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Features within the dataset carry a significant role; however, resource utilization, prediction-time, and model weight are increased by utilizing high-dimensional data in intrusion-detection paradigm. This paper aims to design a novel lightweight intrusion detection system in two phases utilizing a swarm intelligence-based technique. In 1st-phase, essential features are selected using particle swarm optimization algorithm by considering imbalanced dataset. Ant colony optimization algorithm is utilized in 2nd-phase for extracting information-rich and uncorrelated features. Additionally, genetic algorithm is employed for fine-tuning each detection model. Proposed model's performance is evaluated on different base and ensemble classifiers, and it is observed that xgboost achieves best accuracy with 90.38%, 92.63%, and 97.87% on NSL-KDD, UNSW-NB15, and CSE-CIC-IDS2018 datasets, respectively. The proposed model also outperforms other traditional dimensionality reduction and state-of-the-art approaches with statistical validation. This paper also analyses objective function of each metaheuristic algorithm used in this paper, applying convergence graphs, box, and swarm plots. [ABSTRACT FROM AUTHOR]

This paper is inspired by traditional rural fishing methods and proposes a new metaheuristic optimization algorithm based on human behavior: Catch Fish Optimization Algorithm (CFOA). This algorithm simulates the process of rural fishermen fishing in ponds, which is mainly divided into two phases: the exploration phase and the exploitation phase. In the exploration phase, there are two stages to search: first, the individual capture stage based on personal experience and intuition, and second, the group capture stage based on human proficiency in using tools and collaboration. Transition from independent search to group capture during the exploration phase. Exploitation phase: All fishermen will surround the shoal of fish and work together to salvage the remaining fish, a collective capture strategy. CFOA model is based on these two phases. This paper tested the optimization performance of CFOA using IEEE CEC 2014 and IEEE CEC 2020 test functions, and compared it with 11 other optimization algorithms. We employed the IEEE CEC2017 function to evaluate the overall performance of CFOA. The experimental results indicate that CFOA exhibits excellent and stable optimization capabilities overall. Additionally, we applied CFOA to data clustering problems, and the final results demonstrate that CFOA's overall error rate in processing clustering problems is less than 20%, resulting in a better clustering effect. The comprehensive experimental results show that CFOA exhibits excellent optimization effects when facing different optimization problems. CFOA code is open at https://github.com/Meky-1210/CFOA.git. [ABSTRACT FROM AUTHOR]

Cloud computing has revolutionized the way a variety of ubiquitous computing resources are provided to users with ease and on a pay-per-usage basis. Task scheduling problem is an important challenge, which involves assigning resources to users' Bag-of-Tasks applications in a way that maximizes either system provider or user performance or both. With the increase in system size and the number of applications, the Bag-of-Tasks scheduling (BoTS) problem becomes more complex due to the expansion of search space. Such a problem falls in the category of NP-hard optimization challenges, which are often effectively tackled by metaheuristics. However, standalone metaheuristics generally suffer from certain deficiencies which affect their searching efficiency resulting in deteriorated final performance. This paper aims to introduce an optimal hybrid metaheuristic algorithm by leveraging the strengths of both the Artificial Gorilla Troops Optimizer (GTO) and the Honey Badger Algorithm (HBA) to find an approximate scheduling solution for the BoTS problem. While the original GTO has demonstrated effectiveness since its inception, it possesses limitations, particularly in addressing composite and high-dimensional problems. To address these limitations, this paper proposes a novel approach by introducing a new updating equation inspired by the HBA, specifically designed to enhance the exploitation phase of the algorithm. Through this integration, the goal is to overcome the drawbacks of the GTO and improve its performance in solving complex optimization problems. The initial performance of the GTOHBA algorithm tested on standard CEC2017 and CEC2022 benchmarks shows significant performance improvement over the baseline metaheuristics. Later on, we applied the proposed GTOHBA on the BoTS problem using standard parallel workloads (CEA-Curie and HPC2N) to optimize makespan and energy objectives. The obtained outcomes of the proposed GTOHBA are compared to the scheduling techniques based on well-known metaheuristics under the same experimental conditions using standard statistical measures and box plots. In the case of CEA-Curie workloads, the GTOHBA produced makespan and energy consumption reduction in the range of 8.12–22.76% and 6.2–18.00%, respectively over the compared metaheuristics. Whereas for the HPC2N workloads, GTOHBA achieved 8.46–30.97% makespan reduction and 8.51–33.41% energy consumption reduction against the tested metaheuristics. In conclusion, the proposed hybrid metaheuristic algorithm provides a promising solution to the BoTS problem, that can enhance the performance and efficiency of cloud computing systems. [ABSTRACT FROM AUTHOR]

Accurate classification of gene expression data is crucial for disease diagnosis and drug discovery. However, gene expression data usually has a large number of features, which poses a challenge for accurate classification. In this paper, a novel feature selection method based on minimal redundancy maximal relevance (mRMR) and aquila optimizer is proposed, which introduces the mRMR method in the initialization stage of the population to generate excellent initial populations, effectively improve the quality of the population, and then, the using random opposition-based learning strategy to improve the diversity of aquila population and accelerate the convergence speed of the algorithm, and finally, introducing inertia weight in the position update formula in the late iteration of the aquila optimizer to avoid the algorithm falling into the local optimum and improve the algorithm's capability to find the optimum. In order to verify the effectiveness of the proposed method, ten real gene expression datasets are selected in this paper and compared with several meta-heuristic algorithms. Experimental results show that the proposed method is significantly superior to other meta-heuristic algorithms in terms of fitness value, classification accuracy and the number of selected features. Compared with the original aquila optimizer, the average classification accuracy of the proposed method on KNN and SVM classifiers is improved by 3.48–12.41% and 0.53–18.63% respectively. The proposed method significantly reduces the feature dimension of gene expression data, retains important features, and obtains higher classification accuracy, providing a new method and idea for feature selection of gene expression data. [ABSTRACT FROM AUTHOR]

Harris Hawks Optimization (HHO) is a novel meta-heuristic optimization algorithm. The algorithm is inspired by the behavior of Harris Hawks collaborating with each other to pursue prey in nature. The algorithm has the advantages of simple structure, fewer parameters, easy implementation, and excellent performance on high-dimensional problems. However, the algorithm also suffers from the inability to strike a good balance between exploration and exploitation, low convergence accuracy, and slow convergence speed in the early stage. In response to these defects, this paper will introduce three strategies to the HHO: a non-negative stochastic shrinkage exponential energy function, a Cauchy-Gaussian-based dynamic variance reduction selection strategy, and a greedy-difference-based selection strategy. The improved algorithm TSHHO is analyzed on the well-established 28 benchmark test functions, and four industrial engineering design problems. The experimental results show that the TSHHO algorithm proposed in this paper can achieve a better balance in the exploration and development stages,the strategies significantly improve the search efficiency, convergence accuracy, and robustness of the algorithm. [ABSTRACT FROM AUTHOR]

Workflow scheduling poses a significant challenge due to the large scale of workflows and heterogeneity of algorithms in cloud-fog environment. Also, these nature-inspired algorithms have limitations, such as being trapped in local optima, leading to failure in achieving a global optimal solution. Since, workflow scheduling is used to enhance the optimization of task allocation and execution within cloud-fog computing. Therefore, various metaheuristic algorithms have been recommended by researchers for addressing scheduling problems. Our paper used a hybrid algorithm "particle swarm optimization-whale optimization algorithm" (PSO-WOA) to overcome the abovementioned issues. When applied individually, the PSO becomes trapped in the global optimum due to the presence of a high-dimensional search space, while the WOA, which lacks flexibility in search spacing, becomes trapped in local optima. The proposed PSO-WOA algorithm overcomes the constrained exploration of search spaces observed in the population-based WOA algorithm and efficiently mitigates the challenge of becoming trapped in local optima. The simulation was performed using the WorkflowSim toolkit, and the results were tested for several test cases on five scientific workflows with varying numbers of tasks and iterations. The results illustrate that the hybrid PSO-WOA approach produced superior outcomes when compared with state-of-the-art algorithms (ACO, PSO, WOA, GA and BH-FWA) for both total execution cost (TEC) and total execution time (TET). After analysis of results, two workflows i.e., montage and epigenomics stood apart from others in terms of time and cost. Statistical analysis was done with Wilcoxon and Friedman tests and were found to be significant. In summary, this study provides a comparative analysis of enhancing both TET and TEC in a hybrid algorithm along with its impact on performance metrics. [ABSTRACT FROM AUTHOR]

