Abstract The vehicle routing problem with time windows (VRPTW) has gained much attention recently due to its wide application in operations research and logistics. VRPTW has been proven to be an NP-hard problem whose optimal solution is computationally costly. Scholars have proposed many methods, such as exact algorithms, heuristics, and metaheuristics, to find near-optimal solutions for the VRPTW. Exact algorithms are limited to small-scale problems, while heuristic algorithms and metaheuristics often converge to locally optimal solutions, despite their applicability to larger-scale problems. This paper proposes a novel membrane-inspired evolutionary algorithm framework (MEAF) consisting of isolated evolutionary rules, communication output rules, communication input rules, fusion-exchange information operation, and membrane dissolution rules. By leveraging the advantages of multiple metaheuristics algorithms and avoiding the pitfalls of local optima, MEAF offers a promising solution to address complex problems. The effectiveness of the proposed MEAF is verified by applying three classical metaheuristics, namely Genetic Algorithm (GA), Ant Colony System (ACS), and Particle Swarm Algorithm (PSO), to solve the VRPTW problem. The experiments are run on 56 instances of Solomon with 100 client benchmarks. The evaluation of the experimental results combined with the mean and standard deviation values show that the algorithm performs better in 54 out of 56 instances, demonstrating the effectiveness and stability of the proposed algorithm.

Detecting fake accounts on Online Social Networks is a pressing issue due to the rise in unethical online activities. This study presents a new Quantum Beta-behaved Multi-Objective Particle Swarm Optimization Algorithm (QB-MOPSO) for machine learning-based fake account detection. QB-MOPSO aims to enhance the learning process of a random forest algorithm by simultaneously minimizing feature dimensionality and classification error rates. It proposes a novel architecture that employs two optimization profiles: one improves exploratory behavior using a quantum-behaved equation, while the other enhances exploitation through a beta function. The main contributions of this study are as follows: the design of a novel Quantum Beta Distributed Multi-Objective Particle Swarm Optimization algorithm that integrates quantum-behaved exploration and beta-distributed exploitation, the application of this algorithm to enhance artificial intelligence–based fake account detection on Twitter datasets, and a comprehensive experimental evaluation demonstrating superior accuracy, F-measure, and MCC compared to existing methods. Experimental results on two Twitter datasets with 1982 and 928 accounts respectively show QB-MOPSO's effectiveness, achieving accuracy rates of about 99.19 % and 97.52 %. Comparisons with the original architecture demonstrate QB-MOPSO's ability to enhance the performance of the random forest algorithm.

Metaheuristic algorithms solve optimization problems mostly by imitating behaviors observed in nature. Over time, these algorithms have proven to be very effective in solving complex optimization problems. Due to the rising complexity and scale of practical engineering problems, numerous metaheuristic algorithms have been developed recently and applied in various fields. In response to this need, researchers continue to explore novel approaches inspired by natural and social phenomena. Inspired by the competition among ancient tribes and their cooperative behavior, this paper proposes a meta-heuristic called the Competition of Tribes and Cooperation of Members Algorithm (CTCM). Experiments are conducted on 23 benchmark test functions and comprehensively compared with other state-of-the-art algorithms, including particle swarm optimization (PSO), grey wolf optimizer (GWO), sparrow search algorithm (SSA), egret swarm optimization (ESOA), beetle antennae search (BAS) and whale optimization (WOA). The standard deviation and average, as well as statistical tests are utilized to compare the performance of each algorithm, which demonstrates that CTCM is superior in the majority of problems. In addition, the results of Wilcoxon and Friedman rank tests show that the CTCM achieves the first place in all categories of problems. The results indicate that CTCM possesses strong global optimization search capability and stability, and has faster convergence speed. The paper also considers solving practical engineering optimization problems as proof-of-concept case studies , in which CTCM achieves all the optimal solutions for each engineering problem.

Adaptive HCI for education refers to design and development computer interfaces that can dynamically adjust and tailor themselves to individual users’ needs, preferences, and abilities. The goal of...

