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Feature selection (FS) is a crucial preprocessing step that aims to eliminate irrelevant and redundant features, reduce the dimensionality of the feature space, and enhance clustering efficiency and effectiveness. FS is categorized as NP-Hard due to the high number of existing solutions. Various metaheuristic methods have been developed to address the FS problem, yielding promising results. Particularly, particle swarm optimization (PSO), an evolutionary computing (EC) approach guided by swarm intelligence, has gained widespread adoption owing to its implementation simplicity and potential for global search. This paper analyzes several variants of PSO algorithms and introduces a new FS method called HPSO. The proposed approach utilizes an asynchronously adaptive inertia weight and an improved constriction factor. Additionally, it incorporates a chaotic map and a MAD fitness function with a feature count penalty to tackle the clustering FS problem. The efficiency of the developed method is evaluated against the genetic algorithm (GA) and well-known variants of PSO algorithms, including PSOs with fixed inertia weights, PSOs with improved inertia weights, PSOs with fixed constriction factors, PSOs with improved constriction factors, PSOs with adaptive inertia weights, and PSO's includes advanced learning exemplars and sophisticated structure topologies. This paper assesses two different reference text data sets, Reuters-21578 and Webkb. In comparison with competitive methods, the proposed HPSO method achieves higher clustering precision and selects a more informative feature set. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction. [ABSTRACT FROM AUTHOR]

The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees' individual intelligence and hunting behaviors. In this algorithm, the hunting process consists of four steps: driving, blocking, chasing, and attacking. Because of the novelty of ChOA, the steps of the hunting process have been modeled in the simplest possible way, leading to slow and premature convergence similar to other iterative algorithms. This paper proposes six spiral functions and introduces two novel hybrid spiral functions (SEB-ChOA) to rectify the abovementioned deficiencies. The SEB-ChOAs' performance is evaluated on 23 standard benchmarks, 20 benchmarks of IEEE CEC-2005, 10 cases of IEEE CEC06-2019 test-suite, and 12 constrained real-world engineering problems of IEEE CEC-2020. The SEB-ChOAs are compared with three groups of optimization algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as the most well-known optimization algorithms, Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Ant Lion Optimization (ALO), Henry Gas Solubility Optimization (HGSO), as almost novel optimization algorithms, and jDE100 and DISHchain1e+12, as winners of IEEE CEC06-2019 competition, and also EBOwithCMAR and CIPDE as superior secondary optimization algorithms. The SEB-ChOAs reached the first rank among almost all benchmarks and demonstrated very competitive results compared to jDE100 and DISHchain1e+12 as the best-performing optimizers. Statistical evidence shows that the SEB-ChOA outperforms the PSO, GA, SMA, MPA, ALO, and HGSO optimizers while producing results comparable to those of the jDE100 and DISHchain1e+12 algorithms. [ABSTRACT FROM AUTHOR]

Document classification is becoming increasingly essential for the vast number of documents available in digital libraries, emails, the Internet, etc. Textual records frequently contain non-discriminative (noisy and irrelevant) terms that are also high-dimensional, resulting in higher computing costs and poorer learning performance in Text Classification (TC). Feature selection (FS), which tries to discover discriminate terms or features from the textual data, is one of the most effective tasks for this issue. This paper introduces a novel multi-stage term-weighting scheme-based FS model designed for the single-label TC system to obtain the optimal set of features. We have also developed a hybrid deep learning fine-tuning network based on Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) for the classification stage. The FS approach is worked on two-stage criteria. The filter model is used in the first stage, and the multi-objective wrapper model, an upgraded version of the Whale Optimization Algorithm (WOA) with Particle Swarm Optimization (PSO), is used in the second stage. The objective function in the above wrapper model is based on a tri-objective principle. It uses the Pareto front technique to discover the optimal set of features. Here in the wrapper model, a novel selection strategy has been introduced to select the whale instead of the random whale. The proposed work is evaluated on four popular benchmark text corpora, of which two are binary class, and two are multi-class. The suggested FS technique is compared against classic Machine Learning (ML) and deep learning classifiers. The results of the experiments reveal that the recommended FS technique is more effective in obtaining better results than the other results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel hybrid heuristic algorithm, particle swarm optimization, and whale optimization algorithm (PSO–WOA), to solve a multi-objective optimization problem relating to point-to-point trajectory planning of space robots. First of all, the kinematics of the space robot is introduced, and the motion of each revolute joint of the manipulator is parameterized by Bézier curve. Then, contradictory objective functions are proposed, and the trajectory planning problem is transformed into a multi-objective optimization problem. The pose of the end-effector at the end of motion is set as the primary objective. The base disturbance, execution time, and manipulability of the end-effector are also taken into account. Furthermore, self-collision avoidance during the motion is also considered. The trajectory planning problem finally comes down to finding an optimal parameter of the Bézier curve for each joint. We propose a novel hybrid PSO–WOA, which is supposed to take advantages of the best of both methods: the exploration feature of the WOA and exploitation feature of the PSO. In order to enhance the performance of the PSO–WOA, the good point set and lévy flight stochastic steps are employed for the initialization and updating process, respectively. The proposed method is applied to generate an optimal trajectory for a redundant free-floating space robot. The simulation results demonstrate the effectiveness of the PSO–WOA. [ABSTRACT FROM AUTHOR]