In this research paper, we propose a two-stage hybrid approach that uses machine learning techniques and meta-heuristic algorithms. The first step, known as data preparation, involves converting string values to numeric format and subsequently normalizing the data. To increase the performance beyond the limitations of traditional methods, we use population-based meta-heuristic algorithms, namely Atom Search Optimization (ASO) and Equilibrium Optimization (EO), for feature selection, aiming to achieve global optimization. The second step, called attack detection, focuses on distinguishing normal traffic from malicious traffic. To improve the performance of this step, we use K-means clustering and firefly algorithm (FA). In addition, an elitism method is randomly integrated. The resulting approach is called ASO-EO-FA-K-means. We evaluate the performance of our proposed method using two datasets, namely NSL-KDD, UNSW_NB15, and KDD_CUP99. To establish a benchmark, we compare our method with other approaches including Particles Swarm Optimization (PSO), Genetic, Grey Wolf Optimization (GWO), Ant colony optimization (ACO), Harris Hawk Optimization (HHO), NSGA-2, Multi-objective PSO, Multi-objective GWO, learning vector quantization (LVQ), XGBoost, particle swarm optimization based on C4.5 (PSO-C4.5) and genetic algorithm based on multilayer perceptron (GA-MLP)) we compare. The evaluation results show that the proposed method achieves the highest accuracy and the lowest error rate in three datasets NSL-KDD and UNSW_NB15 KDD-CUP99 with accuracy values of 0.998, 0.995 and 0.995, respectively. In addition, our method shows superior efficiency in terms of computation time. In general, our research shows the effectiveness of the ASO-EO-FA-K-means method in intrusion detection and provides better accuracy and efficiency compared to alternative approaches. In all three data sets, the results have shown that NSL-KDD data set with MSE 0.012, accuracy value 0.998 has obtained better results than other data sets. [ABSTRACT FROM AUTHOR]

Virtual machine placement (VMP) has a significant importance with respect to resource utilization in cloud data centers. Indeed, the optimized management of machine placement usually results in a significant reduction in energy consumption. VMP is a bin packing problem generalization, which is a well known hard combinatorial optimization problem. Besides being NP-hard, VMP is characterized by conflicting objectives and a noisy search space. Meta-heuristics, such as genetic algorithms, particle swarm optimization (PSO), cuckoo search (CS), tabu search and simulated annealing (SA) have been shown to be effective for this category of problems. This paper reports a performance comparison between SA, CS and PSO meta-heuristics to solve the VMP problem. In contrast to reported research work in this area, we study the performance behavior of these three meta-heuristics with respect to, not only the quality of solutions, but also the quality of the explored solution sub-space, in addition to the convergence speed towards reported solutions and the speed with which each meta-heuristic evolves towards the best reported optimized solution. Extensive simulations on randomly generated tests with sizes varying between 200 and 1000 virtual machine demands show that PSO achieves the best performance behavior with respect to all criteria. Moreover, for all tests, PSO produces a reduction of as much as 17% of the number of physical machines, 15% of the energy cost and 21% of the resource utilization of physical machines. [ABSTRACT FROM AUTHOR]

The software requirement selection problem endeavors to ascertain the optimal set of software requirements with the dual objectives of minimizing software cost and maximizing customer satisfaction. The intricate nature of this problem stems from the interdependencies among individual software requirements, rendering it a complicated NP-hard problem. In this paper, we introduce a novel multi-objective optimization algorithm christened the Quantum -Inspired Multi-Objective African Vulture Optimization Algorithm with Hierarchical Structures (QMO_HSAVOA), where hierarchical structure and in-quantum computation ideas are introduced to improve the performance of the algorithm in QMO_HSAVOA. To gauge the efficacy of QMO_HSAVOA in tackling the software requirement selection problem, we empirically apply it to the problem, orchestrating three distinct simulation experiments. The ensuing evaluation of QMO_HSAVOA's performance is conducted with meticulous scrutiny through the application of Friedman's statistical test to the experimental outcomes. These results decisively demonstrate that the proposed QMO_HSAVOA not only delivers exceptionally competitive outcomes but also outshines alternative algorithms. This finding provision is an innovative and highly efficient solution for addressing the software requirement selection problem. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid metaheuristic algorithm called the remora crayfish optimization algorithm (HRCOA) designed for solving continuous optimization problems. The crayfish optimization algorithm (COA), recently proposed as a meta-heuristic algorithm (MA), exhibits certain limitations such as imbalanced exploration and exploitation capacities, susceptibility to premature optimization, and a propensity for stagnation. To address these shortcomings, we incorporate the exploitation operators from the remora optimization algorithm (ROA) to enhance the exploitative behaviors of COA. In addition, we simplify the summer resort operator in the original COA to streamline the search operator design, thus avoiding unnecessary complexity. Furthermore, numerical experiments on 10-dimensional (D) and 20-D CEC2022 benchmark functions, 50-D and 100-D CEC2020 benchmark functions, engineering optimization problems, and wireless sensor networks (WSNs) coverage optimization problems are conducted to investigate the performance of our proposed HRCOA comprehensively. We compare the proposed HRCOA against eight well-known state-of-the-art MAs, including CMAES and the original COA, as competitor algorithms. The experimental and statistical results confirm the effectiveness, competitiveness, and scalability of our proposal. Finally, we conclude that the proposed HRCOA possesses significant potential for addressing diverse optimization challenges in real-world scenarios. [ABSTRACT FROM AUTHOR]

This paper addresses the critical gap in the understanding of the effects of various configurations and feedback mechanisms on the performance of hybrid metaheuristics (HMs) in unsupervised clustering applications. Despite the widespread use of HMs due to their ability to leverage multiple optimization methods, the lack of comprehensive studies on their configuration and feedback mechanisms effects often results in sub-optimal clustering performances and premature convergence. To tackle these issues, we introduce two algorithms for implementing eight distinct HM schemes, focusing on the impacts of parallel and serial processing models along with different feedback mechanisms. Our approach involves selecting candidate metaheuristics based on a mix of evolutionary and swarm-based methods, including the k-means algorithm, to form various HM-based clustering schemes. These schemes were then rigorously evaluated across a range of datasets and feedback mechanisms, further assessing their efficiency in the deployment of smart grid base stations. Performance analysis was based on total fitness evaluations, timing capabilities, and clustering accuracy. The results revealed that parallel HMs with decoupled feedback mechanisms performed best in terms of accuracy but at the cost of slower convergence rates as compared to serial HMs. Our findings further suggest that serial HMs will be best suited for time-sensitive applications where a compromise between speed and accuracy is acceptable, while parallel HMs with decoupled feedback mechanisms are preferable for scenarios where precision is paramount. This research significantly contributes to the field by providing a detailed analysis of HM performance in varying conditions, thereby guiding the selection of appropriate HM schemes for specific clustering tasks. [ABSTRACT FROM AUTHOR]

Feature selection plays a critical role in reducing high-dimensional feature space in machine learning applications without affecting the accuracy of performance. The feature selection problem has been extensively studied in the literature. Nevertheless, few studies have been conducted on unsupervised text feature selection because of the absence of feature class labels and local optimization limitations. For that, this paper proposes three binary versions of the fire hawk optimizer based on different transfer functions for unsupervised text feature selection that can be used to select the most informative features for text clustering. The internal feature subset was evaluated using a mean absolute difference filter. The performance of the proposed methods is tested and compared with other state-of-the-art metaheuristic algorithms on several different benchmark text datasets from different sources using various evaluation metrics. The K-means clustering is applied to cluster documents based on the features selected by the methods. The results of the experiments indicate the effectiveness of the proposed method based on the S-shaped transfer function for improving the performance of document clustering compared with other feature selection methods. [ABSTRACT FROM AUTHOR]