This paper presents the enzyme action optimization (EAO) algorithm, a novel bio-inspired optimization algorithm designed to simulate the adaptive enzyme mechanism in biological systems. EAO employs a novel strategy that dynamically balances between exploration and exploitation to efficiently navigate and optimize complex, multi-dimensional search spaces. EAO has been tested over diverse benchmark datasets, including the 23 classical benchmark functions, IEEE CEC2017, CEC2022 benchmark functions, where it has been compared with 14 recent and highly cited optimizers. The results show the superior performance of EAO over the compared optimizers in terms of finding the optimal solution, convergence speed, robustness, and overall performance. Furthermore, EAO was applied to solve five engineering design problems and demonstrated excellent performance results. The source code of EAO is publicly available for both MATLAB at: https://www.mathworks.com/matlabcentral/fileexchange/170296-enzyme-action-optimizer-a-novel-bio-inspired-optimization and PYTHON at: https://github.com/AliRodan/Enzyme-Action-Optimizer .

Deep learning methods, specifically convolutional neural networks (CNNs), have achieved state-of-the-art in various tasks including image classification. However, the computational and memory requirements of advanced CNNs like ResNet-50 pose deployment challenges, particularly in resource-constrained environments. Our study introduces a new lightweight approach, namely ResNet-Lite, for image classification. It combines knowledge distillation and network tuning along with hyperparameter tuning techniques to overcome deployment barriers. ResNet-Lite involves the extracting of knowledge from a pre-trained ResNet-50 network and transferring it to a smaller network, creating a more compact and effective approach. After that, hyperparameter and network tuning have been applied to determine the optimal combination of parameters that maximizes the generalization and performance of the model. Our experimental results demonstrate that ResNet-Lite achieves a significantly reduced model size while maintaining competitive classification performance. Specifically, it outperforms the original ResNet-50 model by 5.40% and by 7.13% in accuracy on the CIFAR-10 and Fashion-MNIST datasets, respectively. In summary, our study provides a practical solution for developing high-performance image classification models, even in resource-constrained environments, contributing to the field of advanced deep learning.

Fetal examinations are a significant and challenging field of healthcare. Cardiotocography is the most commonly used method for monitoring fetal heart rate and uterine contractions. As a promising alternative to cardiotocography, fetal phonocardiography is beginning to emerge. It is an entirely non-invasive, passive, and low-cost method. However, it is tough to estimate the ideal form of the fetal sound signal in most cases due to the presence of disturbances. The disturbances originate from movements or rotations of the fetal body, making fetal heart sound processing difficult. This study presents an automatic method for segmenting the fetal heart sounds in a phonocardiographic signal that is loaded with different types of disturbances and analyzes which of these disturbances most affect segmentation accuracy. To provide a comprehensive investigation, we propose a hybrid classifier based on Transformer and eXtreme Gradient Boosting, short for XGBoost, to improve segmentation performance by decision-making integration. 2000 segments of data from the Research Resource for Complex Physiologic Signals, PhysioNet repository, and created synthetic data (873 recordings) were used for the experiment. In the S1 label, our proposed method ranks first among all compared algorithms in precision, recall, F1, and accuracy score, tying with Transformer in recall score. It achieves an accuracy increase of 5% and 1.3% compared to XGBoost and Transformer, respectively. Similarly, in the S2 label, there is a precision score increase of 5.8% and 3.7% compared to XGBoost and Transformer, respectively. In general, our proposed method shows effective and promising performance..

This paper proposes a new multi-algorithm based evolution strategy with the addition of adaptive mutation operators for global optimization. The new algorithm namely Kepler meerkat naked (KMN) algorithm is based on Kepler’s optimization algorithm (KOA), meerkat optimization algorithm (MOA), and naked mole-rat algorithm (NMRA), as the core algorithms and, grey wolf optimizer (GWO) and cuckoo search (CS) inspired equations for enhanced exploration and exploitation. The proposed algorithm uses six new mutation operators for parametric enhancements, and follows an iterative division mechanism for a balanced operation. A comparative analysis is done with respect to classical benchmarks, CEC 2014, CEC 2017, CEC 2019 and CEC 2022 benchmark datasets for performance evaluation. Six engineering design problems are also used to test the performance of the proposed KMN algorithm for constraint optimization. Apart from that, a binary version of KMN namely bKMN is also proposed, and ten feature selection datasets are used for performance evaluation. Performance testing of the KMN and bKMN algorithm is done with success history-based DE (SHADE), LSHADE-SPACMA, self-adaptive DE (SaDE), fast opposition-based learning golden jackal optimization (FROBL-GJO), LSHADE-EpSin, jSO, EBOwithCMAR, among others. Experimental and statistical results are performed using Wilcoxon’s and Friedman’s tests, and it has been found that the proposed algorithms are highly competitive in contrast to other algorithms under study.

