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Lot streaming technology enables continuous overlapping operations, which is of great significance in shortening production cycles, reducing unnecessary waiting time, and increasing production capacity. However, the capacity constraint of batch processing machines may lead to inevitable variations in sublots. Therefore, the key focus of our research is to control variations of sublot for maximising benefits. In view of this, we investigate a hybrid flowshop scheduling problem (HFSP) with batch processing machines and variable sublots (HFSP-BVS) integrating sequence-dependent setup times and transportation times. To address HFSP-BVS, a MILP model is first established, and a novel collaborative iterative greedy (NCIG) algorithm is proposed to optimise the cumulative payoffs associated with delivery dates. In NCIG, a collaborative initialisation method by extracting good information from an archive is proposed, and a specific destruction-reconfiguration strategy is designed to control the variations of sublots in the batch processing stage. Furthermore, a dynamic acceptance criterion is designed to balance the algorithm's exploitation and exploration capabilities. Lastly, we conduct comparisons between the NCIG algorithm and five other metaheuristic algorithms on 100 test instances. The results show that NCIG outperforms them by 1.89% and 61.42% on average in terms of the total penalty and RPI values, respectively. [ABSTRACT FROM AUTHOR]

In most seaports, pilotage is compulsory for incoming and outgoing vessels to use pilotage services. Maritime pilots play a critical role in providing local knowledge and assistance to ensure the safety of vessels and seafarers. Typically, pilots are classified into multiple classes on the basis of their experiences and qualifications. Moreover, pilotage duration is usually stochastic due to various factors, including harsh weather conditions, visibility, and traffic changes. In this study, we address a multi-class pilot dispatching problem with stochastic pilotage duration. We formulate this problem as a stochastic programming model that considers the expected penalty cost for late arrival at each task's location. To solve this problem, we develop a branch-and-price algorithm that integrates column generation within the branch and bound framework. Additionally, we develop a novel labelling algorithm and several effective acceleration techniques to enhance the efficiency of the proposed algorithm. Extensive numerical experiments demonstrate the effectiveness of the proposed algorithm and show that it can handle realistic-sized instances while outperforming a metaheuristic search method. [ABSTRACT FROM AUTHOR]

Changes to components, known as engineering changes (ECs), rarely occur on their own. In fact, in complex assembly systems, most ECs are introduced in batches to ensure that changed components match. As a result, to implement ECs optimally, multiple component's stock must be considered until the change is executed on the EC effectivity date. This problem is known as the EC effectivity date optimisation problem, a variation of the general inventory control problem with deterministic and dynamic demand. As optimisation and monitoring of this problem is computationally expensive, research has suggested to investigate whether metaheuristics can provide adequate support. To fill this research gap, we present the results of a benchmark on basic metaheuristics for EC effectivity date optimisation. To do so, we have compared five common metaheuristics in their basic form (Ant Colony Optimisation, Genetic Algorithm, Particle Swarm Optimisation, Tabu Search, and Simulated Annealing) on a real-world test set. Of the tested algorithms the Genetic Algorithm identified most best solutions and returned good average results for the test cases. However, as its reliability was comparatively low, our research suggests a sequential application of the Genetic Algorithm and Tabu Search. [ABSTRACT FROM AUTHOR]

Human–robot collaborative technology maximises the advantages of the capabilities of humans and robots, and provides diverse operating scenarios for the remanufacturing industry. Accordingly, this paper proposes an innovative human–robot collaborative disassembly line balancing problem (HRC-DLBP). First, a mixed-integer programming (MIP) model is devised for the HRC-DLBP to minimise the number of workstations, smoothness index, and various costs. Second, a hybrid local search genetic algorithm (HLSGA) is developed to solve the proposed HRC-DLBP efficiently. According to the problem characteristics, a four-layer encoding and decoding strategy was constructed. The search mechanism of the local search operator was improved, and its search strategy was adjusted to suit the genetic algorithm structure better. Furthermore, the accuracy of the proposed MIP model and HLSGA is verified through two HRC-DLBP examples. Subsequently, three HRC-DLBP examples are used to prove that the HLSGA is superior to five other excellent algorithms. The case of the two-sided disassembly line problem reported in the literature is also solved using the HLSGA. The results are found to be significantly better than the reported outputs of the improved whale optimisation algorithm. Besides, HLSGA also outperforms the results reported in the literature in solving EOL state-oriented DLBP. Finally, the HLSGA is applied to a power battery disassembly problem, and several optimal allocation schemes are obtained. [ABSTRACT FROM AUTHOR]

