishing the chances of the algorithm becoming trapped in local optima. In addition, this paper proposes a multi-threshold image segmentation (MTIS) model for COVID-19 X-ray chest images based on EABC. In this paper, the optimization performance of EABC is verified on the benchmark function of IEEE CEC 2017. The effectiveness of the EABC-based MTIS model is also validated on COVID-19 X-ray chest images. • Propose a dynamic mechanical-assisted ABC, EABC, based on two new strategies. • An EABC-based NLM 2D histogram multi-threshold image segmentation method is proposed. • The EABC improves the convergence speed and accuracy of the standard algorithm. • The segmentation effect is verified by analyzing the quality evaluation index results. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy k-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fly optimization, and firefly algorithm, as well as three advanced machine learning methods including support vector machine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. The method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis. [ABSTRACT FROM AUTHOR]

• An advanced shuffled frog leaping algorithm (DSSRLFLA) is developed for model evaluation and feature selection. • A dynamic step size adjustment strategy based on historical information is designed to balance exploration and exploitation. • Specular reflection learning mechanism increases the possibility of searching for valid solutions and enhances the search ability. • An improved simulated annealing strategy is executed for each memetic to improve the efficiency of local exploitation. • DSSRLFLA can obtain higher quality optimal solutions compared to some competitive algorithms. The shuffled frog leaping algorithm is a new optimization algorithm proposed to solve the combinatorial optimization problem, which effectively combines the memetic algorithm based on a memetic algorithm and the particle swarm algorithm based on population behavior. The algorithm is widely used because it is easy to implement and requires few parameters to be adjusted. However, there are still some characteristics of this method that need to be improved because it is easy to fall into local optimization or poor search ability. To alleviate this limitation, a new version of the improved SFLA is proposed in this paper, which incorporates a dynamic step size adjustment strategy based on historical information, a specular reflection learning mechanism, and a simulated annealing mechanism based on chaotic mapping and levy flight. Firstly, the dynamic step size adjustment strategy based on historical information effectively helps to balance local exploration and global exploitation and alleviates the problem of falling into local optimum. Second, the specular reflection learning mechanism increases the possibility of searching for valid solutions in feasible domains and enhances the search ability of individuals in the population. Finally, an improved simulated annealing strategy is executed for each memetic, which improves the efficiency of local exploitation. In order to test the performance of the proposed algorithm, 31 test functions were selected from IEEE CEC2014 and 23 essential benchmark functions, and comparative experiments were carried out from the two dimensions of 30 and 100. Furthermore, a series of competing algorithms are selected, which involve nine classical standard algorithms, including PSO, BA, SSA, FA, SCA, WOA, GWO, MFO, and SFLA, as well as, six well-known improved algorithms, including LSFLA, DDSFLA, GOTLBO, ALCPSO, BLPSO, CLPSO. Furthermore, Wilcoxon signed-rank test and Friedman test are used as testing tools to illustrate the scalability of the proposed algorithm. From the analysis of the results, it can be seen that this proposed method has been effectively improved concerning stability and the quality of the optimal solution obtained from the search, both in low and high dimensions, and the ability to jump out of the local optimum has also been enhanced. In addition, to prove that this method has a reliable performance in discrete problems and continuous problems, DSSRLFLA is mapped into a discrete space, and 24 UCI data sets are also selected to evaluate the performance of the new feature selection method. The experimental results illustrate that this improved method can obtain fewer features and higher classification accuracy than some well-known feature selection methods. [ABSTRACT FROM AUTHOR]