In a Mobile Edge Computing (MEC) environment, latency and energy consumption can be reduced by offloading tasks from mobile devices to Edge Servers (ESs) instead of remote cloud servers. The placement of ESs closest to end users can improve QoE and QoS. Additionally, the deployment of additional servers to cover each user will ensure that user requirements are met even if the designated edge server is unable to provide service. Therefore, the use of additional ESs can improve network robustness. However, edge service providers tend to cover all areas of a city with a minimum number of servers to save costs. Since the coverage zones of ESs can overlap, fewer additional ESs need to be deployed to support overlapping areas, resulting in cost savings. This paper examines the problem of ES placement and proposes a new model to simultaneously optimize network latency, coverage with overlap control, and OPerational EXpenditures (OPEX) of the MEC. In addition, a binary version of the hybrid NSGA II-MOPSO algorithm called BHNM is proposed to obtain the approximated Pareto front. Results based on the real-world dataset from Shanghai Telecom show that the BHNM algorithm outperforms the Binary MOPSO with Turbulence (BMOPSO-T) and NSGA-II algorithms in terms of Pareto front diversity.

In this paper, a novel metaheuristic called ‘The Hiking Optimization Algorithm’ (HOA) is proposed. HOA is inspired by hiking, a popular recreational activity, in recognition of the similarity between the search landscapes of optimization problems and the mountainous terrains traversed by hikers. HOA’s mathematical model is premised on Tobler’s Hiking Function (THF), which determines the walking velocity of hikers (i.e. agents) by considering the elevation of the terrain and the distance covered. THF is employed in determining hikers’ positions in the course of solving an optimization problem. HOA’s performance is demonstrated by benchmarking with 29 well-known test functions (including unimodal, multimodal, fixed-dimension multimodal, and composite functions), three engineering design problems (EDPs), (including I-beam, tension/compression spring, and gear train problems) and two N-P Hard problems (i.e. Traveling Salesman’s and Knapsack Problems). Moreover, HOA’s results are verified by comparison to 14 other metaheuristics, including Teaching Learning Based Optimization (TLBO), Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization, Grey Wolf Optimizer (GWO) as well as newly introduced algorithms such as Komodo Mlipir Algorithm (KMA), Quadratic Interpolation Optimization (QIO), and Coronavirus Optimization Algorithm (COVIDOA). In this study, we employ statistical tests such as the Wilcoxon rank sum, Friedman test, and Dunn’s post hoc test for the performance evaluation. HOA’s results are competitive and, in many instances, outperform the aforementioned well-known metaheuristics.

This study proposes a novel artificial protozoa optimizer (APO) that is inspired by protozoa in nature. The APO mimics the survival mechanisms of protozoa by simulating their foraging, dormancy, and reproductive behaviors. The APO was mathematically modeled and implemented to perform the optimization processes of metaheuristic algorithms. The performance of the APO was verified via experimental simulations and compared with 32 state-of-the-art algorithms. Wilcoxon signed-rank test was performed for pairwise comparisons of the proposed APO with the state-of-the-art algorithms, and Friedman test was used for multiple comparisons. First, the APO was tested using 12 functions of the 2022 IEEE Congress on Evolutionary Computation benchmark. Considering practicality, the proposed APO was used to solve five popular engineering design problems in a continuous space with constraints. Moreover, the APO was applied to solve a multilevel image segmentation task in a discrete space with constraints. The experiments confirmed that the APO could provide highly competitive results for optimization problems. The source codes of Artificial Protozoa Optimizer are publicly available at https://seyedalimirjalili.com/projects and https://ww2.mathworks.cn/matlabcentral/fileexchange/162656-artificial-protozoa-optimizer.

Abstract This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study.

Visible light communication (VLC) is a rapidly growing wireless communication technology for the Internet of Things (IoT) that offers high data rates and low latency, making it ideal for massive connectivity. Efficient resource allocation is essential in VLC networks to minimize inter-symbol and cochannel interferences, which can greatly improve network performance and user satisfaction. This paper focuses on an indoor IoT-based VLC system that utilizes photodetectors (PDs) on users’ cell phones as receivers, with the goal of maximizing system performances and reducing power consumption by selectively activating some PDs while deactivating others. However, this objective presents a challenge due to the inherent non-convex nature of the multi-objective optimization problem, which cannot be solved by analytical means. To address this, we propose an enhanced Aquila optimization (EAO) scheme that improves upon the Aquila Optimizer (AO) by incorporating a fitness distance balance (FDB) function. We evaluate our proposed EAO in various scenarios under different settings, considering both capacity and fairness metrics. Through simulations, we demonstrate the effectiveness of our approach and its superiority over classical algorithms such as Aquila Optimizer (AO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) in finding the optimal solution. Our results confirm that the proposed EAO algorithm can efficiently optimize the system capacity and ensure fairness among all users, providing a promising solution for indoor VLC systems.