This paper studies the customer order scheduling problem in the context of additive manufacturing. The study discusses an integrated problem involving the nesting of parts as well as the scheduling of batches of nested parts onto unrelated parallel machines. A mixed-integer programming model is presented, based on existing formulations from the literature, that integrates different materials and sequence-dependent setup times. Additionally, a metaheuristic based on an iterated local search is proposed for the problem configuration under consideration. Focusing on minimizing the total weighted tardiness of orders, the efficiency of the heuristic approach is evaluated using comprehensive test data. Further, we show the importance of the considered order-related objective by using qualitative analysis. [ABSTRACT FROM AUTHOR]

We study a problem from a real-world application, in which a daily set of orders must be processed following two stages, consisting of preparing perishable products on benches and allocating them to conveyors to be packed in disposable trays. Daily decisions must be made regarding the number and start time of working shifts, the number of workers and their allocation to machines, and the scheduling of orders in a two-stage flexible flow shop environment. The flow shop environment of the studied problem is common in many industries of perishable products, making the problem very general. The problem involves a number of operational constraints, and three objective functions that are minimised in a lexicographic way. To solve the problem, we implement a constructive heuristic and embed it within three metaheuristics: a Random multi-start algorithm (MR), a Biased random key genetic algorithm (BRKGA), and a Variable neighbourhood search (VNS) based one. We perform computational experiments over a set of realistic instances, and present a lower bound obtained from a constraint programming model for the scheduling counterpart. The results of the experiments show that the BRKGA is the most effective in practice for the integrated problem of workforce allocation and scheduling. [ABSTRACT FROM AUTHOR]

In this article, maintenance operations were incorporated to the sequence-dependent setup blocking flow shop to minimise total completion time subject to total maintenance costs. A mixed integer linear programming and procedures to incorporate maintenance to job sequence were developed. A constructive heuristic and three metaheuristics, greedy randomised adaptative search procedure (GRASP), discrete artificial bee colony (DABC), variable block insertion heuristic (VBIH) and iterated greedy algorithm (IG), designed to the blocking flow shop with total completion time minimisation were adapted to minimise total maintenance costs and the hierarchical function, respectively.All the methods were applied to solve small and medium and large size instance sets, with respective 1920 and 2200 problems. Experimental results shows that for small size instances set, DABC with α = 20 (DABC(20)) obtained the best trade-off between effectiveness and efficiency. For medium and large size instances set, DABC(20) VBIH with α = 20 (VBIH(20)) generated the best trade-off between quality of solution and computational time. Considering both instances set together, both DABC(20) and VBIH(20) obtained the best performance between quality of solution and computational time. [ABSTRACT FROM AUTHOR]

This study investigated a multi-objective resequencing scheduling problem in the automotive manufacturing systems due to operational requirements on the color-batching of the paint shop and sequential requirements on the sequence adherence of an assembly shop. Resequencing cars as color-oriented batches reduced the costs of color changes and operational costs for paint shops. Also, assembly shops required paint shops to complete cars with fewer delays so that high sequence adherence with its demand was assured. Based on real-world applications, we investigated two contradictory objectives-color change costs and sequence tardiness-in a single-machine flowshop scheduling environment. A multi-objective-deep-Q-network algorithm was developed to determine the Pareto frontier. Reward shaping was designed to improve the convergence of the neural network. The 2D-folded-normal distribution was designed to sample the preference, which made the exploration and exploitation of the neural network more comprehensive and improved the training efficiency. Two experiments were conducted and showed that the proposed approach outperformed the meta-heuristic algorithm and the envelope Q-learning algorithm in solving time, performance, the convergence of the neural network, and the diversity of the Pareto frontier. Therefore, the proposed approach can be used in automotive paint shops to improve scheduling efficiency and reduce operational costs. [ABSTRACT FROM AUTHOR]

The fleet sizing problem is a critical challenge in closed-loop supply chains because of the additional requirements of managing product returns. To tackle this challenge, this study addresses the integration of fleet sizing with vehicle routing problem in closed-loop supply chain management, considering simultaneous delivery and pickup under demand uncertainty. A mixed-integer linear programming model is proposed to minimize vehicle acquisition and shipment costs, and a multi-stage adjustable robust optimization formulation is employed to handle uncertainty. Prior studies have neglected to address this important problem due to a lack of efficient solving methods. Hence, three exact, heuristic, and metaheuristic algorithms are developed to solve the problem. The numerical experiments demonstrate that the proposed methods outperform the existing Hyper-box based Branch-and-Partition method, improving computational efficiency and solution quality. This study also helps practitioners make better fleet sizing decisions under uncertainty. [ABSTRACT FROM AUTHOR]