• This paper proposes an improved grey wolf optimization (IGWO) for optimizing KELM. • A new hierarchical mechanism was established in the proposed IGWO. • Effectiveness of IGWO strategy is validated on functions and three practical applications. • Experimental results reveal the improved performance of the proposed algorithm. Since its introduction, kernel extreme learning machine (KELM) has been widely used in a number of areas. The parameters in the model have an important influence on the performance of KELM. Therefore, model parameters must be properly adjusted before they can be put into practical use. This study proposes a new parameter learning strategy based on an improved grey wolf optimization (IGWO) strategy, in which a new hierarchical mechanism was established to improve the stochastic behavior, and exploration capability of grey wolves. In the proposed mechanism, random local search around the optimal grey wolf was introduced in Beta grey wolves, and random global search was introduced in Omega grey wolves. The effectiveness of IGWO strategy is first validated on 10 commonly used benchmark functions. Results have shown that the proposed IGWO can find good balance between exploration and exploitation. In addition, when IGWO is applied to solve the parameter adjustment problem of KELM model, it also provides better performance than other seven meta-heuristic algorithms in three practical applications, including students' second major selection, thyroid cancer diagnosis and financial stress prediction. Therefore, the method proposed in this paper can serve as a good candidate tool for tuning the parameters of KELM, thus enabling the KELM model to achieve more promising results in practical applications. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high‐performance optimization method (SCBA) and added the Sobol sequence, vertical crossover and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA‐KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA‐KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2 and CYP1A1/2 have the most impact on rats. The SCBA‐KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment. [ABSTRACT FROM AUTHOR]

Abstract Paraquat (PQ) poisoning seriously harms the health of humanity. An effective diagnostic method for paraquat poisoned patients is a crucial concern. Nevertheless, it's difficult to identify the patients with low intake of PQ or delayed treatment. Here, a new efficient diagnostic approach to integrate machine learning and gas chromatography-mass spectrometry (GC–MS), named GEE, is proposed to identify the PQ poisoned patients. First, GC–MS provides the original data that efficiently identified the paraquat-poisoned patients. According to the high dimensionality of the original data, in the second stage, the chaos enhanced grey wolf optimization (EGWO) is adopted to search the optimal feature sets to improve the accuracy of identification. Finally, the extreme learning machine (ELM) is used to identify the PQ poisoned patients. To efficiently evaluate the proposed method, four measures were used in our experiments and comparisons were made with six other methods. The PQ-poisoned patients and robust volunteers can be well identified by GEE and the values of AUC, accuracy, sensitivity and specificity were 95.14%, 93.89%, 94.44% and 95.83%, respectively. Our experimental results demonstrated that GEE had better performance and might serve as a novel candidate diagnosis of PQ-poisoned patients. [ABSTRACT FROM AUTHOR]

The prognosis of paraquat (PQ) poisoning is highly correlated to plasma PQ concentration, which has been identified as the most important index in PQ poisoning. This study investigated the predictive value of coagulation, liver, and kidney indices in prognosticating PQ-poisoning patients, when aligned with plasma PQ concentrations. Coagulation, liver, and kidney indices were first analyzed by variance analysis, receiver operating characteristic curves, and Fisher discriminant analysis. Then, a new, intelligent, machine learning-based system was established to effectively provide prognostic analysis of PQ-poisoning patients based on a combination of the aforementioned indices. In the proposed system, an enhanced extreme learning machine wrapped with a grey wolf-optimization strategy was developed to predict the risk status from a pool of 103 patients (56 males and 47 females); of these, 52 subjects were deceased and 51 alive. The proposed method was rigorously evaluated against this real-life dataset, in terms of accuracy, Matthews correlation coefficients, sensitivity, and specificity. Additionally, the feature selection was investigated to identify correlating factors for risk status. The results demonstrated that there were significant differences in the coagulation, liver, and kidney indices between deceased and surviving subjects (p<0.05). Aspartate aminotransferase, prothrombin time, prothrombin activity, total bilirubin, direct bilirubin, indirect bilirubin, alanine aminotransferase, urea nitrogen, and creatinine were the most highly correlated indices in PQ poisoning and showed statistical significance (p<0.05) in predicting PQ-poisoning prognoses. According to the feature selection, the most important correlated indices were found to be associated with aspartate aminotransferase, the aspartate aminotransferase to alanine ratio, creatinine, prothrombin time, and prothrombin activity. The method proposed here showed excellent results that were better than that produced based on blood-PQ concentration alone. These promising results indicated that the combination of these indices can provide a new avenue for prognosticating the outcome of PQ poisoning. [ABSTRACT FROM AUTHOR]