Abstract Over recent decades, research in Artificial Intelligence (AI) has developed a broad range of approaches and methods that can be utilized or adapted to address complex optimization problems. As real-world problems get increasingly complicated, this requires an effective optimization method. Various meta-heuristic algorithms have been developed and applied in the optimization domain. This paper used and ameliorated a promising meta-heuristic approach named Crow Search Algorithm (CSA) to address numerical optimization problems. Although CSA can efficiently optimize many problems, it needs more searchability and early convergence. Its positioning updating process was improved by supporting two adaptive parameters: flight length (fl) and awareness probability (AP) to tackle these curbs. This is to manage the exploration and exploitation conducts of CSA in the search space. This process takes advantage of the randomization of crows in CSA and the adoption of well-known growth functions. These functions were recognized as exponential, power, and S-shaped functions to develop three different improved versions of CSA, referred to as Exponential CSA (ECSA), Power CSA (PCSA), and S-shaped CSA (SCSA). In each of these variants, two different functions were used to amend the values offlandAP. A new dominant parameter was added to the positioning updating process of these algorithms to enhance exploration and exploitation behaviors further. The reliability of the proposed algorithms was evaluated on 67 benchmark functions, and their performance was quantified using relevant assessment criteria. The functionality of these algorithms was illustrated by tackling four engineering design problems. A comparative study was made to explore the efficacy of the proposed algorithms over the standard one and other methods. Overall results showed that ECSA, PCSA, and SCSA have convincing merits with superior performance compared to the others.

In today’s era of data-driven digital society, there is a huge demand for optimized solutions that essentially reduce the cost of operation, thereby aiming to increase productivity. Processing a huge amount of data, like the Microarray based gene expression data, using machine learning and data mining algorithms has certain limitations in terms of memory and time requirements. This would be more concerning, when a dataset comes with redundant and non-important information. For example, many report-based medical datasets have several non-informative attributes which mislead the classification algorithms. To this end, researchers have been developing several feature selection algorithms that try to discard the redundant information from the raw datasets before feeding them to machine learning algorithms. Metaheuristic based optimization algorithms provide an excellent option to solve feature selection problems. In this paper, we propose a music-inspired harmony search (HS) algorithm based wrapper feature selection method. At the beginning, we use a chaotic mapping to initialize the population of the HS algorithm in order to better coverage of the search space. Further to complement the inferior exploitation of the HS algorithm, we integrate it with the Late Acceptance Hill Climbing (LAHC) method. Thus the combination of these two algorithms provides a good balance between the exploration and exploitation of the HS algorithm. We evaluate the proposed feature selection method on 15 UCI datasets and the obtained results are found to be better than many state-of-the-art methods both in terms of the classification accuracy and the number of features selected. To evaluate the effectiveness of our algorithm, we utilize a combination of precision, recall, F1 score, fitness value, and execution time as performance indicators. These metrics enable us to obtain a comprehensive assessment of the algorithm’s abilities and limitations. We also apply our method on 3 microarray based gene expression datasets used for prediction of cancer to ensure the scalability and robustness as a feature selection method in real-life scenarios. In addition to this, we test our approach using the COVID-19 dataset, and it performs better than several metaheuristic based optimization techniques.

The huge advancement in the field of communication has pushed the innovation pace toward a new concept in the context of Internet of Things (IoT) named IoT for Financial Technology applications (IoT-FinTech). The main intention is to leverage the businesses’ income and reducing cost by facilitating the benefits enabled by IoT-FinTech technology. To do so, some of the challenging problems that mainly related to routing protocols in such highly dynamic, unreliable (due to mobility), and widely distributed network need to be carefully addressed. This article, therefore, focuses on developing a new trustworthy and efficient routing mechanism to be used in routing data traffic over IoT-FinTech mobile networks. A new nonlinear Lévy Brownian generalized normal distribution optimization (NLBGNDO) algorithm is proposed to solve the problem of finding an optimal path from source to destination sensor nodes to be used in forwarding FinTech’s related data. We also propose an objective function to be used in maintaining the trustworthiness of the selected relay-node candidates by introducing a trust-based friendship mechanism to be measured and applied during each selection process. The formulated model also considering node’s residual energy, experienced response time, and internode distance (to figure out density/sparsity ratio of sensor nodes). Results demonstrate that our proposed mechanism could maintain very wise and efficient decisions over the selection period in comparison with other methods.