The content of heavy metal Cu in soil is an important indicator for assessing soil quality. However, soil hyperspectral data contain a large amount of redundant data, which can affect the effectiveness of spectral modelling. To address this problem, a combined method based on Whale Optimization Algorithm and Successive Projection Algorithm (WOA-SPA) is proposed to select the characteristic bands. And a prediction model of soil heavy metal Cu content was constructed using the partial least squares method. The experimental results show that: (1) The prediction accuracy of soil Cu content using the WOA-PLSR model was higher than that of the SPA-PLSR model, with RPD increased by 0.229, the R2 increased by 0.07, and RMSE decreased by 0.354. (2) Running the WOA and WOA-SPA combination algorithms 30 times respectively to prove the stability of the algorithms, the prediction accuracy of the model constructed based on the WOA-SPA combination algorithm is better than that of the model constructed based on the WOA algorithm as a whole. (3) Compared with the WOA or SPA algorithm alone, the prediction model constructed based on the WOA-SPA algorithm has a better prediction performance, with RPD of 2.259, R2 of 0.804, and RMSE of 2.396 for the prediction set. (4) Comparing the spatial distribution maps of the measured and predicted values, it can be observed that the spatial distribution between the predicted Cu content values based on the combined WOA-SPA algorithm and the measured Cu content values are basically consistent. This study indicates that the WOA-SPA-PLSR model has good stability and prediction accuracy for the prediction of Cu content in soil, which is of great practical significance for the rapid and accurate estimation of heavy metal Cu content in soil. HIGHLIGHTS: A novel dimensionality reduction method based on WOA-SPA was proposed. The WOA-SPA-PLSR model was constructed, which can accurately and quantitatively predict the content of heavy metal Cu in soil. The characteristic band selected by the WOA-SPA combination algorithm accounts for 2.79% of the full band, effectively reducing the dimensionality of soil hyperspectral data. Comparing the spatial distribution maps of measuredsoil Cu content and predicted soil Cu content,it can be found that the spatialdistribution of thetwois basically consistent. This shows that the WOA-SPA-PLSR model can effectively predict the spatialdistribution of soil Cu content in this area. [ABSTRACT FROM AUTHOR]

Affected by the uncertainty of external environmental factors, wind power generation has significant characteristics of randomness and non-stationarity. Accurately predicting wind power is a necessary technical means to improve the efficiency of wind energy utilization and reduce wind abandonment. Integrating large-scale wind power into the grid has put higher requirements for wind power prediction accuracy. Hence, this study proposes a combined prediction model based on wavelet denoising (WD), hybrid enhanced seagull optimization algorithm (HESOA) and support vector machine (SVM) to predict short-term wind power. The WD technology obtains effective signals from original wind power data, providing a reasonable basis for prediction research. The HESOA is proposed by introducing the Tent mapping, individual self-learning factor and Gaussian population strategy into the original seagull optimization algorithm to optimize SVM's prediction ability. The case study results reflect that the prediction method is feasible and universal in suppressing wind power generation volatility in spring and winter. The RMSE of short-term wind power prediction is controlled below 65 kW, and the value of R2 is above 98%. This study improves the accuracy of wind power prediction and helps to strengthen the renewable energy consumption level and reliability of wind power grid-connected generation systems. [ABSTRACT FROM AUTHOR]

Evaluation of petroleum fiscal regimes for alternative selection is a challenging task in the oil and gas industry. The qualitative methods or quantitative methods which are based on static reserve estimates are problematic. A new approach incorporating dynamic reserve estimates and quadrant scale to evaluate fiscal regimes is proposed in this study. In this article, metaheuristic algorithms were applied to self-adaptively learn about the complex nature of reservoir to incorporate the changes in reservoir properties and well behaviors to generate field production profiles for building dynamic economic models. It also utilized the z-score standardization to create attractiveness–competitiveness and attractiveness–progressiveness quadrant scales. The results showed that the scales readily identified fiscal regimes that were at same time attractive, competitive, and progressive from fiscal regimes that were unattractive, noncompetitive, and regressive, and fiscal regimes that were within these two extremes. The relative movement along the scales also revealed how fiscal regimes responded as the profitability levels of the project were improved under each algorithm. The quadrant scale is a simple and easy-to-use method and could be an invaluable standard assessment tool for oil and gas decision and policymakers to improve upon investment considerations. [ABSTRACT FROM AUTHOR]