The application of soft computing techniques can be largely found in engineering sciences. These include the design and optimization of navigation systems for use in land, sea, and air transportation systems. In this paper, an attempt is made to leverage on novel metaheuristic optimization approaches for designing integrated navigation systems. For this purpose, a simplified version of the inclined planes system optimization (called SIPO) algorithm alongside its two standard and modified versions are used in comparison with the two conventional methods of genetic algorithm and particle swarm optimization. Considerations are made on an INS/GNSS problem with IMU MEMS modules. Outputs are presented in terms of statistical and performance indicators, such as runtime, fitness, convergence, navigation accuracy (velocity, latitude, longitude, altitude, roll, pitch, yaw), and routing along with the ranking of algorithms. Competitive performance and relative superiority of the standard IPO over other methods in evaluating results have been confirmed. So that compared to other state-of-the-art algorithms (GA, PSO, IPO, and MIPO), the best runtime rank with a value of 6/4 by SIPO and the best performance rank of fitness, navigation accuracy for the two assumed IMU modules, and the total rank with values of 4/4, 149/60, 165/60, and 332/128 obtained by IPO, respectively. [ABSTRACT FROM AUTHOR]

Computations that mimic nature are known as nature-inspired computing. Nature presents a wealthy source of thoughts and ideas for computing. The use of natural galvanized techniques has been found to provide machine solutions to complex problems. One of the challenging issues among researchers is high-dimensional data which contains a large number of unwanted, redundant, and irrelevant features. These redundant or unwanted features reduce the accuracy of machine learning models. Therefore, to solve this problem nowadays metaheuristic techniques are being used. The paper presents both surveys as well as comparison of five metaheuristic algorithms for feature selection. A wrapper-based feature selection approach using five nature-inspired techniques for feature selection has been applied. The binary version of the five swarm-based nature-inspired algorithms (NIAs), namely particle swarm optimization, whale optimization algorithm (WOA), grey wolf optimization (GWO), firefly algorithm, and bat algorithm. WOA and GWO are recent algorithms used for finding optimal feature subsets when there is no empirical information. The S-shape transfer function has been used to convert the continuous value to binary form and K-nearest neighbor is used to calculate the classification accuracy of selected feature subsets. To validate the results of the selected NIAs eleven benchmark datasets from the UCI repository are used. The strength of each NIA has been verified using a nonparametric test called the Friedman rank and Holm test. p value obtained shows that WOA is statistically significant and performs better than other models. [ABSTRACT FROM AUTHOR]

Feature extraction, feature clustering, feature selection are suitable to enhance learning performance, reduce computational complexity, create better generalizable models, and reduce required storage. Although there are several feature reduction techniques, still it remains one of the research hot spots in the field of data mining and machine learning groups. Owl search algorithm (OSA) is one of the recent metaheuristic optimization algorithms that mimic the hunting mechanism of owls in dark. However, OSA suffers from the same problem faced by many other optimization algorithms and tends to fall in local optima and premature convergence. To overcome these problems, two improvements for OSA algorithm are proposed in this paper. The first improvement uses a fuzzy system which is responsible to tune a control parameter in the updating phase of OSA. The second improvement includes the fuzzy system along with modifying the updating equation of OSA to enhance the exploration activity. In addition, this paper presents a new feature extraction technique for regression problems based on the improved OSA, called Fuzzy Owl Clustering Dimension Reduction (FOCDR). We apply a method that uses three weighting methods (i.e., soft, hard, and mixed) to extract new features based on the generated clusters. The experiment is divided into two parts. In the first part, the performance of the OSA algorithm and two improvement versions are analyzed with ten benchmark functions. The results show that the proposed versions on average can improve the convergence rate by 6.75% and 14.2% compared to OSA in solving complex problems. The second part is conducted to show FOCDR's ability for feature selection problems. The effectiveness of FOCDR has been evaluated using four benchmark datasets and a real-world case study. [ABSTRACT FROM AUTHOR]

Protecting water resources from pollution is one of the most important challenges facing water management researchers. The governing equation for river pollution is mostly the advection–dispersion equation, with considering the longitudinal dispersion coefficient as its most important effective parameter. The purpose of this paper is to develop a new framework for accurate prediction of the longitudinal dispersion coefficient of rivers based on artificial intelligence (AI) methods. To do this, we used a combination of multilayer perceptron (MLP), one of the most robust neural networks, and a novel metaheuristic algorithm, namely Harris hawk optimization (HHO). Besides, two optimized MLP models with particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were utilized to demonstrate the accuracy of the proposed model. To evaluate the developed models, 164 series of data collected from previous studies, including hydraulic and geometric parameters of rivers, were used. The indicated results proved the efficiency of the HHO to improve the optimum auto-selection of the AI models. Thus, the recorded results show very high accuracy of the newly developed model, MLP-HHO compared to others. Furthermore, to increase the prediction accuracy, a K-means clustering technique is coupled with MLP-HHO model during dividing the data to train and test categories. The proposed hybrid K-means-MLP-HHO model with coefficient of determination (R2) and root mean square error (RMSE), of 0.97 and 30.94 m2/s, respectively, significantly outperformed all existing and AI-based models. Furthermore, the sensitivity analysis showed that the flow width is the most influential factor in predicting the longitudinal dispersion coefficient. [ABSTRACT FROM AUTHOR]