Feature Selection (FS), a pre-processing step used in the majority of big data processing applications, aims to eliminate irrelevant and redundant features from the data. Its purpose is to select a final set of data characteristics that best represent the data as a whole. To achieve this, it explores every potential solution in order to identify the optimal subset. Meta-heuristic algorithms have been found to be particularly effective in solving FS problems, especially for high-dimensional datasets. This work adopts a recently developed meta-heuristic called the White Shark Optimizer (WSO) due to its simplicity and low computational overhead. However, WSO faces challenges in effectively balancing exploration and exploitation, particularly in complex multi-peak search problems. It tends to converge prematurely and get stuck in local optima, which can lead to poor search performance when dealing with FS problems. To overcome these issues, this paper presents three enhanced binary variants of WSO for well-known FS problems. These variants are as follows: (1) Binary Distribution-based WSO (BDWSO), where the algorithm refines the positions of white sharks by considering both the average and standard deviation of the current shark, the local best shark, and the global best shark. This strategy is designed to alleviate issues of premature convergence and stagnation during iterations , (2) Binary Sine Cosine WSO (BSCWSO), which uses sine and cosine adaptive functions for the social and cognitive components of the position update rule, and (3) Binary Hybrid Sine Cosine WSO (BHSCWSO), which employs sine and cosine acceleration factors to regulate local search and achieve convergence to the global optimal solution. Additionally, the population was initialized using the Opposition-Based Learning (OBL) mechanism, and the sine map was used to modify the inertia weight of WSO. These revised variants of WSO were established to have a better harmony between the exploration and exploitation facets. The proposed methods were extensively compared to the fundamental binary WSO and other well-known algorithms in the field. The experimental findings and comparisons demonstrate that the proposed methods outperform the conventional and most evaluated similar algorithms in terms of robustness and solution quality. In terms of classification accuracy, number of selected features, specificity, sensitivity, and fitness values, the proposed BHSCWSO optimizer performed better than all other proposed peer optimizers, including BSWO, BDWSO, and BSCWSO, in 11, 8, 13, 18, and 10 datasets, respectively. The proposed BHSCWSO optimizer showed performance levels of more than 90% in terms of accuracy, sensitivity, and specificity metric measures on 15, 14, and 11 of the 24 datasets deemed. [ABSTRACT FROM AUTHOR]

As a new network architecture, Software Defined Network (SDN) has been widely used in recent years. The continuous increase of network scale makes centralized SDN unable to meet network requirements, so distributed SDN comes into being. The application of the optimization algorithm to solve the Controller Placement Problem (CPP) can improve the performance of distributed SDN. The meta-heuristic algorithm has been widely concerned because it is not easy to fall into the local optimal solution. Based on the advantages of meta-heuristic algorithm, this paper uses the multi-point replacement method and the two-point replacement method which have low complexity to discretization African Vultures Optimization Algorithm (AVOA) in the search and development stages respectively to solve CPP in SDN. In addition, the performance of the improved optimization algorithm is compared with other algorithms on three real SDN topologies of different scales. The research results show that compared with other six latest meta-heuristic algorithms, the improved optimization algorithm in this paper can obtain the lowest average delay between the controller and switch in the shortest time. Compared with PHCPA with the best performance, IAVOA proposed in this paper improves the convergence speed of 38.16%. [ABSTRACT FROM AUTHOR]

Task scheduling in cloud computing systems is an important and challenging NP-Hard problem that involves the decision to allocate resources to tasks in a way that optimizes a performance metric. The complexity of this problem rises due to the size and scale of cloud systems, the heterogeneity of cloud resources and tasks, and the dynamic nature of cloud resources. Metaheuristics are a class of algorithms that have been used effectively to solve NP-Hard cloud scheduling problems (CSP). Bonobo optimizer (BO) is a recent metaheuristic-based optimization algorithm, which mimics several interesting reproductive strategies and social behaviour of Bonobos and has shown competitive performance against several state-of-the-art metaheuristics for many optimization problems. Besides its good performance, it still suffers from inherent deficiencies such as imbalanced exploration-exploitation and trapping in local optima. This paper proposes a modified version of the BO algorithm called mBO to solve the cloud scheduling problem to minimize two important scheduling objectives; makespan and energy consumption. We have incorporated four modifications namely Dimension Learning-Based Hunting (DLH) search strategy, (2) Transition Factor (TF), (3) Control Randomization (DR), and 4) Control Randomization Direction in the traditional BO to improve the performance, which helps it to escape local optima and balance exploration-exploitation. The efficacy of mBO is initially tested on the popular standard CEC'20 benchmarks followed by its application on the CSP problem using real-world supercomputing workloads namely CEA-Curie and HPC2N. Results and observations reveal the supremacy of the proposed mBO algorithm over many contemporary metaheuristics by a competitive margin for both CEC'20 benchmarks and the CSP problem. Quantitatively for the CSP problem, mBO was able to reduce makespan and energy consumption by 8.20–23.73% and 2.57–11.87%, respectively against tested algorithms. For HPC2N workloads, mBO achieved a makespan reduction of 10.99–29.48% and an energy consumption reduction of 3.55–30.65% over the compared metaheuristics. [ABSTRACT FROM AUTHOR]

The rapid integration of cloud computing and edge computing has brought the cloud-edge environment into the spotlight in information technology. Within this context, the selection of high-quality and reliable services is crucial to meet the needs of users. However, ensuring the reliability of service information is a challenge due to its vulnerability to tampering. This research paper proposes a method for service selection in the cloud-edge environment based on blockchain smart contracts. By leveraging blockchain technology, this method achieves decentralized and trustworthy service selection. Through smart contracts, user interactions are securely recorded, significantly reducing the risk of information tampering and enhancing information reliability. Additionally, the Arithmetic Optimization Algorithm is improved for service selection on the blockchain by introducing mutation and crossover operations. Experimental results demonstrate that this method effectively prevents tampering with service information and improves the utility value of selected services compared to traditional methods and metaheuristic algorithms mentioned. [ABSTRACT FROM AUTHOR]

Medical imaging is essential in modern healthcare because it assists physicians in the diagnosis of cancer. Various tissues and features in medical imaging can be recognized using image segmentation algorithms. This feature makes it possible to pinpoint and define particular areas, which makes it easier to precisely locate and characterize anomalities or lesions for cancer diagnosis. Among cancers affecting women, breast cancer is particularly prevalent, underscoring the urgent need to improve the accuracy of image segmentation for breast cancer in order to assist medical practitioners. Multi-threshold image segmentation is widely acknowledged for its direct and effective characteristics. In this context, this paper suggests a refined whale optimization algorithm to improve the segmentation accuracy of breast cancer data. This algorithm optimizes performance by combining a quantum phase interference mechanism and an enhanced solution quality strategy. This work compares the method with classical, homogeneous, state-of-the-art algorithms and runs experiments on the IEEE CEC2017 benchmark to validate its practical optimization performance. Furthermore, a multi-threshold image segmentation algorithm-based image segmentation technique is presented in this study. The Berkeley segmentation dataset and the breast invasive ductal carcinomas segmentation dataset are segmented using the approach using a non-local means two-dimensional histogram and Renyi's entropy. Experimental results demonstrate the excellent performance of this segmentation method in image segmentation applications across both low and high threshold levels. As a result, it emerges as a valuable image segmentation technique with practical applications. [ABSTRACT FROM AUTHOR]