This article investigates a 27-degree-of-freedom dynamic model of a full-scale railway vehicle. The controlling action is performed by developing a decentralized control structure with five independent Fraction Order Proportional Integral Derivative (FOPID) controllers three types of random track irregularities, vertical profile, lateral alignment, and cross-level. A novel metaheuristic optimization technique named hybrid Particle Swarm Optimization-Gray Wolf Optimization (hybrid PSO-GWO) is proposed to optimize active force for an active suspension system. The simulated results show that the hybrid metaheuristic algorithm outperforms with a significant reduction in vehicle vibration ensuring enhanced ride comfort of the car body. [ABSTRACT FROM AUTHOR]

Stope layout optimisation is an important sub-problem of the generic underground mining optimisation problem that contributes towards optimal ore extraction. It is a growing research area as some surface mines transition from open-pit to underground mining. Several researchers acknowledge the complexity of optimising underground mine layouts because exact methods are limited in generating three-dimensional optimal solutions, hence the growing use of metaheuristic approaches to solve the problem in three dimensions. This paper presents a PRISMA-based bibliometric mapping of stope layout optimisation approaches using R-Biblioshiny and VOSviewer software, their concomitant limitations, and suggests potential future research directions in stope layout optimisation. [ABSTRACT FROM AUTHOR]

In this study, a machine learning-based hybrid optimization method is developed to optimize tuned mass damper considering soil-structure interaction. A total of 200,000 time history analyses of the tuned mass damper-structure-soil system are conducted and the results are used to construct the database of six machine learning models. Three meta-heuristic algorithms are used for optimization. A weighted optimization algorithm is developed based on the three individual optimization algorithms. Compared with three individual optimization algorithms, the weighted optimization algorithm can achieve better optimization results. The robustness of the single ground motion-based optimization applied to other ground motions is also investigated. [ABSTRACT FROM AUTHOR]

Among the most significant non-linear challenges for power network design and smooth functioning of current modern updated power system networks is the optimum power flow (OPF) problem. Importance of the electrical system modeling has recently come to light due to the incremental use of energy from renewable sources in power systems networks. The goal is to use wind, solar and tidal sources to recreate the issue of OPF. In this work, Weibull, Lognormal, and also Gumbel probability distribution functions were applied to simulate the uncertainties of wind, photovoltaic, and tidal energy system. Additionally, by adding test scenarios of unpredictable wind, solar and tidal energy systems involving minimization of the cost function, loss of active power, voltage deviation, and increase stability of voltage. In accordance with the chosen thermal producing units, the solutions were evaluated using different locations using IEEE 30-bus systems of testing that incorporate renewable energy sources. The proposed power system planning problem was solved by multi-objective function where unified power flow controller are utilized as flexible AC transmission systems controllers via recently introduced optimization algorithms and the simulation outcomes of the aforementioned technique have been compared with Multi Objective Adaptive Guided Differential Evolution algorithms. The adaptive improved flower pollination algorithm (AIFPA) is a strong and reliable algorithm that is presented in this work. The AIFPA can efficiently deal with many kinds of high-complexity objective regions in multi-objective optimization situations. Utilizing an IEEE 30-bus test system, the suggested approaches' performance was examined and evaluated for a range of objective functions. The simulation results obtained from the proposed algorithm were effective in finding the optimal solution compared to the results of the meta-heuristic algorithm and were reported in the literature. [ABSTRACT FROM AUTHOR]