Slime Mould Algorithm (SMA) has recently received much attention from researchers because of its simple structure, excellent optimisation capabilities, and acceptable convergence in dealing with various types of complex real-world problems. this study aims to retrieve, identify, summarise and analyse critical studies related to SMA development. Based on this, 98 SMA-related studies in the Web of Science were retrieved, selected, and identified. The two main review vectors were advanced versions of SMAs and application domains. First, we counted and analysed various advanced versions of SMAs, summarised, classified, and discussed their improvement methods and directions. Secondly, we sort out the application domains of SMA and analyse the role, development status, and shortcomings of SMA in each domain. A survey based on the existing literature shows that SMAs clearly outperform some established metaheuristics in terms of speed and accuracy in handling various benchmark problems and solving multiple realistic optimization problems. This review not only suggests possible future directions in the field but, due to the inclusion of graphical and tabular comparisons of various properties, also provides future researchers with a comprehensive source of information about SMA and advanced versions of SAMs and the scope of adaptation for multiple application domains. [ABSTRACT FROM AUTHOR]

The determination of photovoltaic (PV) parameters is of great importance for the reliability of solar system operation, continuity of the load power consumption, and control management of the energy source. Therefore, this study proposes an advanced backtracking search optimization algorithm (BSA) equipped with teaching and learning‐based optimization (TLBO), named TLBOBSA, to accurately simulate the PV model. During the evaluation of the proposed algorithm, the concept of teaching from TLBO is introduced into the BSA to guide optimal individuals, thus improving the convergence rate of the algorithm. The learning behavior among individuals in the student phase of TLBO facilitates interindividual learning and provides beneficial information for its evolution, which is introduced into the BSA to ensure the diversity of the population. The comprehensive test results of different PV module models in different environmental conditions show that the proposed algorithm is more advantageous for parameter extraction than other existing algorithms. This can be seen in the simulation experiments of two commercial PV models, where the simulated current is consistent with the measured current at each measured voltage. This demonstrates that the proposed TLBOBSA is an accurate and reliable tool for evaluating unknown parameters of PV models. [ABSTRACT FROM AUTHOR]

• After introducing the Laplacian strategy and Nelder-Mead simplex mechanism to the basic hunger games search, a novel LNMHGS algorithm is developed. • The proposed LNMHGS compares against the original HGS and other algorithms to optimize unknown parameters of static PV systems. • The LNMHGS method is used to extract SDM and DDM models' parameters on ST40, KC200GT and SM55 datasheet under different environmental conditions. • The results show the fast convergence and stable convergence of the proposed LNMHGS algorithm to identify parameters for PV models. Photovoltaic (PV) technology can convert solar energy to electric power, which is an essential tool for future years. Subsequently, several static solar PV models have been designed to simulate the current in a PV cell. However, the modeling process of PV systems requires extracting the unknown parameters of these cells, which can be modeled as an optimization problem. However, this is a very challenging task as it is multimodal and nonlinear. In this case, a Laplacian Nelder-Mead hunger games search (HGS) method, denoted as LNMHGS, is proposed for parameter extraction of static solar PV models. It realizes the equilibrium between exploitation and exploration by introducing the Laplacian strategy and Nelder-Mead simplex mechanism to hunger name search. LNMHGS compares against many recent methods and stands out by efficiently estimating static PV models' parameters. The simulation outcomes show that the root mean square error (RMSE) values and standard deviation offered by LNMHGS are smaller than other algorithms. Meanwhile, LNMHGS obtains the best performance both in different light conditions and at different temperature conditions. Therefore, LNMHGS is a promising, reliable, and feasible alternative for optimizing the parameters for PV systems. [ABSTRACT FROM AUTHOR]