Deep large excavations in urban areas are an important engineering challenge, whereas secant piling techniques are among the best solutions to have a safe workplace environment. Optimal design of these structures will increase efficiency as well as reduce costs. In this paper, the optimum design of secant pile walls as a retaining system of a deep excavation pit is evaluated. For this purpose, an on-going Tabriz metro station project is investigated as the case study. The structural piles are made of steel material with a hollow pipe section. A layer of struts is also considered for the horizontal bracing of the excavation pit. A detailed finite element model is developed in the OpenSees platform in order to perform static analyses. The optimization of the retaining system is conducted by the mean of four different metaheuristic algorithms including genetic, particle swarm optimization, bee, and biogeography-based optimization algorithms. The total cost of retaining structures is considered as an objective function, which should be minimized in the design space of the variables. The results highlight the excellence of the bees algorithm in achieving a minimum cost, lower dispersion, and rapid convergence rate. The optimum placement of the bracing system and its effect on the soil shear stress are also investigated based on the obtained optimal results. [ABSTRACT FROM AUTHOR]

The ability to accurately estimate energy consumption in the medium and long term based on actual indications is critical for countries to plan and prioritize their futures and take the appropriate actions. This paper proposes a new modified artificial bee colony (M-ABC) method that can adaptively select an optimal search equation to more accurately estimate Turkey's energy consumption. In the study, linear (M-ABCL) and quadratic (M-ABCQ) mathematical models were developed, and gross domestic product (GDP), population, import, and export data were used as input parameters for energy demand estimation. The weight values in the regression models are calculated according to the objective function with the proposed M-ABC. In this way, the weight values that will produce estimations with the lowest error according to the selected years are found, and then the most appropriate energy demand estimations are made. We compared the performance of our proposed M-ABC algorithm with ant colony optimization (ACO), particle swarm optimization (PSO), and hybrid ACO and PSO (HAP) algorithms. In addition, various estimation suggestions are presented under four different scenarios using input parameters. According to the results, the models suggested with the M-ABC algorithm were more successful in estimating the energy demand. According to the results of the presented four scenarios, the energy demand in 2025 is 145.26, 139.85, 126.26, and 144.17 million tons of oil equivalent (Mtoe) for the M-ABCL model, and 185.62, 161.94, 118.96, and 159.71 Mtoe for the M-ABCQ model, respectively. Thus, it is predicted that average consumption will increase by 51.65% in the linear model and 70.94% in the quadratic model. [ABSTRACT FROM AUTHOR]

Cancer is considered a leading cause of mortality in both developed and developing countries. Cancer classification based on the microarray dataset has provided insight into possible treatment strategies. A complicated and high-dimensional number of genes and a few numbers of instances are characteristics of the microarray datasets. Gene selection is therefore a challenging and required task for the data analysis of microarray expression. The selection of genes may reveal insight into the underlying mechanism of a particular biological phenomenon. Several academics have recently developed methods of feature selection, utilizing metaheuristic algorithms for interpreting and analyzing microarray data. Nevertheless, due to the few numbers of samples in microarray data compared to the high dimensionality, several data mining approaches have been unsuccessful to select the most relevant and informatics genes. As a result, incorporating various classifiers can enhance feature selection and classification performance. The current study aims to propose a method for cancer classification by employing ensemble learning. Hence, in this paper, particle swarm optimization and an ensemble learning method collaborate for feature selection and cancer classification. As a result, the analysis indicates the effectiveness of the proposed method for cancer classification based on microarray datasets, and in terms of accuracy, the performance outcomes are 100%, 92.86%, 86.36%, 100%, 85.71% for leukemia, colon, breast cancer, ovarian, and central nervous system, respectively, which overcome most of the state-of-the-art methods and also dominance on the baseline ensemble method with 12% enhancement. [ABSTRACT FROM AUTHOR]