Image segmentation is a critical step in digital image processing applications. One of the most preferred methods for image segmentation is multilevel thresholding, in which a set of threshold values is determined to divide an image into different classes. However, the computational complexity increases when the required thresholds are high. Therefore, this paper introduces a modified Coronavirus Optimization algorithm for image segmentation. In the proposed algorithm, the chaotic map concept is added to the initialization step of the naive algorithm to increase the diversity of solutions. A hybrid of the two commonly used methods, Otsu's and Kapur's entropy, is applied to form a new fitness function to determine the optimum threshold values. The proposed algorithm is evaluated using two different datasets, including six benchmarks and six satellite images. Various evaluation metrics are used to measure the quality of the segmented images using the proposed algorithm, such as mean square error, peak signal-to-noise ratio, Structural Similarity Index, Feature Similarity Index, and Normalized Correlation Coefficient. Additionally, the best fitness values are calculated to demonstrate the proposed method's ability to find the optimum solution. The obtained results are compared to eleven powerful and recent metaheuristics and prove the superiority of the proposed algorithm in the image segmentation problem.

Abstract A novel multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA) is presented to solve global optimization problems with up to three objective functions. This algorithm used an archive to store non-dominated POSs during the optimization process. Then, a roulette wheel selection mechanism selects the effective archived solutions by simulating the frameshifting technique Coronavirus particles use for replication. We evaluated the efficiency by solving twenty-seven multi-objective (21 benchmarks & 6 real-world engineering design) problems, where the results are compared against five common multi-objective metaheuristics. The comparison uses six evaluation metrics, including IGD, GD, MS, SP, HV, and deltap( $$\Delta \mathrm{P}$$ ΔP ). The obtained results and the Wilcoxon rank-sum test show the superiority of this novel algorithm over the existing algorithms and reveal its applicability in solving multi-objective problems.

Abstract The Chaos Game Optimization (CGO) has only recently gained popularity, but its effective searching capabilities have a lot of potential for addressing single-objective optimization issues. Despite its advantages, this method can only tackle problems formulated with one objective. The multi-objective CGO proposed in this study is utilized to handle the problems with several objectives (MOCGO). In MOCGO, Pareto-optimal solutions are stored in a fixed-sized external archive. In addition, the leader selection functionality needed to carry out multi-objective optimization has been included in CGO. The technique is also applied to eight real-world engineering design challenges with multiple objectives. The MOCGO algorithm uses several mathematical models in chaos theory and fractals inherited from CGO. This algorithm's performance is evaluated using seventeen case studies, such as CEC-09, ZDT, and DTLZ. Six well-known multi-objective algorithms are compared with MOCGO using four different performance metrics. The results demonstrate that the suggested method is better than existing ones. These Pareto-optimal solutions show excellent convergence and coverage.

Breast cancer has become a common malignancy in women. However, early detection and identification of this disease can save many lives. As computer-aided detection helps radiologists in detecting abnormalities efficiently, researchers across the world are striving to develop reliable models to deal with. One of the common approaches to identifying breast cancer is through breast mammograms. However, the identification of malignant breasts from mass lesions is a challenging research problem. In the current work, we propose a method for the classification of breast mass using mammograms which consists of two main stages. At first, we extract deep features from the input mammograms using the well-known VGG16 model while incorporating an attention mechanism into this model. Next, we apply a meta-heuristic called Social Ski-Driver (SSD) algorithm embedded with Adaptive Beta Hill Climbing based local search to obtain an optimal features subset. The optimal features subset is fed to the K-nearest neighbors (KNN) classifier for the classification. The proposed model is demonstrated to be very useful for identifying and differentiating malignant and healthy breasts successfully. For experimentation, we evaluate our model on the digital database for screening mammography (DDSM) database and achieve 96.07% accuracy using only 25% of features extracted by the attention-aided VGG16 model. The Python code of our research work is publicly available at: https://github.com/Ppayel/BreastLocalSearchSSD.