Lupus nephritis (LN) is the most common symptom of systemic lupus erythematosus, emphasizing its importance in the field of medicine. The growing frequency of LN has increased the need for effective image segmentation algorithms. With the increasing prevalence of LN, the demand for efficient image segmentation techniques has grown. To enhance the efficiency of image segmentation of LN, many researchers employ a methodology that integrates multi-threshold image segmentation (MTIS) with metaheuristic algorithms (MAs). However, conventional MAs-based MTIS methods tend to converge towards local optima and have slow convergence rates, resulting in poor segmentation results within a limited iteration number. To address these challenges, this study proposes an advanced optimization algorithm termed Biogeography-based Learning Rime Optimization Algorithm (BLRIME) and integrates it with the MTIS approach for LN image segmentation. MTIS employs a non-local means 2D histogram to gather image information and uses 2D Renyi's entropy as the fitness function. BLRIME builds upon the foundation of the RIME algorithm, incorporating two significant strategies. Firstly, the introduction of piecewise chaotic mapping (PCM) ameliorates the quality of the initial solution provided by the algorithm. Secondly, a stochastic biogeography-based learning strategy (SBLS) prevents the RIME algorithm from falling into the local optimum early. SBLS is proposed by this study based on the biogeography-based learning strategy. In order to assess the efficacy of the BLRIME, this paper devises a series of experiments to compare it with similar algorithms presented at IEEE CEC 2017. Experimental studies have been conducted to provide empirical evidence demonstrating the superior rates of convergence and precision achieved by BLRIME. Subsequently, the BLRIME-based MTIS algorithm is employed to segment the LN images compared to other peer algorithms. Furthermore, the peak signal-to-noise ratio, feature similarity index, and structural similarity index are utilized as evaluation metrics to assess the image segmentation outcomes. The experimental results prove that BLRIME demonstrates superior global search capabilities, resulting in remarkable outcomes in the segmentation of LN images. [ABSTRACT FROM AUTHOR]

Classification as an essential part of Machine Learning and Data Mining has significant roles in engineering, medicine, agriculture, military, etc. With the evolution of data collection tools and the unceasing efforts of researchers, new datasets with huge dimensions are obtained so that each data sample has multiple labels. This kind of classification is called Multi-Class Classification (MLC) and demands new techniques to predict the set of labels for a data instance. To date, a variety of methods have been proposed to solve MLC problems. However, new high-dimensional datasets with challenging patterns are being developed, making it necessary for new research to be conducted to develop more efficient methods. This paper presents a novel framework named QLHA to solve MLC problems more efficiently. In the QLHA, the Principal Label Space Transformation (PLST) and Ridge Regression (RR) are recruited to predict the labels of data. Next, an effective objective function is introduced. Also, a hybrid metaheuristic algorithm called QGTOJS is provided to optimize objective value and enhance the predicted labels by selecting the most relevant features. In the QGTOJS, the Gorilla Troops Optimization (GTO) and Jellyfish Search algorithm (JS) are binarized and hybridized through a modified variant of the Q-learning algorithm. Besides, an adjusted Hill Climbing strategy is adopted to balance the exploration and exploitation and improve local optima departure. Likewise, a local search mechanism is provided to enhance searchability as much as possible. Eventually, the QLHA is applied to ten multi-label datasets and the obtained results are compared with heuristic and metaheuristic-based MLC methods numerically and visually. The experimental results disclosed the effectiveness of the contributions and superiority of the QLHA over competitors. [ABSTRACT FROM AUTHOR]

This paper proposes a meta-heuristic called Eel and Grouper Optimizer (EGO). The EGO algorithm is inspired by the symbiotic interaction and foraging strategy of eels and groupers in marine ecosystems. The algorithm's efficacy is demonstrated through rigorous evaluation using nineteen benchmark functions, showcasing its superior performance compared to established meta-heuristic algorithms. The findings and results on the benchmark functions demonstrate that the EGO algorithm outperforms well-known meta-heuristics. This work also considers solving a wide range of real-world practical engineering case studies including tension/compression spring, pressure vessel, piston lever, and car side impact, and the CEC 2020 Real-World Benchmark using EGO to illustrate the practicality of the proposed algorithm when dealing with the challenges of real search spaces with unknown global optima. The results show that the proposed EGO algorithm is a reliable soft computing technique for real-world optimization problems and can efficiently outperform the existing algorithms in the literature. [ABSTRACT FROM AUTHOR]

The continual increase in the amount of generated data by social media, IoT devices, and monitoring systems have motivated the use of Distributed Data Stream Processing (DSP) systems to harness data in a real-time manner. The scheduling of processing tasks in DSP systems across the machines in a cluster or cloud environment is an NP-Hard problem. Different scheduling schemes have been proposed to address the scheduling problem, but most fail to take into account the runtime adaptation and workload changes after initial scheduling. In this paper, we propose a new scheduler (GT-Scheduler) that leverages a heuristic and rule-based algorithm to schedule tasks at near-optimal performance alongside using a meta-heuristic algorithm to make runtime adaptation. Firstly, K-way graph partitioning divides the tasks in an application graph according to the communication patterns. It places tasks with the highest amount of communication near each other to limit an increase in the topology response time. Secondly, instead of assigning tasks to the worker nodes, a partition of tasks is assigned to the nodes by adopting a greedy strategy. If the capacity of nodes is insufficient to host a specific partition of tasks, this partition is iteratively divided by the k-partitioning algorithm to assign to a proper node. The idea of runtime adaptation lies in detecting overutilized worker nodes and reassigning their tasks by exploiting a Tabu-Search and a new scoring strategy to find the best solution in a way that no worker node is overutilized. GT-Scheduler is implemented on the standard Apache Storm and using the standard benchmarks, it is shown that GT-Scheduler outperforms the R-Storm and the Online-Scheduler by at least 35% in reducing the topology response time. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is a population-based metaheuristic search algorithm that exhibits excellent performance. However, it is sensitive to mutation strategies and control parameters.To mitigate the impact of these factors, this paper proposes refining differential evolution with mutation rate and neighborhood weight local search(MRNLDE). The algorithm guides individuals in selecting an appropriate mutation mode through the mutation rate, effectively utilizing the evolutionary information inherent in the individuals themselves, thereby enhancing the search efficiency of the algorithm. Furthermore, the neighborhood individual information is utilized to re-explore the solution space, allowing for a comprehensive exploration of individual evolutionary potential and enhancing the algorithm's diversity. The performance of MRNLDE was validated under two sets of benchmark problems at the Institute of Electrical and Electronics Engineers (IEEE) Conference on Computing in Evolution (CEC), and the results show that MRNLDE performs well overall. [ABSTRACT FROM AUTHOR]

In today's world, agricultural production and operation activities generate a lot of data. As a result, computer-aided agriculture applications have become a hot topic in the study, with various machine learning (ML) algorithms being used to classify agricultural data. This paper presents a comparative study consisting of a combination of ML algorithms with meta-heuristic algorithms for feature selection to improve the classification capability of ML algorithms by finding the features that significantly impact accuracy. We have used six different meta-heuristic algorithms for feature selection. Experiments are conducted on four different agricultural datasets with five classification models. To understand the effect of proposed models, the selected features are fed into the ML algorithms. The results prove that combining ML and meta-heuristic algorithms achieves higher classification accuracy with fewer features on agricultural datasets. [ABSTRACT FROM AUTHOR]

Fog Computing paradigm that provisions low-latency computing services at the edge network, is a bonanza for supply chain computing resources in Internet of Things (IoT) applications. In different scenarios such as smart homes/healthcare systems, multiple IoT applications are distributed simultaneously in cloud and fog nodes to provide different IoT-based services. In addition, each program requires resources and has its desired quality of service (QoS) which should be met. One of the key challenges in fog computing environment is how to efficiently allocate resources to IoT applications because inefficient resource allocation leads to burdening providers high costs and it lowers down the delivered QoS to users. Since the majority of IoT applications are time-sensitive, the low delay and near physically allocated resources improve the amount of delivered QoS. Therefore, the resource clustering algorithms with the lowest distance error rate and the lowest delay as a consequence are favorable. The aim is to reduce clustering errors and improve the overall performance of the system. This paper formulates resource allocation to IoT applications in heterogeneous 4-layered fog platforms to an optimization problem. To solve this problem, a fusion approach incorporating a genetic algorithm (GA) and the k-means clustering approach is presented. Firstly, it utilizes the k-means approach and Jaccard measurement to cluster fog nodes with a minimum clustering rate. Then, the resources of fog clusters are allocated to IoT devices with the minimum error rate by incorporating GA algorithm. This selection of processing nodes in a fog layer helps to minimize latency and allows applications to access resources simultaneously. The simulation results in extensive scenarios prove the superiority of the proposed algorithm against other successful meta-heuristic approaches in terms of the objective function and lowest error/delay rate. [ABSTRACT FROM AUTHOR]