A control strategy must be developed to maintain the product composition of the two- or more-mixed-flow reactor series. In this research, a two-mixing tank series (TMTS) was utilized in the laboratory to approach the two-mixed flow reactors' problems. Both tanks are designed to overflow so that the volume remains constant. The salt solution was directly fed to Tank 1, while the water was charged to both tanks as the mixing progressed. The output of Tank-1 flowed to Tank-2 through a rather long pipe, resulting in dead time in Tank-2. The Smith predictor was applied to overcome dead time. The PRC (process reaction curve) method was used to tune the PID (proportional-integral-derivative) parameters of Tank-1 and Tank-2. The novel composition controls, adaptive-PID (APID) and modified metaheuristic adaptive-PID (MMAPID), have been proposed and compared with conventional PID. The mathematical models were rigorously examined through XCOS simulations. The processing system is considered sensitive to changes in input disturbances since the process time constants of both tanks were quite low. The dead time of 0.5 minutes was found in Tank 2. The PID parameters produced by PRC experiments resulted in stable responses to changes in the disturbance system. As shown in the dynamic simulation study, both novel APID and MMAPID give almost the same results; their responses are faster than the conventional PID. Based on the integral absolute error (IAE) results, APID with scheduling PID parameters produced the smallest IAE and outperformed the conventional PID. The results of the study highlight APID and MMAPID potential in PID control applications for composition control in a TMTS. The combined APID-Smith and MMAPID-Smith were able to overcome the dead time well. This study creates opportunities for engineers and professionals to use cutting-edge control technologies, improving automation and chemical process industries. [ABSTRACT FROM AUTHOR]

Cyber security is the most studied in recent years by the association of metaheuristic techniques thanks to their ability to solve optimization problems in a limited time and NP-hard problems. The Early Warning System (EWS) can identify malicious exercises and anomalies in the CPS and provide robust network systems protection. In addition, grouping attacks in EWS is important for defining defense policies. Information security in the CPS architecture is about securing information during data aggregation, large-scale processing and sharing in the network environment, especially low-linkage open networks. Motivated by these considerations, we propose a new security approach based on the Artificial Bee Colony (ABC) metaheuristic algorithm to strengthen the security framework of the CPS architecture against the system and within CPS attacks. In this work, we propose a new Early Warning System approach based on metaheuristics. Based on two fundamental concepts of clustering, coherence and separation, an integrated index was proposed based on Davies-Bouldin (DB) and Silhouette Index (SI). An improved ABC algorithm was proposed based on these indices. After having identified two different types of an objective function, based on the above concepts, and after having tested the algorithm on DBSI, the results of the experiments found are auspicious. [ABSTRACT FROM AUTHOR]

The present study is focused on the investigation and simultaneous optimisation of MQL-turning parameters with the application of the weighted Mayfly algorithm for sustainable machining of Nimonic 75. The nanofluid used for the experimental work is graphene oxide dispersed rice bran oil. The parameters of cutting velocity, feed rate, nozzle angle, and nozzle distance were considered for processing characteristics of force, roughness, and residual stress. The weight in the Mayfly algorithm is determined using a grey relational coefficient (GRC). The weight importance calculated for each response is compared with the different schemes in the algorithm. The result showed that the parameters with these schemes had different optimal values. At optimal factor levels determined using GRC in the Mayfly, the algorithm improved 'F-value = 0.40' compared to factor levels in other methods. Compared to experimental results, the optimised value reduces the surface roughness, cutting force, and residual stress by 30%, 3%, and 10%, respectively. The feed rate, cutting velocity, and nozzle distance are the most vital parameters for optimising the output response with the GRC-Mayfly process. [ABSTRACT FROM AUTHOR]

AbstractThe present research examines the utilization of metaheuristic optimization algorithms to enhance the predictive accuracy of the Extreme Gradient Boosting (XGBoost) machine learning model in predicting the capacity for punching shear of Fiber Reinforced Polymer (FRP) reinforced concrete slabs in the absence of shear reinforcement. A database including 610 experimental results is constructed, and the efficiency of twenty metaheuristic algorithms is assessed in optimizing the hyperparameters of the XGBoost model. The enhanced models undergo evaluation and comparison with existing prediction algorithms, showcasing their better accuracy. The study reveals that the DE, MVO, EOA, and VCS algorithms yield the highest precision among the investigated optimization techniques out of which VCS model performed better. Furthermore, the significance of input parameters on the punching shear capacity is analyzed by implementing the Shapley Additive Explanation (SHAP) method. The findings emphasize the potential of metaheuristic optimization in enhancing the estimating abilities of machine learning models for complex engineering problems and provide valuable insights into the key factors influencing the punching shear strength of concrete slabs reinforced with FRP. [ABSTRACT FROM AUTHOR]