Tuberculous pleural effusion poses a significant threat to human health due to its potential for severe disease and mortality. Without timely treatment, it may lead to fatal consequences. Therefore, early identification and prompt treatment are crucial for preventing problems such as chronic lung disease, respiratory failure, and death. This study proposes an enhanced differential evolution algorithm based on colony predation and dispersed foraging strategies. A series of experiments conducted on the IEEE CEC 2017 competition dataset validated the global optimization capability of the method. Additionally, a binary version of the algorithm is introduced to assess the algorithm's ability to address feature selection problems. Comprehensive comparisons of the effectiveness of the proposed algorithm with 8 similar algorithms were conducted using public datasets with feature sizes ranging from 10 to 10,000. Experimental results demonstrate that the proposed method is an effective feature selection approach. Furthermore, a predictive model for tuberculous pleural effusion is established by integrating the proposed algorithm with support vector machines. The performance of the proposed model is validated using clinical records collected from 140 tuberculous pleural effusion patients, totaling 10,780 instances. Experimental results indicate that the proposed model can identify key correlated indicators such as pleural effusion adenosine deaminase, temperature, white blood cell count, and pleural effusion color, aiding in the clinical feature analysis of tuberculous pleural effusion and providing early warning for its treatment and prediction. • The CFDE is designed and verified on benchmarks. • The binary version of CFDE (bCFDE) is designed for TBPE. • The bCFDE is verified on 24 datasets and compared to other peers. • The bCFDE-SVM is applied to TBPE successfully. [ABSTRACT FROM AUTHOR]

Summary: The inherent multimodal and nonlinear characteristics of solar photovoltaic (PV) systems make it challenging to accurately extract PV parameters. Therefore, this study proposes an efficient and accurate optimization technique, improved Hunger Games Search, to identify unknown parameters in PV systems named IHGS. IHGS makes full use of the population information generated by the differential vectors while incorporating the quantum rotation gate strategy and the Nelder‐Mead simplex method (NMs) are fused to further increase the population diversity while rotating the corresponding positions of the search agents, thus efficiently exploring the neighborhood of the optimal solution in the decision space and improving the stability of the algorithm in developing the global optimal solution. The first part of the experiments is to simply verify the effectiveness and the stable merit‐seeking ability of the IHGS to handle complex functions by putting the proposed IHGS to test on the IEEE CEC 2017 benchmark test suite. The efficiency of IHGS is verified based on Wilcoxon singed‐rank test and Friedman tests. Next, we apply IHGS to three different PV module models (single‐diode, double‐diode, and PV module) for PV parameter extraction experiments to verify the efficiency and accuracy of IHGS in extracting PV parameters. The root mean square error produced by the IHGS proposed in this study is 9.8602E‐04, 9.8248E‐04, and 2.4251E‐03 for SDM, DDM, and PV modules, respectively. Examples show that IHGS outperforms HGS and other advanced algorithms in terms of convergence speed, data fitting, consistency, and stability. Finally, we perform the PV module parameter extraction problem on three commercial PV models (thin‐film ST40, monocrystalline SM55, and multicrystalline KC200GT). We observe that IHGS can still maintain high accuracy when dealing with commercial PV models under complex environments such as different irradiation and temperature. The results show that IHGS is a valuable optimization technique for solar PV parameter estimation based on multi‐model prediction. [ABSTRACT FROM AUTHOR]

With countless devices connected to the Internet of Things, trust mechanisms are especially important. IoT devices are more deeply embedded in the privacy of people's lives, and their security issues cannot be ignored. Smart contracts backed by blockchain technology have the potential to solve these problems. Therefore, the security of smart contracts cannot be ignored. We propose a flexible and systematic hybrid model, which we call the Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC). The model showed excellent performance benefits in smart contract vulnerability detection. In addition, we propose a serial-parallel convolution (SPCNN) suitable for our hybrid model. It can extract features from the input sequence for multivariate combinations while retaining temporal structure and location information. The Ensemble Classifier is used in the classification phase of the model to enhance its robustness. In addition, we focused on six typical smart contract vulnerabilities and constructed two datasets, CESC and UCESC, for multi-task vulnerability detection in our experiments. Numerous experiments showed that SPCBIG-EC is better than most existing methods. It is worth mentioning that SPCBIG-EC can achieve F1-scores of 96.74%, 91.62%, and 95.00% for reentrancy, timestamp dependency, and infinite loop vulnerability detection. [ABSTRACT FROM AUTHOR]