Due to the nonlinear characteristic of the power-voltage (P–V) and current–voltage (I–V) relationship of the photovoltaic systems, building accurate mathematical models of photovoltaic cell and module is essential for validation and optimization performance of photovoltaic systems. However, determination of the unknown parameters of photovoltaic cell and module models is a complex nonlinear optimization problem that requires effective solving methods. This paper proposes an approach of parameter estimation of photovoltaic cell and module models based on artificial ecosystem-based optimization (AEO). The AEO is a new developed metaheuristic algorithm taken idea of the mechanisms of ecosystem consisting of production, consumption and decomposition. The advanced feature of AEO is the diversity of exploration and exploitation mechanisms without special control parameters. The performance of AEO is discussed in two photovoltaic cell models and two photovoltaic module models. Its performances compared to five algorithms consisting of backtracking search algorithm (BSA), cuckoo search algorithm (CSA), equilibrium optimizer (EO), genetic algorithm (GA) and particle swarm optimization (PSO) as well as the previous methods. The numerical results show that AEO has given the better performance than BSA, CSA, EO, GA and PSO for the problem of parameter estimation of photovoltaic models in terms of aspects such as reaching the lower error between the experimental and estimated values and having the better statistical results in several runs than the above methods. Thus, AEO can be an effective method for estimating the unknown parameters of photovoltaic cell and module models. [ABSTRACT FROM AUTHOR]

This paper presents the applications of novel metaheuristic algorithms including equilibrium optimization (EO), coot optimization, and slime mould optimization together with three Particle swarm optimization variants for optimal power generation cooperation of power systems with conventional hydroelectric power plants, thermal power plants and especially pumped-storage hydroelectric plants (PHPs). Three different power systems are applied to run these algorithms to prove that PHPs have a significant contribution to the generation cost reduction. In the first two systems, there are not inflows to the upper reservoirs of the PHPs and these plants only produce electricity by using pumped water. In the third system, inflows to PHPs are considered. The requirement is that the volume of reservoir at the beginning and the end of a day must be the same. However, the generation cost for the cases with the operation of PHPs is much less than other cases without the PHPs or without the pump mode of PHP. About the generation cost of three systems, PHPs support to reach the saving cost of $1,222.1998, $89,091.16, and $94,449.2 corresponding to 0.45%, 3% and 3.4%, respectively. So, the PHPs should be run in power systems. Among the six applied methods, EO is the best since it can reach less cost than others from higher than 2% to under 8%. Hence, it is recommended that EO is a potential method for finding optimal operation parameters of power systems with the PHPs. [ABSTRACT FROM AUTHOR]

Power system stabilizers (PSSs) are extensively used in generator units to enhance the transient stability of the power system. Hence, optimal tuning and placement of the parameters of PSS are crucial for the efficiency of the stabilizer. Researchers have been proposed many methods for optimizing such parameters. In this paper, the Crow search algorithm (CSA), which is based on the intelligence of crows, was employed in a single-machine infinite-bus (SMIB) system to determine the optimum parameters of the PSS. Modeling and simulation of the SMIB and designing of PSS were made by MATLAB/Simulink. PSSs are designed to minimize low-frequency oscillations such as power angle, rotor speed, and field current deviation following a large disturbance. Our objective in this study is the minimization of the rotor speed deviation. The results of the simulations proved the effectiveness and robustness of the optimization process compared to other metaheuristic algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA). When compared to the performance attained by the GA-based and PSO-based PSS controller designs, the simulations show that the CSA-based PSS delivers a far better dynamic response when the system is disrupted. The CSA-based PSS settles 48.1% faster than the PSO-based PSS and 55.7% faster than the GA-based PSS. CSA has just 2 parameters to adjust, making it much easier to implement than other methods. These parameters for PSO and GA are 4 and 6. When CSA-based PSS is used in the SMIB system, overshoot and low-frequency oscillations are also significantly reduced compared to other methods. [ABSTRACT FROM AUTHOR]

Accurate classification of high-dimensional biomedical data highly depends on the efficient recognition of the data's main features which can be used to assist diagnose related diseases. However, due to the existence of a large number of irrelevant or redundant features in biomedical data, classification approaches struggle to correctly identify patterns in data without a feature selection algorithm. Feature selection approaches seek to eliminate irrelevant and redundant features to maintain or enhance classification accuracy. In this paper, a new wrapper feature selection method is proposed based on the chimp optimization algorithm (ChOA) for biomedical data classification. The ChOA is a newly proposed metaheuristic algorithm whose capability for solving feature selection problems has not been investigated yet. Two binary variants of the ChoA are introduced for the feature selection problem. In the first approach, two transfer functions (S-shaped and V-shaped) are used to convert the continuous version of ChoA to binary. In addition to the transfer function, the crossover operator is utilized in the second approach to improve the ChOA's exploratory behavior. To validate the efficiency of the proposed approaches, five publicly available high-dimensional biomedical datasets, and a few datasets from different domains such as life, text, and image are employed. The proposed approaches were then compared with six well-known wrapper-based feature selection methods, including multi-objective genetic algorithm (GA), particle swarm optimization (PSO), Bat algorithm (BA), ant colony optimization (ACO), firefly algorithm (FA), and flower pollination (FP) algorithm, as well as two standard filter-based feature selection methods using three different classifiers. The experimental results demonstrate that the proposed approaches can effectively remove the least significant features and improve classification accuracy. The suggested wrapper feature selection techniques also outperform the GA, PSO, BA, ACO, FA, FP, and other existing methods in the terms of the number of selected genes, and classification accuracy in most cases. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance and suitability of four different metaheuristic algorithms for optimal sizing of standalone microgrids in remote area. The studied metaheuristic algorithms are particle swarm optimization, differential evolution, water cycle algorithm and grey wolf optimization. These algorithms are applied to optimize the capacity of diesel generator, fuel tank, solar photovoltaic, wind turbine, and battery energy storage in four different AC-coupled standalone microgrids for a remote area community in South Australia. The objective function is selected as the net present value of electricity over a 20-year lifetime. The optimisation study is conducted based on the real data of annual load consumption, ambient temperature, solar insolation, and wind speed of the site. Capital, replacement, and maintenance costs of components in Australian market are incorporated for the economic analysis. An operating power reserve is maintained based on the static and dynamic reserve concepts. Uncertainty analysis based on 10-year real data of renewable energies and load consumption is conducted. Sensitivity analysis is provided for variations of the battery price and capacity. The performance of the applied algorithms is evaluated by comparing the economic and operational results, as well as the computational time and optimization convergence. It is found that differential evolution algorithm is unreliable for optimal sizing problem of the studied standalone microgrids.. [ABSTRACT FROM AUTHOR]