In this paper, the learning process of multilayer perceptron (MLP) neural network is boosted using hybrid metaheuristic optimization algorithms. Normally, the learning process in MLP requires suitable settings of its weight and bias parameters. In the original version of MLP, the gradient descent algorithm is used as a learner in MLP which suffers from two chronic problems: local minima and slow convergence. In this paper, six versions of memetic algorithms (MAs) are proposed to replace gradient descent learning mechanism of MLP where adaptive β -hill climbing (A β HC) as a local search algorithm is hybridized with six population-based metaheuristics which are hybrid flower pollination algorithm, hybrid salp swarm algorithm, hybrid crow search algorithm, hybrid grey wolf optimization (HGWO), hybrid particle swarm optimization, and hybrid JAYA algorithm. This is to show the effect of the proposed MA versions on the performance of MLP. To evaluate the proposed MA versions for MLP, 15 classification benchmark problems with different size and complexity are used. The A β HC algorithm is invoked in the improvement loop of any MA version with a probability of B r parameter, which is investigated to monitor its effect on the behavior of the proposed MA versions. The B r setting which obtains the most promising results is then used to set the hybrid MA. The results show that the proposed MA versions excel the original algorithms. Moreover, HGWO outperforms all other MA versions in almost all the datasets. In a nutshell, MAs are a good choice for training MLP to produce results with high accuracy. [ABSTRACT FROM AUTHOR]

Cloud computing has experienced extraordinary development across a wide range of industries by giving customers the flexibility to employ computing resources as needed. The task scheduling problem is one of several major challenges in cloud computing, and it should be scheduled effectively to minimize makespan and maximize resource utilization. Therefore, this paper put forward an improved scheduling efficiency algorithm called Enhanced Marine Predator Algorithm (EMPA). Firstly, task scheduling model with makespan and resource utilization is constructed. Secondly, each individual represents a result of task scheduling, and the purpose of algorithms is to find the optimal scheduling result, therefore the operator of WOA, nonlinear inertia weight coefficient and golden sine strategy are introduced into Marine Predator Algorithm. In the simulation experiment, EMPA is compared with Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) under different number of tasks in synthetic datasets and GoCJ datasets.The experimental results show that the EMPA algorithm has more advantages in terms of makespan, degree of imbalance, and resource utilization. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) paradigm, by transforming physical devices into innovative items, affects every aspect of our daily lives. It has brought a slew of evolutionary and revolutionary services that were almost impossible to imagine until recently. The numerous services and applications offered by IoT cover numerous fields, such as personal healthcare, urban life, energy management, and manufacturing. A tremendous amount of data is produced to reach valuable information and meet users' needs. In addition, since the number of services and applications is increasing rapidly, an efficient method to fulfill the growing demands in different application domains becomes challenging. To handle the mentioned problems, task scheduling mechanisms have a significant influence. Despite the importance of these methods in the IoT, an in-depth and systematic review of the current works in this area is clearly lacking. Therefore, we aim to overcome this gap by adopting an organized manner. In fact, this paper aims to specify the challenging problems in IoT task scheduling, highlight the effective works, and outline some hints for upcoming studies. [ABSTRACT FROM AUTHOR]

In the era of big data, the number of science and technology service resources has proliferated, and the integration and clustering of science and technology documents become a challenging issue. This paper proposes a novel meta-heuristic algorithm, banyan tree growth optimization (BTGO), for resource clustering of science and technology services. The proposed algorithm is inspired by the growth process of banyan tree, which periodically uses three operators including rooting, multi-trunk, and adjustment to search the solution space globally according to the growth conditions of different stages. To evaluate the performance of BTGO, 29 CEC17 benchmark functions were first utilized to examine its effectiveness. Moreover, a clustering study on UCI datasets is then presented, which compares the suggested algorithm with seven advanced metaheuristic optimization algorithms. The results of numerical experiments and standard datasets demonstrate the effectiveness and efficiency of BTGO. In clustering optimization problems, BTGO can not only finding the optimal solution efficiently, but also improving the clustering accuracy and NMI significantly. Our method was successfully applied to solve the science and technology text clustering problem and validated on the Hainan Science and Technology Service Experimental Platform. [ABSTRACT FROM AUTHOR]

Image enhancement is an essential step in image analysis and processing as it helps people to recognize and understand images because their perception is greatly influenced by image quality. Incomplete beta function (IBF) is a broadly employed transformation function for image contrast enhancement (ICE). However, IBF has low parameter selection efficiency, a limited range of mutable parameters to stretch areas with high or low gray levels, and image enhancement with stretching at both ends is almost ineffectual. In this paper, a hybrid whale optimization algorithm (WOA) with the Chameleon Swarm algorithm (CSA), referred to as HWOA, is presented to adaptively determine the optimal parameters of IBF for ICE. Then, bilateral gamma correction (BGC) is utilized to produce better contrast and brightness while preserving edge detail. The proposed HWOA algorithm follows a multi-phased process of strategies. Many improvements were made to the mathematical model of WOA followed by its hybridization with CSA for further exploration and exploitation aspects. The proposed HWOA is tested over some standard images along with well-known available Kodak image dataset and assessed using several standard measures. The experimental results showed that the proposed algorithm can satisfactorily surpass many other algorithms that used the same image enhancement approach as well as other traditional image enhancement methods deemed here for comparison. Specifically, the findings on ten color images revealed that the performance of HWOA in terms of average peak signal-to-noise ratio, average structural similarity index, and average values of entropy results are more than 33.0, 96.6%, and 7.3, respectively, and these results are much better than all other comparative methods in the corresponding evaluation criteria. [ABSTRACT FROM AUTHOR]

In Industry 4.0, the focus of task scheduling is more on smart services such as robotic services under the paradigm of mobile edge computing, which are widely used to improve the efficiency of smart manufacturing. Existing scheduling research efforts on optimizing efficiency such as swarm algorithm, reinforcement learning, but with little real-time dynamic scheduling. Online learning and adaptation is a critical function when tackling real challenges in unpredictable and dynamically changing edge computing environments. Our goal is to propose algorithms that can meet the real-time dynamic task scheduling even reach the level of communication. We propose a Deep Reinforcement Learning (DRL)-based online real-time task scheduling method which has the new exploration and exploitation strategy using the improved sparrow search algorithm (ISSA). We replace the traditional strategy with a meta-heuristic algorithm, while introducing natural disturbances and modifying the population position update formula to converge to the optimal position. The algorithm has two layers. The first layer is scheduling generation layer, which is responsible for making decisions, and the second layer is scheduling policy update layer, which optimizes the strategy and finds the best set of hyperparameters. The experimental results show that the running time of the method to solve the problem can reach 0.0092 seconds, and the real-time performance reaches up to the millisecond communication level. At the same time other methods are compared, effectively reducing the running cost and meeting the requirements of production scheduling tasks. [ABSTRACT FROM AUTHOR]