Abstract Particle swarm optimization (PSO) is one of the most well-regard metaheuristics with remarkable performance when solving diverse optimization problems. However, PSO faces two main problems that degrade its performance: slow convergence and local optima entrapment. In addition, the performance of this algorithm substantially degrades on high-dimensional problems. In the classical PSO, particles can move in each iteration with either slower or faster speed. This work proposes a novel idea called velocity pausing where particles in the proposed velocity pausing PSO (VPPSO) variant are supported by a third movement option that allows them to move with the same velocity as they did in the previous iteration. As a result, VPPSO has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, VPPSO modifies the first term of the PSO velocity equation. In addition, the population of VPPSO is divided into two swarms to maintain diversity. The performance of VPPSO is validated on forty three benchmark functions and four real-world engineering problems. According to the Wilcoxon rank-sum and Friedman tests, VPPSO can significantly outperform seven prominent algorithms on most of the tested functions on both low- and high-dimensional cases. Due to its superior performance in solving complex high-dimensional problems, VPPSO can be applied to solve diverse real-world optimization problems. Moreover, the velocity pausing concept can be easily integrated with new or existing metaheuristic algorithms to enhance their performances. The Matlab code of VPPSO is available at: https://uk.mathworks.com/matlabcentral/fileexchange/119633-vppso .

The performance of optimization algorithms significantly depends on the landscape of the problems. It is known that there is no single algorithm that outperforms others on problems with different fitness landscapes. One of the issues in metaheuristic algorithms is keeping the balance between exploration and exploitation. The features extracted from analysis of fitness landscapes can be used to select the suitable algorithm for the given problem. However, these features are usually expensive and extracted prior to the optimization process which leads to a single algorithm to be selected. In this work, we propose an intelligent switch mechanism that enjoys an efficient non-convex ratio (ENCR) feature extracted online during the optimization to switch between two choices of algorithms, each favoring a type of landscape in terms of modality. For this work, two case studies including a pair of Harris hawks optimizer (HHO) and differential evolution (DE) and another pair of multiverse optimizer (MVO) and moth-flame optimizer (MFO) are selected among several algorithms to evaluate the performance of this framework. The proposed one-way and two-way switch algorithms take advantage of the merits of the two base algorithms to reach better final solutions and higher convergence rates in the majority of case studies. The overall comparison and ranking of the algorithms, including a random switch baseline, demonstrates the superiority of the intelligent switch mechanisms over the baselines.

This paper is an influential attempt to identify and alleviate some of the issues with the recently proposed optimization technique called the Marine Predator Algorithm (MPA). With a visual investigation of its exploratory and exploitative behavior, it is observed that the transition of search from being global to local can be further improved. As an extremely cost-effective method, a set of nonlinear functions is used to change the search patterns of the MPA algorithm. The proposed algorithm, called Nonlinear Marin Predator Algorithm (NMPA), is tested on a set of benchmark functions. A comprehensive comparative study shows the superiority of the proposed method compared to the original MPA and even other recent meta-heuristics. The paper also considers solving a real-world case study around power allocation in non-orthogonal multiple access (NOMA) and visible light communications (VLC) for Beyond 5G (B5G) networks to showcase the applicability of the NMPA algorithm. NMPA algorithm also shows its superiority in solving a wide range of benchmark functions as well as obtaining fair power allocation for multiple users in NOMA-VLC-B5G systems compared with the state-of-the-art algorithms.1

Pneumonia is a major cause of death among children below the age of 5 years, globally. It is especially prevalent in developing and underdeveloped nations where the risk factors for the disease such as unhygienic living conditions, high levels of pollution and overcrowding are higher. Radiological examination (usually X-ray scans) is conducted to detect pneumonia, yet it is prone to subjective variability and can lead to disagreements among different radiologists. To detect traces of pneumonia from X-ray images, a more robust method is therefore required, which can be achieved by using a computer-aided diagnosis (CAD) system. In this study, we develop a two-stage framework, using the combination of deep learning and optimization algorithms, which is both accurate and time-efficient. In its first stage, the proposed framework extracts feature using a customized deep learning model called DenseNet-201 following the concept of transfer learning to cope with the scanty available data. In the second stage, we then reduce the feature dimension using an improved sine cosine algorithm equipped with adaptive beta hill climbing-based local search algorithm. The optimized feature subset is utilized for the classification of “Pneumonia” and “Normal” X-ray images using a support vector machines classifier. Upon an evaluation on a publicly available data set, the proposed method demonstrates the highest accuracy of 98.36% and sensitivity of 98.79% with a feature reduction of 85.55% (74 features selected out of 512), using a five-fold cross-validation scheme. Extensive additional experiments on continuous benchmark functions as well as the CEC-2017 test suite further showcase the superiority and suitability of our proposed approach in application to real-valued optimization problems. The relevant codes for the proposed method can be found in https://github.com/soumitri2001/Pneumonia-Detection-Local-Search-aided-SCA.