Blockchain presents a chance to address the security and privacy issues of the Internet of Things; however, blockchain itself has certain security issues. How to accurately identify smart contract vulnerabilities is one of the key issues at hand. Most existing methods require large-scale data support to avoid overfitting; machine learning (ML) models trained on small-scale vulnerability data are often difficult to produce satisfactory results in smart contract vulnerability prediction. However, in the real world, collecting contractual vulnerability data requires huge human and time costs. To alleviate these problems, this paper proposed an ensemble learning (EL)-based contract vulnerability prediction method, which is based on seven different neural networks using contract vulnerability data for contract-level vulnerability detection. Seven neural network (NN) models were first pretrained using an information graph (IG) consisting of source datasets, which then were integrated into an ensemble model called Smart Contract Vulnerability Detection method based on Information Graph and Ensemble Learning (SCVDIE). The effectiveness of the SCVDIE model was verified using a target dataset composed of IG, and then its performances were compared with static tools and seven independent data-driven methods. The verification and comparison results show that the proposed SCVDIE method has higher accuracy and robustness than other data-driven methods in the target task of predicting smart contract vulnerabilities. [ABSTRACT FROM AUTHOR]

In the context of the rapid development of blockchain technology, smart contracts have also been widely used in the Internet of Things, finance, healthcare, and other fields. There has been an explosion in the number of smart contracts, and at the same time, the security of smart contracts has received widespread attention because of the financial losses caused by smart contract vulnerabilities. Existing analysis tools can detect many smart contract security vulnerabilities, but because they rely too heavily on hard rules defined by experts when detecting smart contract vulnerabilities, the time to perform the detection increases significantly as the complexity of the smart contract increases. In the present study, we propose a novel hybrid deep learning model named CBGRU that strategically combines different word embedding (Word2Vec, FastText) with different deep learning methods (LSTM, GRU, BiLSTM, CNN, BiGRU). The model extracts features through different deep learning models and combine these features for smart contract vulnerability detection. On the currently publicly available dataset SmartBugs Dataset-Wild, we demonstrate that the CBGRU hybrid model has great smart contract vulnerability detection performance through a series of experiments. By comparing the performance of the proposed model with that of past studies, the CBGRU model has better smart contract vulnerability detection performance. [ABSTRACT FROM AUTHOR]

• An enhanced political optimizer (QNMPO) is proposed to realize the extraction of photovoltaic models parameters. • The performance of the proposed QNMPO algorithm is compared with some well-known competing algorithms. • The stability performance of QNMPO was tested on three commercial PV models at different temperature levels and irradiation levels. • QNMPO exhibits better convergence speed and accuracy. Evaluating the PV system parameters is crucial for the more efficient utilization of sustainable solar energy. Due to PV systems' nonlinear and multimodal nature, efficient evaluation of PV system parameters is still a very challenging task. This paper proposes an improved political optimization algorithm, namely the QNMPO algorithm, which combines quantum rotation gate and Nelder-Mead simplex (NMs). It can be used to evaluate unknown parameters of PV systems and solar cells efficiently. In QNMPO, the quantum rotation gate method can rotate the population of agents to a more favorable position for finding the optimal solution, making the individuals in the middle of the population communicate more frequently and enhancing the population's diversity. In addition, the NMs method can improve the solution quality by better searching the neighborhoods near the optimal solution and then strengthen the advantage of local development of the algorithm so that it can converge to the global optimal solution faster. The developed QNMPO algorithm extracts the unknown parameters for single diode, double diode, triple diode and photovoltaic modules in this study. It is compared with some other representative algorithms. The experimental results show that QNMPO outperforms similar algorithms in convergence speed, solution accuracy, and better solution performance. QNMPO was then used to successfully extract the best parameter values for three real PV models (three commercial PV models (thin-film ST40, monocrystalline SM55, and multi crystalline KC200GT)) at different temperatures and different irradiance levels. The results also show that QNMPO performs consistently and accurately enough under varying temperatures and irradiation. Therefore, the QNMPO algorithm proposed in this paper can be a promising parameter extraction method for solar PV systems with good performance. [ABSTRACT FROM AUTHOR]