Today, increasing communication of Internet of Things (IoT) devices, wearable sensors, healthcare applications and cloud providers provides power and energy consumption for high processing personal data and healthcare records. Cloud-edge data centers are processing enormous amount of electrical power resulting in high performance computing. One of the important challenges in this problem is Virtual Machine (VM) service placement that attempts to enhance energy management of VMs to cloud-edge service providers dynamically to support Service Level Agreement (SLA) metrics in IoT systems. Service placement is a very important issue because if VMs are not confident in the SLA of their critical data, IoT applications will not want to use existing resources safety. Providing a way to increase the Quality of Service (QoS) factors and energy efficiency of the service placement in the IoT environment is an important and critical issue because the use of IoT devices and wearable sensors increases energy consumption in human life in smart contracts. Therefore, this paper presents a hybrid Genetic algorithm and social spider optimization (GA-SSO) algorithm for an energy-aware service placement model in the IoT to manage data congestion and system safety. After conducting studies and comparisons, the accuracy and superiority of the proposed model were established. Experimental results show that the energy consumption with the proposed GA-SSO algorithm can be reduced 24% and we can achieve to the performance of QoS factors with fitness function 88% with compare to the other meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Edge intelligence has become popular recently since it brings smartness and copes with some shortcomings of conventional technologies such as cloud computing, Internet of Things (IoT), and centralized AI adoptions. However, although utilizing edge intelligence contributes to providing smart systems such as automated driving systems, smart cities, and connected healthcare systems, it is not free from limitations. There exist various challenges in integrating AI and edge computing, one of which is addressed in this paper. Our main focus is to handle the adoption of AI methods on resource-constrained edge devices. In this regard, we introduce the concept of Edge devices as a Service (EdaaS) and propose a quality of service (QoS) and quality of experience (QoE)-aware dynamic and reliable framework for AI subtasks composition. The proposed framework is evaluated utilizing three well-known meta-heuristics in terms of various metrics for a connected healthcare application scenario. The experimental results confirm the applicability of the proposed framework. Moreover, the results reveal that black widow optimization (BWO) can handle the issue more efficiently compared to particle swarm optimization (PSO) and simulated annealing (SA). The overall efficiency of BWO over PSO is 95%, and BWO outperforms SA with 100% efficiency. It means that BWO prevails SA and PSO in all and 95% of the experiments, respectively. [ABSTRACT FROM AUTHOR]

Cyber Physical System (CPS) plays an important role in industry 4.0 applications such as smart factories, smart energy, smart transportation, smart buildings, smart healthcare, etc. Similarly, Cyber Physical Sensor System (CPSS) has gained popularity in recent times and is composed of a computing platform linked to an actuator, sensor, and wireless access point. In real-time scenarios, CPSS continuously gathers data from physical objects and conducts real-time control events based on the process algorithm. Then, the gathered data is transferred to the control centre or cloud services via network layer for further processing. In this scenario, there exists a need to identify the way of utilizing the intellect correctly, by designing effective data sensing and fusion schemes for CPSS. With this background, the current paper presents a Deep Learning with Metaheuristics based Data Sensing and Encoding (DLMB-DSE) scheme for CPSS. The aim of the proposed DLMB-DSE technique is to present a prediction-based data sensing and fusion approach to reduce the quantity of data communication and maintain maximum coverage by ensuring security. DLMB-DSE technique involves the design of Optimal Deep Belief Network (DBN) with Adagrad optimizer to primarily predict the data of the succeeding period with minimum number of data items. It also helps in making the primary predicted value, estimate the actual value, with maximum accuracy. Besides, Multi-Key Homomorphic Encryption (MKHE) technique is also applied for useful data encoding and decoding processes, thereby accomplishing security. Moreover, the novelty of the study lies in optimal key generation process, followed in MKHE technique, using Equilibrium Optimizer (EO). This helps in improving the security. A wide range of experiments was implemented to validate the better performance of the proposed DLMB-DSE technique. The experimental results exhibit the promising performance of DLMB-DSE approach over other methods under different measures. [ABSTRACT FROM AUTHOR]

Cloud computing is the new technology offering services to build new application through virtualization. Virtualization improves the usage of resource utilization in cloud environment. Recently research in Task Scheduling problem has received more attention because the customers want to maximize the utilization of resources in a cheaper way. In this paper an enhanced particle swarm optimization (PSO) algorithm for improving the efficiency in the task scheduling has been proposed. A ranging function and tuning function based PSO (RTPSO) based on data locality is introduced in this paper for solving the inertia weight assignment problem in existing PSO algorithm for task scheduling. The large inertia weight and small inertia weight will assist a global search and local search respectively. In addition, we have combined the RTPSO with Bat algorithm (RTPSO-B) to improve the optimization. Cloudsim is used in this paper to simulate the task scheduling in cloud environment. The proposed RTPSO-B based task scheduling is compared with various existing task scheduling algorithms such as GA, ACO, ordinary PSO. Simulation results proved proposed RTPSO-B based task scheduling method reduces makespan, cost and increases the utilization of resources. [ABSTRACT FROM AUTHOR]

This paper introduces and tests a novel machine learning approach to detect Android malware. The proposed approach is composed of Support Vector Machine (SVM) classifier and Harris Hawks Optimization (HHO) algorithm. More specifically, the role of HHO algorithm is to optimize SVM classifier hyperparameters while the SVM performs the classification of malware based on the best-chosen model, as well as producing the optimal solution for weighting the features. The effectiveness of the proposed approach and the ability to increase detection performance are demonstrated by scientific testing using CICMalAnal2017 sampled datasets. We test our method and its robustness on five sampled datasets and achieved the best results in most datasets and measures when compared with other approaches. We also illustrate the ability of the proposed approach to measure the significance of each feature. In addition, we provide deep analysis of possible relationships between weighted features and the type of malware attack. The results show that the proposed approach outperforms the other metaheuristic algorithms and state-of-art classifiers. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) defines the network of physical objects, commonly used to interconnect and communicate with other devices through the internet. Security is highly essential in IoT based communication owing to the massive heterogeneity of devices involved in the network. The interlinked systems in IoT, requires the trusted model for assuring security, authenticity, authorization and confidentiality of interconnected things, irrespective of the functionalities. Considering the challenges in the provision of security in the IoT network, this paper proposes a new chaotic bumble bees mating optimization (CBBMO) algorithm for secure data transmission with trust sensing model, called CBBMOR-TSM model. The BBMO is stimulated by the mating nature of a swarm of bumble bees. To improve the convergence rate of the BBMO technique, the CBBMO model is defined by the integration of chaotic concept into the classical BBMO technique. The aim of the proposed model is to design a trust sensing model and perform secure routing using the CBMO algorithm. The proposed model initially designs a trust sensing model by incorporating indirect and direct trusts that are utilized to determine the trust values of the IoT nodes and thereby the malicious node can be identified. In addition, the secure routing process is invoked using the CMBO algorithm by using the trust sensing model to determine an optimal and secure path for data transmission. To examine the superior performance of the presented method, an extensive set of experiments are performed and the results are investigated in terms of different measures. The CBBMOR-TSM model has attained a higher average PDR of 0.931 and lower average PLR of 0.069 whereas the TRM_IOT, OSEAP_IOT and MCTAR-IOT methods have achieved a maximum PLR of 0.219, 0.161 and 0.110 respectively. [ABSTRACT FROM AUTHOR]

Generally, ideal manufacturing system environments are assumed before determining effective scheduling. However, the original schedule is no longer optimal or even to be infeasible due to many uncertain events. This paper investigates a multi-objective inverse scheduling problem in single-machine shop system with due-dates and uncertain processing parameters. Moreover, in order to more close the addressed problem into the situations encountered in real world, the processing parameters are considered to be uncertain stochastic parameters. First, a comprehensive mathematical model for multi-objective single-machine inverse scheduling problem (MSMISP) is addressed. Second, an effective hybrid multi-objective evolutionary algorithm (HMNL) is proposed to handle uncertain processing parameters (uncertainties) and multiple objectives at the same time. In HMNL, using an effective decimal system encoding scheme and genetic operators, the non-dominated sorting based on NSGA-II is adapted for the MSMISP. In addition, hybrid HMNL are proposed by incorporating an adaptive local search scheme into the well-known NSGA-II, where applies a separate local search process, total six strategies, to improve quality of solutions. Furthermore, an on-demand layered strategy is embedded into the elitism strategy to keep the population diversity. Afterwards, an external archive set is dynamically updated, where a non-dominated solution is selected to participate in the creation of the new population. Finally, 36 public problem instances with different scales and statistical performance comparisons are provided for the HMNL algorithm. This paper is the first to propose a mathematical model and develop a hybrid MOEA algorithm to solve MSMISP in inverse scheduling domain. [ABSTRACT FROM AUTHOR]