The paper demonstrates a comprehensive performance assessment of the two metaheuristic swarm-based optimization algorithms namely PSO (Particle swarm optimization), BFOA (Bacterial foraging optimization algorithm), and the hybrid PSO-BFOA optimizer for the alleviation and control of the power oscillations in a two-area four generator system integrated with a large-scale PV-farm. After sunset, the PV-plant operates as VSC (Voltage Source Converter)-STATCOM (Static synchronous compensator) using its overall inverting capabilities for the power system stability improvement. While in the daytime during the faults, the PV-farm immediately stops the active power production and behaves as PV-STATCOM until the normal operating conditions are resumed. The modified version of Kundur's two-area system comprising of a large-scale PV-farm is simulated with MATLAB software. An innovative control strategy employing the two PI controllers distinctly controls the DC-AC currents of the PV-STATCOM. The series compensation is set to an optimal value of 85% and subjected to a 3-φ fault. Zero mechanical dampings, along with extra disturbances of 20% variation in reference voltage and electromagnetic torque are introduced to flaunt the worst damping scenarios. The simulation outcomes and time-domain analysis for various test conditions: without a controller, with PSO-based PV-STATCOM, with BFOA-based PV-STATCOM, and with the Hybrid PSO-BFOA-based PV-STATCOM, reveal that all the system modes are stabilized with PSO application. The stability of modes is progressively improved with BFO control, eventually, the modes are optimally stabilized by deploying the hybrid PSO-BFO algorithm. [ABSTRACT FROM AUTHOR]

Feature selection (FS) represents an important task in classification. Hadith represents an example in which we can apply FS on it. Hadiths are the second major source of Islam after the Quran. Thousands of Hadiths are available in Islam, and these Hadiths are grouped into a number of classes. In the literature, there are many studies conducted for Hadiths classification. Sine Cosine Algorithm (SCA) is a new metaheuristic optimization algorithm. SCA algorithm is mainly based on exploring the search space using sine and cosine mathematical formulas to find the optimal solution. However, SCA, like other Optimization Algorithm (OA), suffers from the problem of local optima and solution diversity. In this paper, to overcome SCA problems and use it for the FS problem, two major improvements were introduced to the standard SCA algorithm. The first improvement includes the use of singer chaotic map within SCA to improve solutions diversity. The second improvement includes the use of the Simulated Annealing (SA) algorithm as a local search operator within SCA to improve its exploitation. In addition, the Gini Index (GI) is used to filter the resulted selected features to reduce the number of features to be explored by SCA. Furthermore, three new Hadith datasets were created. To evaluate the proposed Improved SCA (ISCA), the new three Hadiths datasets were used in our experiments. Furthermore, to confirm the generality of ISCA, we also applied it on 14 benchmark datasets from the UCI repository. The ISCA results were compared with the original SCA and the state-of-the-art algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization Algorithm (GOA), and the most recent optimization algorithm, Harris Hawks Optimizer (HHO). The obtained results confirm the clear outperformance of ISCA in comparison with other optimization algorithms and Hadith classification baseline works. From the obtained results, it is inferred that ISCA can simultaneously improve the classification accuracy while it selects the most informative features. [ABSTRACT FROM AUTHOR]