Exploring the finest shortest-path traveling salesman optimization application is a typical NP-hard problem. Similarly the solution of the large-scale optimization applications is also a big challenging issue in front of scientists. First, African Vultures Optimization Algorithm (AVOA) was developed to resolve continuous applications where it performed fine. In the last few months, many enhanced strategies of AVOA have been offered in recent literature works and it has been extensively utilized to resolve large-scale engineering optimization applications. This study offers a newly modified dimension learning hunting (DLH)-based AVOA called DLHAV algorithm to resolve highly complex continuous and discrete applications. It helps improve the imbalance amid the hunting (or exploitation) and search (or exploration), the lack of crowd diversity, slow convergence speed, trapping in local optima, and early convergence of the AVOA variant. The proposed strategy benefits from a newly driven approach called the DLH search approach congenital from the separate exploitation behavior of vultures in the search domain. DLH exploration strategy utilizes a distinct method to make the best neighborhood for all vultures in which the nearest member information can be supplied amid vultures. DLH helps in improving the balance amid global and local and sustains diversity. To scrutinize the performance of DLHAV, the solutions of the DLHAV method are verified on 29-CEC'17 and 10-CEC'20 with familiar comparative methods and some other classical optimization approaches over many familiar traveling salesman problem/large-scale instances. With the intention of attaining unbiased and rigorous comparison, descriptive statistics such as standard deviation and mean have been applied, and the statistical Friedman test is also conducted. The experimental solution carried out in this study has revealed that the proposed algorithm outperforms significantly over the other alternative optimizers.

This paper presents a novel bio-inspired optimization algorithm called Coronavirus Optimization Algorithm (COVIDOA). COVIDOA is an evolutionary search strategy that mimics the mechanism of coronavirus when hijacking human cells. COVIDOA is inspired by the frameshifting technique used by the coronavirus for replication. The proposed algorithm is tested using 20 standard benchmark optimization functions with different parameter values. Besides, we utilized five IEEE Congress of Evolutionary Computation (CEC) benchmark test functions (CECC06, 2019 Competition) and five CEC 2011 real-world problems to prove the proposed algorithm's efficiency. The proposed algorithm is compared to eight of the most popular and recent metaheuristic algorithms from the state-of-the-art in terms of best cost, average cost (AVG), corresponding standard deviation (STD), and convergence speed. The results demonstrate that COVIDOA is superior to most existing metaheuristics.

The rapid spread of coronavirus disease has become an example of the worst disruptive disasters of the century around the globe. To fight against the spread of this virus, clinical image analysis of chest CT (computed tomography) images can play an important role for an accurate diagnostic. In the present work, a bi-modular hybrid model is proposed to detect COVID-19 from the chest CT images. In the first module, we have used a Convolutional Neural Network (CNN) architecture to extract features from the chest CT images. In the second module, we have used a bi-stage feature selection (FS) approach to find out the most relevant features for the prediction of COVID and non-COVID cases from the chest CT images. At the first stage of FS, we have applied a guided FS methodology by employing two filter methods: Mutual Information (MI) and Relief-F, for the initial screening of the features obtained from the CNN model. In the second stage, Dragonfly algorithm (DA) has been used for the further selection of most relevant features. The final feature set has been used for the classification of the COVID-19 and non-COVID chest CT images using the Support Vector Machine (SVM) classifier. The proposed model has been tested on two open-access datasets: SARS-CoV-2 CT images and COVID-CT datasets and the model shows substantial prediction rates of 98.39% and 90.0% on the said datasets respectively. The proposed model has been compared with a few past works for the prediction of COVID-19 cases. The supporting codes are uploaded in the Github link: https://github.com/Soumyajit-Saha/A-Bi-Stage-Feature-Selection-on-Covid-19-Dataset.