• BFGSOLMFO is proposed for global optimization and real-world problems. • OL is used to enhance exploitation and exploration ability of MFO. • BFGS is employed to further excavate the potential global best solution. As a typical emergent swarm intelligence algorithm, Moth-Flame Optimization (MFO) has been created to deal with global optimization problems. Since the introduction, it has been applied to various optimization problems. However, MFO may have the trouble of getting into the local best, and the convergence rate cannot be satisfying when handling the high-dimensional and some multimodal problems. In this work, an enhanced MFO integrated with orthogonal learning (OL) and Broyden-Fletcher-Goldfarb-Shanno (BFGS), which we called BFGSOLMFO, is proposed to alleviate the stagnation shortcomings and accelerate the performance of well-regarded MFO. In the BFGSOLMFO, OL is used to construct a better candidate solution for each moth and then guide the whole population to a reasonable potential area. Meanwhile, in each iteration, after the evolution of population finished and the global optima are obtainable, BFGS is employed to further excavate the potential of the global best moth in the current population. With the aim of evaluating the efficacy of the BFGSOLMFO, first of all, the IEEE CEC2014 benchmark set is utilized to measure the performance in solving function optimizations with high-dimensional and multimodal characteristics. Both sets of the IEEE CEC2011 real-world benchmark problems and the three constrained engineering optimization problems are adopted to estimate the performance of BFGSOLMFO in tackling practical scenarios. In all the experiments, the developed BFGSOLMFO is compared with state-of-the-art advanced algorithms. Experimental results and statistical tests demonstrate that the proposed method outperforms the basic MFO and a comprehensive set of advanced algorithms. [ABSTRACT FROM AUTHOR]

Most of the energy consumption is now being used to supply human demand for electricity, which has increased the burden of power system planning to some extent, and thus, researchers proposed solutions to the optimal power flow (OPF) problem. In such a background, flexible AC transmission system (FACTS) devices are widely used in modern power systems to alleviate human power demand and improve network congestion problems. This research proposes a better-quality sine cosine algorithm (CPSCA) based on a crossover mechanism and pattern search algorithm for optimizing multiple objectives, such as system generation cost and transmission losses when we have multiple types of FACTS devices in the power system. The proposed method uses sine and cosine theory and evolutionary strategy to continuously sample and update the framework and improve the quality of the solutions. The method uses a class of vertical and horizontal crossover operators and pattern-shifting techniques to find the optimal solution set. In addition, the Weibull probability density function was used in this study to model uncertainty in wind energy, while direct costs, penalty costs, and standby costs were considered in constructing the target model. Various types of FACTS devices, such as thyristor-controlled series compensators, thyristor-controlled phase shifters, and static reactive power compensators, are also inserted into the model as power in the target optimization problem. The proposed algorithm has experimented on an IEEE30 bus test system. Based on the experimental results, CPSCA achieves an optimal total cost of 806.0225 $ / h. In terms of power loss of the bus system, CPSCA reduces the total power loss value from 2.042 MW to 1.41 MW compared to the original sine cosine algorithm, which is a 31% improvement and has an optimal power loss value. Therefore, the simulation results demonstrate that the proposed algorithm is an effective technique for optimizing the optimal power flow of the entire power system and FACTS equipment. • A vertical and horizontal crossover mechanism and the pattern search method are embedded into SCA algorithm. • The proposed method CPSCA to solve the optimal power flow with a wind power and FACTS devices. • A constraint handling approach (SF) was used to solve the generator constraints. • The CPSCA has superiority over other methods in terms of solution quality. [ABSTRACT FROM AUTHOR]

Background and Objective: In countries with high prevalence of tuberculosis (TB), clinicians often diagnose tuberculous pleural effusion (TPE) by using diagnostic tests, which have not only poor sensitivity, but poor availability as well. The aim of our study is to develop a new artificial intelligence based diagnostic model that is accurate, fast, non-invasive and cost effective to diagnose TPE. It is expected that a tool derived based on the model be installed on simple computer devices (such as smart phones and tablets) and be used by clinicians widely. Methods: For this study, data of 140 patients whose clinical signs, routine blood test results, blood biochemistry markers, pleural fluid cell type and count, and pleural fluid biochemical tests’ results were prospectively collected into a database. An Artificial intelligence based diagnostic model, which employs moth flame optimization based support vector machine with feature selection (FS-MFO-SVM), is constructed to predict the diagnosis of TPE. Results: The optimal model results in an average of 95% accuracy (ACC), 0.9564 the area under the receiver operating characteristic curve (AUC), 93.35% sensitivity, and 97.57% specificity for FS-MFO-SVM. Conclusions: The proposed artificial intelligence based diagnostic model is found to be highly reliable for diagnosing TPE based on simple clinical signs, blood samples and pleural effusion samples. Therefore, the proposed model can be widely used in clinical practice and further evaluated for use as a substitute of invasive pleural biopsies. [ABSTRACT FROM AUTHOR]