This paper presents a powerful swarm intelligence metaheuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization Algorithm. The original Cat Swarm Optimization suffers from the shortcoming of "premature convergence," which is the possibility of entrapment in local optima which usually happens due to the off balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real-world scenario are used. In addition, the dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion and future directions to further improve the algorithm are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we present two metaheuristic evolutionary algorithms-based approaches to position the customer order decoupling point (CODP) in smart mass customization (SMC). SMC tries to autonomously mass customize and produce products per customer needs in Industry 4.0. SMC shown here is from the perspective of arriving at a CODP during manufacturing process flow designs meant for fast moving and complex product variants. Learning generally needs several repetitive cycles to break the complexity barrier. We make use of fruit fly and particle swarm optimization (PSO) evolutionary algorithms with the help of MATLAB programming to constantly search better fitting consecutive process modules in manufacturing chain. CODP is optimized by increasing modularity and reducing complexity through evolutionary concept. Learning-based PSO iterations are performed. The methods shown here are recommended for process flow design in a learning-oriented supply chain organization which can involve in-house and outsourced manufacturing steps. Finally, a complexity reduction model is presented which can aid in deploying this concept in design of supply chain and manufacturing flows. [ABSTRACT FROM AUTHOR]

Cloud computing provides infinite resources and various services for the execution of variety of applications to end users, but still it has various challenges that need to be addressed. Objective of cloud users is to select the optimal resource that meets the demand of end users in reasonable cost and time, but sometimes users pay more for short time. Most of the proposed state-of-the-art algorithms try to optimize only one parameter at a time. Therefore, a novel compromise solution is needed to make the balance between conflicting objectives. The main goal of this research paper is to design and develop a task processing framework that has the decision-making capability to select the optimal resource at runtime to process the applications (diverse and complex nature) at virtual machines using modified particle swarm optimization (PSO) algorithm within a user-defined deadline. Proposed algorithm gives non-dominance set of optimal solutions and improves various influential parameters (time, cost, throughput, task acceptance ratio) by series of experiments over various synthetic datasets using Cloudsim tool. Computational results show that proposed algorithm well and substantially outperforms the baseline heuristic and meta-heuristic such as PSO, adaptive PSO, artificial bee colony, BAT algorithm, and improved min–min load-balancing algorithm. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) are the backbone in various modern Internet of Things (IoT) smart applications ranging from automated control, surveillance, forest fire detection, etc. One of the most important applications is the smart agriculture. The deployment of WSN in agricultural processes can predict crop yield, soil temperature, air quality, water level, crop price, and the appropriate time for market delivery which will help to increase productivity. In this paper, an enhanced metaheuristic algorithm called multi-verse optimizer with overlapping detection phase (DMVO) is introduced for optimizing the area coverage percentage of WSN. The proposed algorithm is tested on many datasets with different criterions and is compared with other algorithms including the original MVO, particle swarm optimization, and flower pollination algorithm. The experimental results are analyzed with one-way ANOVA test. In addition, DMVO is applied to IoT smart agriculture in East Oweinat area in Egypt and compared with Krill Herd algorithm. In addition, the experimental results are analyzed with Wilcoxon signed-rank test. The experimental results and the statistical analysis prove the prosperity and consistency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a comprehensive survey of SOS advances and its applications, and this will be of benefit to the researchers engaged in the study of SOS algorithm. [ABSTRACT FROM AUTHOR]

During each year, natural disasters like floods, hurricanes, tornadoes, earthquakes, and mass movements cause enormous damages to the people and infrastructure. Designing an effective decision support model to allocate and schedule of the rescue units can reduce economic losses and casualties in the natural disasters. By assuming the incidents as jobs and rescue units as machines, we can formulate the research problem as an unrelated parallel machine scheduling problem. In this paper, a mixed integer linear programming model is proposed to minimize the sum of the weighted completion times and delays at the start of relief operations. After relieving several incidents, rescuers will become tired and then need more time to relieve the remaining incidents which were assigned to them; therefore, we consider this phenomenon as fatigue effect in this research. The rescue units also have different capabilities, and each incident just can be allocated to a rescue unit that is able to do it. Due to NP-hardness of the research problem, three metaheuristic algorithms, namely simulated annealing (SA) algorithm, particle swarm optimization (PSO) algorithm, and a method based on hybrid SA and PSO (SA-PSO), are developed to solve the research problem. Finally, the developed metaheuristic algorithms are ranked by applying the technique for order of preference by similarity to ideal solution. The experimental results illustrate that the SA algorithm and the hybrid SA-PSO are better than others in terms of CPU time and solution quality, respectively. [ABSTRACT FROM AUTHOR]

The tuning of proportional–integral–derivative (PID) controller is essential for any control application in order to ensure the best performance by step change or disturbance. This paper presents the tuning of PID controller for single-input single-output (SISO) and multiple-input multiple-output (MIMO) control systems using tree physiology optimization (TPO). TPO is a metaheuristic algorithm inspired from a plant growth system derived based on the idea of plant architecture and Thornley model (TM). The basic principle of TM simplifies the plant growth into shoots and roots part. The plant shoots grow towards sunlight with the help of nutrients supplied by the root system in order to undergo photosynthesis process, a process of converting light photon into carbon. The carbon gain from the shoots extension will be supplied to the root system in order for the root to grow and search for water plus nutrients. As a result, the nutrients are supplied upwards towards shoot system for further extension. This concept runs iteratively in order to ensure optimum plant growth. The iterative search of shoot towards better light supported by the root counterparts leads to an optimization idea of TPO algorithm. TPO also has a unique exploration strategy due to its multiple branches and shoots that can be defined by user. This concept may improve the search mechanism with a better trade-off between diversification and intensification search. A simulation of SISO control system and an industrial application of MIMO control are applied to demonstrate the effectiveness of the proposed algorithm and compared with other optimization methods such as particle swarm optimization, Ziegler–Nichols, Tyreus–Luyben and Chien–Hrones–Reswick methods. The results clearly exhibit the capability of TPO algorithm towards finding the optimum PID parameters for SISO and MIMO process with faster settling time and better performance with respect to other methods. [ABSTRACT FROM AUTHOR]