The remarkable growth of cloud computing applications has caused many data centers to encounter unprecedented power consumption and heat generation. Cloud providers share their computational infrastructure through virtualization technology. The scheduler component decides which physical machine hosts the requested virtual machine. This process is virtual machine placement (VMP) which, affects the power distribution, and thereby the energy consumption of the data centers. Due to the heterogeneity and multidimensionality of resources, this task is not trivial, and many studies have tried to address this problem using different methods. However, the majority of such studies fail to consider the cooling energy, which accounts for almost 30% of the energy consumption in a data center. In this paper, we propose a metaheuristic approach based on the binary version of gravitational search algorithm to simultaneously minimize the computational and cooling energy in the VMP problem. In addition, we suggest a self-adaptive mechanism based on fuzzy logic to control the behavior of the algorithms in terms of exploitation and exploration. The simulation results illustrate that the proposed algorithm reduced energy consumption by 26% in the PlanetLab Dataset and 30% in the Google cluster dataset relative to the average of compared algorithms. The results also indicate that the proposed algorithm provides a much more thermally reliable operation. [ABSTRACT FROM AUTHOR]

Clustering is a technique which is used to group the data into different subgroups or subsets to retrieve meaningful information from the available huge dataset. The trending swarm based intelligent system replaces the conventional clustering algorithm with the objective of increased performance. Ant lion optimization (ALO) technique is the swarm based intelligence that exhibits the hunting mechanism of the ant lions in the natural environment. Ant colony optimization (ACO) algorithm is a swarm based intelligence technique which inherits the behaviour of natural ant. In this paper new hybrid ACO–ALO algorithm was proposed to solve the data clustering problem. Additionally Cauchy's mutation operator is added with this proposed algorithm to avoid the local minima trapping problem. The main objective is to reduce the intra cluster distance in clustering problem. From the experimental analysis, it evidences the proposed ACO–ALO algorithm outperforms the traditional algorithms of data clustering. [ABSTRACT FROM AUTHOR]

Due to the limitations associated with the processing capability of mobile devices in cloud environments, various tasks are offloaded to the cloud server. This has led to an increase in the efficiency of mobile applications in the two decades since the advent of the cloud paradigm. However, task offloading may not be a suitable option for delay-sensitive mobile applications because the cloud server is usually located remotely from mobile users. To overcome this problem, fog computing, also known as "Cloud at the Edge", has been introduced as a complementary solution. On the other hand, although fog computing brings computing and radio resources closer to mobile devices, fog nodes cannot adequately meet users' needs due to limited computing resources. To minimize delays in responding to mobile users' requests, it is necessary to establish a trade-off between local execution of requests on end-devices and the fog environment. In this paper, we present task offloading in the form of a multi-objective optimization problem with a focus on reducing both total power consumption of the system and the delay in executing tasks. Then, considering the NP-hardness of the problem, we solve it using two meta-heuristic methods, namely the non-dominated sorting genetic algorithm (NSGA-II) and the Bees algorithm. The simulation results supported the robustness of both meta-heuristic algorithms in terms of energy consumption and delay reduction. The proposed methods achieve a better tradeoff concerning both offloading probability and the power required for data transmission. [ABSTRACT FROM AUTHOR]

In a cloud environment, scheduling problem as an NP-complete problem can be solved using various metaheuristic algorithms. The metaheuristic algorithms are very popular for scheduling tasks because of their effectiveness. A bacterial foraging is a swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. This paper proposes a task scheduling algorithm based on bacterial foraging optimization to reduce the idle time of virtual machines whereas the load balancing and reducing of runtime have occurred. The Cloudsim toolkit has assessed the performance of the proposed method in comparison with some scheduling algorithms. According to the obtained results, the makespan and energy consumption were reduced by using the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we consider the contention aware task scheduling problem on a grid topology of processors. By contention awareness, we mean that simultaneous communication on a link has to be serialized. To solve this problem, we propose several nature inspired metaheuristic algorithms: Simulated Annealing (SA), Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Bat Algorithm (BA), Cuckoo Search (CS), and Firefly Algorithm (FA). We perform benchmark evaluation of these algorithms for the Normalized Schedule Length (NSL) parameter. The benchmark task graphs that we consider are: random task graphs, peer set task graphs, systolic array task graphs, Gaussian elimination task graphs, divide and conquer task graphs, and fast Fourier transform task graphs. [ABSTRACT FROM AUTHOR]

Schizophrenia is a chronic mental illness that can negatively affect emotions, thoughts, social interaction, motor behavior, attention, and perception. Early diagnosis is still challenging and is based on the disease's symptoms. However, electroencephalography (EEG) signals yield incredibly detailed information about the activities and functions of the brain. In this study, a hybrid algorithm approach is proposed to improve the search performance of the marine predator algorithm (MPA) based on chaotic maps. For evaluating the performance of the proposed chaotic-based marine predator algorithm (CMPA), benchmark datasets are used. The results of the suggested variation method on the benchmarks show that the Sine Chaotic-based MPA (SCMPA) significantly outperforms the other MPA variants. The algorithm was verified using a public dataset consisting of 14 subjects. Moreover, the proposed SCMPA is essential for EEG electrode selection because it minimizes model complexity and selects the best representative features for providing optimal solutions. The extracted features for each subject were used in the decision tree (DT), random forest (RF), and extra tree (ET) methods. Performance measures showed that the proposed model was successful at differentiating schizophrenia patients (SZ) from healthy controls (HC). In the end, it was demonstrated that the feature selection technique SCMPA, which is the subject of this research, performs significantly better in regard to classification using EEG signals. [ABSTRACT FROM AUTHOR]

Software defect prediction is a critical challenge within software engineering aimed at enhancing software quality by proactively identifying potential defects. This approach involves selecting defect-prone modules ahead of the testing phase, thereby reducing testing time and costs. Machine learning methods provide developers with valuable models for categorising faulty software modules. However, the challenge arises from the numerous elements present in the training dataset, which frequently reduce the accuracy and precision of classification. Addressing this, selecting effective features for classification from the dataset becomes an NP-hard problem, often tackled using metaheuristic algorithms. This study introduces a novel approach, the Binary Chaos-based Olympiad Optimisation Algorithm, specifically designed to select the most impactful features from the training dataset. By selecting these influential features for classification, the precision and accuracy of software module classifiers can be notably improved. The study's primary contributions involve devising a binary variant of the chaos-based Olympiad optimisation algorithm to meticulously select effective features and construct an efficient classification model for identifying faulty software modules. Five real-world and standard datasets were utilised across both the training and testing phases of the classifier to evaluate the proposed method's effectiveness. The findings highlight that among the 21 features within the training datasets, specific metrics such as basic complexity, the sum of operators and operands, lines of code, quantity of lines containing code and comments, and the sum of operands have the most significant influence on software defect prediction. This research underscores the combined effectiveness of the proposed method and machine learning algorithms, significantly boosting accuracy (91.13%), precision (92.74%), recall (97.61%), and F1 score (94.26%) in software defect prediction. [ABSTRACT FROM AUTHOR]