This study proposes a novel learning scheme for the kernel extreme learning machine ( KELM ) based on the chaotic moth-flame optimization ( CMFO ) strategy. In the proposed scheme, CMFO simultaneously performs parameter optimization and feature selection. The proposed methodology is rigorously compared to several other competitive KELM models that are based on the original moth-flame optimization, particle swarm optimization, and genetic algorithms. The comparison is made using the medical diagnosis problems of Parkinson's disease and breast cancer. And the proposed method has successfully been applied to practical medical diagnosis cases. The experimental results demonstrate that, compared to the alternative methods, the proposed method offers significantly better classification performance and also obtains a smaller feature subset. Promisingly, the proposed CMFOFS - KELM , can serve as an effective and efficient computer aided tool for medical diagnosis in the field of medical decision making. [ABSTRACT FROM AUTHOR]

Background and objectives It is important to be able to accurately distinguish between benign and malignant thyroid nodules in order to make appropriate clinical decisions. The purpose of this study was to improve the effectiveness and efficiency for discriminating the malignant from benign thyroid cancers based on the Ultrasonography (US) features. Methods There were 114 benign nodules in 106 patients (82 women and 24 men) and 89 malignant nodules in 81 patients (69 women and 12 men) included in this study. The potential of extreme learning machine (ELM) has been explored for the first time to discriminate malignant and benign thyroid nodules based on the sonographic features in ultrasound images. The influence of two key parameters (the number of hidden neurons and type of activation function) on the performance of ELM was investigated. The relationship between feature subsets obtained by the feature selection method and the classification performance of ELM was also examined. A real-life dataset was used to evaluate the effectiveness of the proposed method in terms of classification accuracy, sensitivity, specificity, and area under the ROC (receiver operating characteristic) curve (AUC). Results The results demonstrate that there are significant differences between the malignant and benign thyroid nodules (p-value<0.01), the most discriminative features are echogenicity, calcification, margin, composition and shape. Compared with other methods, the proposed method not only has achieved very promising classification accuracy via 10-fold cross-validation (CV) scheme, but also greatly reduced the computational cost compared to other counterparts. The proposed ELM-based approach achieves 87.72% ACC, 0.8672 AUC, 78.89% sensitivity, and 94.55% specificity. Conclusions Based on the empirical analysis, the proposed ELM-based approach for thyroid cancer detection has promising potential in clinical use, and it can be of assistance as an optional tool for the clinicians. [ABSTRACT FROM AUTHOR]

This study proposes a new kernel extreme learning machine (KELM) parameter tuning strategy using a novel swarm intelligence algorithm called grey wolf optimization (GWO). GWO, which simulates the social hierarchy and hunting behavior of grey wolves in nature, is adopted to construct an effective KELM model for bankruptcy prediction. The derived model GWO-KELM is rigorously compared with three competitive KELM methods, which are typical in a comprehensive set of methods including particle swarm optimization-based KELM, genetic algorithm-based KELM, grid-search technique-based KELM, extreme learning machine, improved extreme learning machine, support vector machines and random forest, on two real-life datasets via 10-fold cross validation analysis. Results obtained clearly confirm the superiority of the developed model in terms of classification accuracy (training, validation, test), Type I error, Type II error, area under the receiver operating characteristic curve (AUC) criterion as well as computational time. Therefore, the proposed GWO-KELM prediction model is promising to serve as a powerful early warning tool with excellent performance for bankruptcy prediction. [ABSTRACT FROM AUTHOR]

• This paper proposes an enhanced SCA algorithm variant, called ARSCA. • ARSCA improves the convergence accuracy and avoids premature convergence. • This paper compares ARSCA with classical algorithms, variations of other algorithms. • ARSCA is applied to six engineering design problems and its results are satisfactory. The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA. [ABSTRACT FROM AUTHOR]