In this research, a quantum computing idea based bat algorithm (QBA) is proposed to solve many-objective combined economic emission dispatch (CEED) problem. Here, CEED is represented using cubic criterion function to reduce the nonlinearities of the system. Along with economic load dispatch, emissions of SO2, NOx, and CO2 are considered as separate three objectives, thus making it a four-objective (many-objective) optimization problem. A unit-wise price penalty factor is considered here to convert all the objectives into a single objective in order to compare the final results with other previously used methods like Lagrangian relaxation (LR), particle swarm optimization, and simulated annealing. QBA is applied in six-unit power generation system for four different loads. The obtained results show QBA successfully solve many-objective CEED problem with greater superiority than other methods found in the literature in terms of quality results, robustness, and computational performance. In the end of this paper, a detailed future research direction is provided based on the simulation results and its analysis. The outcome of this research demonstrates that the inclusion of quantum computing idea in metaheuristic technique provides a useful and reliable tool for solving such many-objective optimization problem. [ABSTRACT FROM AUTHOR]

A novel optimization algorithm called hybrid sine–cosine algorithm with teaching–learning-based optimization algorithm (SCA–TLBO) is proposed in this paper, for solving optimization problems and visual tracking. The proposed hybrid algorithm has better capability to escape from local optima with faster convergence than the standard SCA and TLBO. The effectiveness of this algorithm is evaluated using 23 benchmark functions. Statistical parameters are employed to observe the efficiency of the hybrid SCA–TLBO qualitatively, and results prove that the proposed algorithm is very competitive compared to the state-of-the-art metaheuristic algorithms. The hybrid SCA–TLBO algorithm is applied for visual tracking as a real thought-provoking case study. The hybrid SCA–TLBO-based tracking framework is used to experimentally measure object tracking error, absolute error, tracking detection rate, root mean square error and time cost as parameters. To reveal the capability of the proposed algorithm, a comparison of hybrid SCA–TLBO-based tracking framework and other trackers, viz. alpha–beta filter, linear Kalman filter and extended Kalman filter, particle filter, scale-invariant feature transform, particle swarm optimization and bat algorithm, is presented. [ABSTRACT FROM AUTHOR]

Just-In-Time (JIT) is a popular philosophy in many industrial practices. The concept of JIT in early studies concerned with improving operational efficiency and waste minimization. In recent decades, however, JIT principles have also connected to logistics efficiency particularly for distribution of raw materials and finished goods. In the literature, several attempts have been made to optimize JIT logistics networks. On the one hand, most studies have typically focused on deterministic and small-scale problems which have been solved by exact algorithms. On the other hand, when large-scale problems were considered and usually were solved by metaheuristics algorithms, uncertainty sources and fine-tuning of the metaheuristics parameters were generally ignored. In this paper, we develop a mixed-integer linear optimization model to investigate a large-scale JIT logistics problem with 15 different sizes. To deal with different uncertainty sources, the customers demand and suppliers’ capacity as the two main sources of uncertainty in practice are considered as triangular fuzzy parameters. The proposed model aims to minimize total logistics cost including costs of transportation, inventory holding and backorders. A particle swarm optimization algorithm is applied to solve the problem, and its results are then validated by a harmony search algorithm. Both algorithms parameters are tuned using response surface methodology and Taguchi method. Finally, the conclusion and some directions for future research are proposed. [ABSTRACT FROM AUTHOR]

One important aspect that should be achieved during the operation of the distribution network is minimizing the total active loss. This objective can be achieved by network reconfiguration in which switching events such as closing tie switches or opening sectionalizing switches are efficiently determined. This paper presents a reliable meta-heuristic algorithm for optimal reconfiguration of the distribution network which is binary particle swarm optimization gravity search algorithm (BPSOGSA). The methodology is applied on four test systems: 16-bus system, 33-bus system, 69-bus system and 119-bus system. Reliability indices, system average interruption frequency, system average interruption duration and energy not supplied, are incorporated to check the validity of the network after reconfiguration process. Comparison with other reported previous methods is performed; the power loss is reduced by 9.3242% in 16-bus system, for 33-bus system; the power loss is reduced by 31.46%. In case of 69-bus system, the power loss is reduced by 56.1761%, while for 119-bus, the power loss is reduced by 33.7216%. Additionally, the performance of the proposed BPSOGSA is the best one compared with the others for all studied cases. The obtained results prove the reliability of the proposed methodology. [ABSTRACT FROM AUTHOR]