Population-based meta-heuristic optimization algorithms play a vital role in addressing optimization problems. Nowadays, exponential distribution optimizer (EDO) can be considered to be one of the most recent among these algorithms. Although it has achieved many promising results, it has a set of shortcomings, for example, the decelerated convergence, and provides local optima solution as it cannot escape from local regions in addition to imbalance between diversification and intensification. Therefore, in this study, an enhanced variant of EDO called mEDO was proposed to address these shortcomings by combining two efficient search mechanisms named orthogonal learning (OL) and local escaping operator (LEO). In mEDO, the LEO has been exploited to escape local optima and improve the convergence behavior of the EDO by employing random operators to maximize the search process and to effectively discover the globally optima solution. Then the OL has been combined to keep the two phases (i.e., exploration and exploitation) balanced. To validate the effectiveness and performance of the mEDO algorithm, the proposed method has been evaluated over ten functions of the IEEE CEC'2020 test suite as well as eight real-world applications (engineering design optimization problems), Furthermore we test the applicability of the proposed algorithm by tackling 21 instance of the quadratic assignment problem (QAP). The experimental and statistical results of the proposed algorithm have been compared against seven other common metaheuristic algorithms (MAs), including the basic EDO. The results show the supremacy of the mEDO algorithm over the other algorithms and reveal the applicability and effectiveness of the mEDO algorithm compared to well-established metaheuristic algorithms. The experimental results and different statistical measures revealed the reliability and applicability of the mEDO method in solving the global, engineering design, and combinatorial optimization problems by achieving a reasonable solution in terms of scoring a global optima solutions and avoiding premature convergence by increasing the population's diversity. [ABSTRACT FROM AUTHOR]

Wireless rechargeable sensor networks (WRSNs) are a popular and promising field of research that can be used in many different fields. The battery life and storage space of the sensors are limited, due to this it is hard to keep the network up for longer. Combining wireless energy transfer and wireless data gathering devices on a Mobile Vehicle (MV) is one solution to this challenge. The objective of this work is to reduce the number of dead sensors and packet delivery delay. We proposed the circle-covering based algorithm to determine sojourn point based on energy consumption rates and the location of sensors. An Improved Grey Wolf Optimization (IGWO) meta-heuristic algorithm partitions the network into the minimum number of regions, assigns a MV to each region, and ensures balanced sub-tour lengths among the regions. A novel weight function is proposed to determine the order of the sojourn points. To accomplish our objectives, we propose a heuristic partial charging and data gathering strategy to determine the sojourn times of the MVs at the sojourn points. The performance of our proposed PCDGS scheme is compared with IMPSS, PMCDC, MOAC and JERDC schemes. The simulation results show that our proposed PCDGS scheme outperforms the others. [ABSTRACT FROM AUTHOR]

The number of cloud-, edge-, and Internet of Things (IoT)-based applications that produce sensitive and personal data has rapidly increased in recent years. The IoT is a new model that integrates physical objects and the Internet and has become one of the principal technological evolutions of computing. Cloud computing is a paradigm for centralized computing that gathers resources in one place and makes them available to consumers via the Internet. Despite the vast array of resources that cloud computing offers, real-time mobile applications might not find it acceptable because it is typically located far from users. However, in applications where low latency and high dependability are required, edge computing—which disperses resources to the network edge—is becoming more and more popular. Though it has less processing power than traditional cloud computing, edge computing offers resources in a decentralized way that can react to customers' needs more quickly. There has been a sharp increase in attackers stealing data from these applications since the data is so sensitive. Thus, a powerful Intrusion Detection System (IDS) that can identify intruders is required. IDS are essential for the cybersecurity of the IoT, cloud, and edge architectures. Investigators have mostly embraced the use of deep learning algorithms as a means of protecting the IoT environment. However, these techniques have some issues with computational complexity, long processing times, and poor precision. Feature selection approaches can be utilized to overcome these problems. Optimization methods, including bio-inspired algorithms, are applied as feature selection approaches to enhance the classification accuracy of IDS systems. Based on the cited sources, it appears that no study has looked into these difficulties in depth. This research thoroughly analyzes the current literature on intrusion detection and using nature-inspired algorithms to safeguard IoT and cloud/edge settings. This article examines pertinent analyses and surveys on the aforementioned subjects, dangers, and outlooks. It also examines many frequently used algorithms in the development of IDSs used in IoT security. The findings demonstrate their efficiency in addressing IoT and cloud/edge ecosystem security issues. Moreover, it has been shown that the methods put out in the literature might improve IDS security and dependability in terms of precision and execution speed. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms, due to their high search speed and strong generalization ability, are frequently applied in programs mainly to discover the corresponding optimal strategy for any problem in view of their defined rules. After years of collision evolution, they have been continuously used to solve the complex, unordered and diverse optimization problems and improve efficiency. Aiming at the problems of low convergence accuracy and easy to fall into local optima of the traditional Harris hawks optimization algorithm, a compound improved Harris Hawks Optimization algorithm (CIHHO) is proposed. Firstly, the early circling exploration and later attack exploitation phase of the dynamic adjustment algorithm for environmental factors is introduced to regulate the energy of Harris hawks; Secondly, the concept of Versoria function is introduced to modify the random jump strength and raise the data grabbing ability of local space; Introducing the Levy flight function to adjust the factor and reduce the disturbance impact of Levy flight is beneficial for getting rid of the local space after entering the exploitation phase, and introducing random white noise to reduce step size and improve algorithm accuracy. Taking CEC 2017 test function suite set as the core, the performance of CIHHO algorithm is analyzed. Firstly, the performance of CIHHO algorithm is compared with HHO, HHO_JOS, LHHO, LMHHO and NCHHO. Secondly, the performance of unimodal function, multimodal function, mixed function and compound function is compared with other 7 improved algorithms. Finally, ablation experiments are carried out. The convergence value of the iterative curve obtained is more quantitative than the improved algorithm, The generality of the improved CIHHO algorithm in solving multiple optimization problems with different dimensions is verified. Further applying the CIHHO algorithm to three different engineering experiments, the minimum cost calculation results directly demonstrate that the CIHHO algorithm obtained has certain advantages in dealing with search space problems. [ABSTRACT FROM AUTHOR]

Honey Badger algorithm (HBA) is an intelligent adaptive meta-heuristic optimization algorithm with few parameters, fast convergence and good convergence accuracy for single-objective problems. However, many real-world optimization problems involve multiple conflicting objectives that need to be optimized simultaneously. A new multi-objective Honey Badger algorithm is proposed to solve the combined economic and environmental power scheduling problem. The proposed MOHBA combines the HBA with the Pareto dominance principle to produce a non-dominated solution. It uses an external elite storage mechanism with congested distance ordering to maintain the diversity of the distribution during the evolution of the Pareto optimal solutions. Furthermore, a fuzzy decision strategy is used to select the best compromise solution from the obtained Pareto bound. Then, to validate the performance of the proposed MOHBA, 20 different benchmark test functions are used to test it against other multi-objective optimization techniques. Moreover, the method is implemented on the multi-objective CEEPD problem for the IEEE 30-bus 6 generator and IEEE 118-bus 14 generator systems. Various objective function s in a multi-objective optimization space is confirmed by comparative studies with minimization schemes and fuzzy decision strategies are utilized to achieve the best scheduling solution for energy and emissions savings. The predominance of the algorithm and its potentiality to handle CEEPD problem several other algorithms. [ABSTRACT FROM AUTHOR]

Various popular multiplayer battle royale games share a lot of common elements. Drawing from our observations, we summarized these shared characteristics and subsequently proposed a novel heuristic algorithm named multiplayer battle game-inspired optimizer (MBGO). The proposed MBGO streamlines mainstream multiplayer battle royale games into two discrete phases: movement and battle. Specifically, the movement phase incorporates the principles of commonly encountered "safe zones" to incentivize participants to relocate to areas with a higher survival potential. The battle phase simulates a range of strategies players adopt in various situations to enhance the diversity of the population. To evaluate and analyze the performance of the proposed MBGO, we executed it alongside ten other algorithms, including three classics and five latest ones, across multiple diverse dimensions within the CEC2017 and CEC2020 benchmark functions. In addition, we employed several industrial design problems to evaluate the scalability and practicality of the proposed MBGO. The statistical analysis results reveal that the novel MBGO demonstrates significant competitiveness, excelling in convergence speed and achieving high levels of convergence accuracy across both benchmark functions and real-world problems. [ABSTRACT FROM AUTHOR]