This paper evaluates the human resource information systems provided by different vendors using two new hybrid multicriteria decision-making methods that require ordinal data as inputs. First, the grey-point-allocation full-consistency (Grey-PA-FUCOM) weighting method is proposed. The Grey-PA-FUCOM combines the simple point-allocation method widely used by human resource managers and the advanced FUCOM method widely accepted by scholars in grey system theory. Second, the grey-regime method, an extension of the classical regime method based on grey system theory, is used to account for the uncertainty in the evaluation. Next, the Grey-PA-FUCOM weights are applied in conjunction with the grey-regime scheme to evaluate the five vendors. Finally, to validate the results of this study, grey relational analysis with grey numbers, the grey weighted sum model, and a technique for order performance based on the similarity to the ideal solution with grey values are used.

Metaheuristics play a critical role in solving optimization problems, and most of them have been inspired by the collective intelligence of natural organisms in nature. This paper proposes a new metaheuristic algorithm inspired by gorilla troops' social intelligence in nature, called Artificial Gorilla Troops Optimizer (GTO). In this algorithm, gorillas' collective life is mathematically formulated, and new mechanisms are designed to perform exploration and exploitation. To evaluate the GTO, we apply it to 52 standard benchmark functions and seven engineering problems. Friedman's test and Wilcoxon rank-sum statistical tests statistically compared the proposed method with several existing metaheuristics. The results demonstrate that the GTO performs better than comparative algorithms on most benchmark functions, particularly on high-dimensional problems. The results demonstrate that the GTO can provide superior results compared with other metaheuristics.

Robust optimization of real-world problems is essential to reduce the significant negative impact of uncertainties and noises present in the environment. Uncertainties in the decision variables are often handled using explicit or implicit averaging methods, in which the fitness of a solution isjudged based on the objective values of neighbouring solutions. Explicit averaging methods are highly reliable but require additional objective function evaluation, which can significantly increases the overall computational cost of an optimization process. On the other hand, implicit averaging techniques are computationally cheap, yet they suffer from low reliability since they use the history of search in a population-based optimization algorithm. This work proposes a conditional Pareto optimal dominance to improve the reliability of robust optimization methods that use implicit averaging methods. The proposed method is applied to Multi-Objective Particle Swarm optimisation. Empirical study with a benchmark suite shows the benefit of the proposed conditional Pareto optimal dominance in locating robust solutions in multi-objective problems.

Data delivery and acquisition are the main factors needed for the success of any proposed Internet of Medical Things (IoMT) systems. To achieve good performance and high quality of services in IoMT systems, data acquisition, and delivery should be performed accurately. In general, IoMT systems are usually vulnerable to the collected data with missing value(s) since missing data is the main problem that affects the overall performance of any system. This leads to a reduction in the satisfaction level of end users. Missing data for IoMT systems originates from a number of sources, including bad connections, outside attack, or sensing errors. To obtain a high performance in such systems, missing data should be imputed once occurred. In this paper, a dynamic adaptive network-based fuzzy inference system (D-ANFIS) approach is proposed to impute the missing values in a simple yet accurate manner. The major contribution is to impute the missing value(s) once received by dividing the collected data into two groups: 1) complete dataset (without missing data) and 2) incomplete dataset (with missing data). A holdout method is used to train the D-ANFIS using complete data, while the incomplete dataset is used to impute the missing value(s). Two methods are used to evaluate the final performance of IoMT application: 1) adaptive network-based fuzzy inference system (ANFIS) with genetic algorithm (ANFIS-GA) and 2) ANFIS with particle swarm optimization (ANFIS-PSO). The results show that the performance of IoMT is enhanced 5% using ANFIS-GA and 3% using ANFIS-PSO.

Due to the gradient-free mechanism, flexibility, high local optima avoidance, and simplicity, meta-heuristics have been reliable alternatives to conventional optimisation techniques over the course of last two decades. This has resulted in the application of such techniques in diverse branches of science and technology. Despite all the successful applications, meta-heuristics are less effective in real-time applications where there is a need to find the optimal solutions instantly due to the need for a large number of function evaluations. This paper investigates the effectiveness of meta-heuristics in modelling hands for recognising hand gestures. Several well-known and recent algorithms have been utilised to find an optimal shape for a 3D model of the hand. Qualitative and quantitative results have been collected to see how well meta-heuristics perform in this field. Firstly, the results show that a free model of the hand can be very expensive to optimise: a constrained model is essential to reduce the search space. Secondly, the results show that population-based algorithms are more suitable rather than individual-based mainly because of the presence of a large number of local solutions. Thirdly, despite the accuracy of the optimal model obtained using population-based algorithms, the run time is an issue which should be considered. Finally, several recommendations are made for reducing the run time of meta-heuristics and making them more practical in the field of gesture detection.