Parameter estimation is a fundamental research issue which has attracted great concern in the control and synchronization of chaotic systems. This problem can be mathematically described as a multi-dimensional continuous optimization problem through constructing an appropriate fitness function, and then solved via meta-heuristic algorithms. A hybrid flower pollination algorithm is proposed in this paper for solving this problem more efficiently. The proposed algorithm well combines the good global exploration ability of the original flower pollination algorithm and the powerful local exploitation ability of the Nelder-Mead simplex method together by integrating these two methods in a very simple way. The experimental results tested on three typical chaotic system parameter estimation problems with three unknown system parameters, including the Lorenz system, the Rossler system, and the Lorenz system under the noise condition, demonstrate that the algorithm can estimate the unknown parameters efficiently and accurately. The comparisons with the basic flower pollination algorithm and some other four reported methods including the quantum particle swarm optimization, the hybrid adaptive cuckoo search optimization algorithm, the oppositional backtracking search optimization algorithm, and the hybrid algorithm combining differential evolution with artificial bee colony suggest that the proposed algorithm is energy efficient and superior. The proposed algorithm can be used as a new and effective choice for parameter estimation of chaotic systems. [ABSTRACT FROM AUTHOR]

Most of today’s mobile robots operate in controlled environments prone to various unpredictable conditions. Programming or reprogramming of such systems is time-consuming and requires significant efforts by number of experts. One of the solutions to this problem is to enable the robot to learn from human teacher through demonstrations or observations. This paper presents novel approach that integrates Learning from Demonstrations methodology and chaotic bioinspired optimization algorithms for reproduction of desired motion trajectories. Demonstrations of the different trajectories to reproduce are gathered by human teacher while teleoperating the mobile robot in working environment. The learning (optimization) goal is to produce such sequence of mobile robot actuator commands that generate minimal error in the final robot pose. Four different chaotic methods are implemented, namely chaotic Bat Algorithm, chaotic Firefly Algorithm, chaotic Accelerated Particle Swarm Optimization and newly developed chaotic Grey Wolf Optimizer (CGWO). In order to determine the best map for CGWO, this algorithm is tested on ten benchmark problems using ten well-known chaotic maps. Simulations compare aforementioned algorithms in reproduction of two complex motion trajectories with different length and shape. Moreover, these tests include variation of population in swarm and demonstration examples. Real-world experiment on a nonholonomic mobile robot in indoor environment proves the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Finding the appropriate values of parameters and structure of type-2 fuzzy logic systems is a difficult and complex task. Many types of meta-heuristic algorithms have been used to find the complex structure and appropriate parameter values of the type-2 fuzzy systems and more recently hybrid meta-heuristic algorithms. In this paper, we review recent advances (2012 to date) on the application of meta-heuristic algorithms and hybrid meta-heuristic algorithms, for the optimization of type-2 fuzzy logic systems in intelligent control. It was found that the major meta-heuristic algorithms used for optimizing the design of type-2 fuzzy logic systems in intelligent control were genetic algorithms and particle swarm optimization as well as hybrid meta-heuristic algorithms. Researchers can use this review as a starting point for further advancement as well as an exploration of other meta-heuristic algorithms that have received little or no attention from researchers. [ABSTRACT FROM AUTHOR]

Grey Wolf Optimizer (GWO) is a recently developed meta-heuristic search algorithm inspired by grey wolves (Canis lupus), which simulate the social stratum and hunting mechanism of grey wolves in nature and based on three main steps of hunting: searching for prey, encircling prey and attacking prey. This paper presents the application of GWO algorithm for the solution of non-convex and dynamic economic load dispatch problem (ELDP) of electric power system. The performance of GWO is tested for ELDP of small-, medium- and large-scale power systems, and the results are verified by a comparative study with lambda iteration method, Particle Swarm Optimization algorithm, Genetic Algorithm, Biogeography-Based Optimization, Differential Evolution algorithm, pattern search algorithm, NN-EPSO, FEP, CEP, IFEP and MFEP. Comparative results show that the GWO algorithm is able to provide very competitive results compared to other well-known conventional, heuristics and meta-heuristics search algorithms. [ABSTRACT FROM AUTHOR]

A novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The paper also considers the proposal of binary and multi-objective versions of DA called binary DA (BDA) and multi-objective DA (MODA), respectively. The proposed algorithms are benchmarked by several mathematical test functions and one real case study qualitatively and quantitatively. The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature. The results of MODA also show that this algorithm tends to find very accurate approximations of Pareto optimal solutions with high uniform distribution for multi-objective problems. The set of designs obtained for the submarine propeller design problem demonstrate the merits of MODA in solving challenging real problems with unknown true Pareto optimal front as well. Note that the source codes of the DA, BDA, and MODA algorithms are publicly available at . [ABSTRACT FROM AUTHOR]

This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at . [ABSTRACT FROM AUTHOR]