• This paper proposes an enhanced SCA algorithm variant, called ARSCA. • ARSCA improves the convergence accuracy and avoids premature convergence. • This paper compares ARSCA with classical algorithms, variations of other algorithms. • ARSCA is applied to six engineering design problems and its results are satisfactory. The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA. [ABSTRACT FROM AUTHOR]

In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts. [ABSTRACT FROM AUTHOR]

The complexity and scale of high-performance computer systems are rapidly increasing, so fault tolerance is becoming a critical challenge. In this paper, we consider the impact of multiple proactive actions on proactive fault tolerance and periodic checkpointing. We extended Aupy's model in the presence of multiple proactive actions, including proactive checkpointing and task migration. We then propose optimal strategies for deciding when to trust predictions, and provide algorithms for the optimal storage interval for periodic checkpointing. The results show that the proposed method can significantly improve system productivity. Our case study indicates that the recall of the predictor is more important for small platforms, and that precision becomes increasingly important as the scale of the system increases. [ABSTRACT FROM AUTHOR]

The slime mould algorithm (SMA) is a logical swarm-based stochastic optimizer that is easy to understand and has a strong optimization capability. However, the SMA is not suitable for solving multimodal and hybrid functions. Therefore, in the present study, to enhance the SMA and maintain population diversity, a dispersed foraging SMA (DFSMA) with a dispersed foraging strategy is proposed. We conducted extensive experiments based on several functions in IEEE CEC2017. The DFSMA were compared with 11 other meta-heuristic algorithms (MAs), 10 advanced algorithms, and 3 recently proposed algorithms. Moreover, to conduct more systematic data analyses, the experimental results were further evaluated using the Wilcoxon signed-rank test. The DFSMA was shown to outperform other optimizers in terms of convergence speed and accuracy. In addition, the binary DFSMA (BDFSMA) was obtained using the transform function. The performance of the BDFSMA was evaluated on 12 datasets in the UCI repository. The experimental results reveal that the BDFSMA performs better than the original SMA, and that, compared with other optimization algorithms, it improves classification accuracy and reduces the number of selected features, demonstrating its practical engineering value in spatial search and feature selection. • An improved slime mould algorithm (DFSMA) is proposed for feature selection. • The performance of DFSMA is verified by comparing with several famous algorithms. • DFSMA has faster convergence speed and accuracy compared with others. • DFSMA has achieved higher classification accuracy and smaller number of features. [ABSTRACT FROM AUTHOR]

• An improved Harris Hawks Optimizer (CCNMHHO) is proposed for photovoltaic systems. • The performance of CCNMHHO is compared with some well-known competitive algorithms. • Three PV models are simulated to verify the effectiveness of CCNMHHO. • This method has enhanced the convergence speed and accuracy in various conditions. An improved Harris hawks optimization is proposed in this work to facilitate the simulation of an efficient photovoltaic system and extraction of unknown parameters, which combines horizontal and vertical crossover mechanism of the crisscross optimizer and Nelder-Mead simplex algorithm, named CCNMHHO. In CCNMHHO, the cores appeared in the crisscross optimizer are utilized to enrich the information exchange between the individuals and avoid the problem of dimensional stagnation of individuals all through the iterations. Hence, it enhances to change to improve the population quality and prevent the shortcoming of falling into a local optimum. In contrast, the Nelder-Mead simplex algorithm is employed in the proposed CCNMHHO methodology. Nelder-Mead simplex helps to improve individual searching capabilities in performing the local search phase and showing a faster convergence to optimal values. Compared to some algorithms that have a competitive performance in dealing with this type of problem, CCNMHHO has a faster convergence speed, and it shows high stability. In different environments, the experimental data obtained by this improved Harris hawks Optimization can reveal a high agreement with the measurement data. The experimental results show that the proposed method not only is very competitive in extracting the unknown parameters of different PV models compared to other state-of-the-art algorithms but also perform well in dealing with the complex outdoor environments such as different temperature and radiance. Therefore, we observed that the CCNMHHO could be considered as a reliable and efficient method in solving a class of cases for the assessment of unknown parameters of solar cells and photovoltaic models. For post-publication guidance, supports, and materials for this research, please refer to the supporting homepage: http://aliasgharheidari.com. [ABSTRACT FROM AUTHOR]