This research presents a comprehensive comparative analysis of optimization techniques for achieving the optimal design of cellular beams. The incorporation of gaps within cellular beams reduces the weight of the beam and increases section height, resulting in the production of lighter and stronger sections. The Taguchi method is employed to fine-tune the parameters of the Grey Wolf Optimizer, enabling the achievement of a robust design. The performance of each algorithm is evaluated through three design examples, facilitating comprehensive comparisons among the seven algorithms. Moreover, the study encompasses the modeling and analysis of optimally designed cellular beams using finite element software. [ABSTRACT FROM AUTHOR]

In this article, a production–assembly scheduling problem is presented in three steps. In the first step, the production operation is performed by dedicated parallel machines; in the second step, the assembly operation is performed by the same parallel machines, and in the third step, the post-assembly operation is performed by one machine, and these three steps exist in parallel factories. This problem is NP hard and cannot be solved in a reasonable time by mathematical models. Therefore, a mixed integer linear programming algorithm is presented for small-sized problems, and a new metaheuristic algorithm is presented for large-sized problems, by combining quantum-behaved particle swarm optimization (QPSO), shortest processing time (SPT) and dominance rules, which is called the hybrid QPSOSPT dominance rules (HQSD) algorithm. Accurate parameter adjustment was achieved by analysis of variance. The HQSD algorithm was shown to obtain better results than the other algorithms. [ABSTRACT FROM AUTHOR]

The minimum multiplex arrangement problem locates each vertex of a graph on an integer point on a line, with each integer point being assigned at most $ \mathfrak {m} $ m vertices. The case with $ \mathfrak {m}=2 $ m = 2 , known as the minimum duplex arrangement (MinDA) problem, is investigated in this study. MinDA is $ \mathscr {N}\!\!\mathscr {P} $ N P -hard and is related to applications such as manufacturing systems design and VLSI layout. A mixed-integer linear programming formulation of MinDA is presented. The capabilities of the proposed formulation are assessed on 63 problem instances with varying densities and sizes up to 25 vertices. Additionally, an examination of the structure of an optimal MinDA solution is conducted. Based on this analysis, an iterated local search (ILS) algorithm is established, and novel strategies are developed to enhance its effectiveness. In contrast to traditional ILS approaches, where the local search phase invariably reaches a local optimum, the local search phase in the proposed algorithm may converge to a point in the vicinity of a local optimum, thereby reducing the computational burden for large-scale instances. Extensive computational experiments employing the ILS algorithm are conducted on instances with sizes n up to 10,240 vertices. [ABSTRACT FROM AUTHOR]

Heart disease is one of the foremost reasons for death globally. Machine learning (ML) can be used to predict heart diseases early, which can help improve patient outcomes. This research proposes a novel machine learning method for predicting heart disease using a combination of Grey Wolf Optimization (GWO) and stacked ensemble techniques. GWO is a metaheuristic algorithm that can be used to optimize the parameters of machine-learning models. The stacked ensemble technique is a combination of multiple machine learning models to improve the overall accuracy of the prediction. The model proposed was evaluated using a dataset of heart patients. The results showed that the model achieved a 93% accuracy, which was significantly higher compared to traditional machine learning methods. The proposed method also had a higher precision of 91%, sensitivity of 95.3%, F1 score of 92.9%, and Matthew coefficient of 0.83, less in Log_Loss 2.87 than the traditional methods. The results of this research suggest that the proposed model is a promising new approach for predicting heart diseases. This method is more accurate and reliable than traditional methods and has the potential to improve patient outcomes. [ABSTRACT FROM AUTHOR]

Advanced brain cancer is the deadliest type with just a few months survival rate. Existing technologies hinder the objective of forecasting cancer. This work aims to fulfil the pressing requirement for timely and precise identification of advanced-stage brain tumours, which are notorious for their markedly reduced life expectancy. It presents an innovative hybrid approach for predicting brain tumours and improves diagnostic capabilities. The Multiple Kernel K-Means Cluster Algorithm (MKKCA) is used to segment brain MRI images effectively, differentiating healthy and tumorous tissues. After segmentation, a hybrid approach with 3D-Convolutional Neural Network (CNN) and U-Net has been utilized for classification. The objective is to effectively and accurately distinguish normal and pathological brain images. To enhance the efficiency, we include the Improved Whale Optimization Algorithm (IWOA), which guarantees accurate and dependable performance via location updates. The methodology demonstrates outstanding precision with 98.5% accuracy rate, 98.56% specificity, 91% sensitivity, 87.45% precision and a recall rate of 96% with the F-Measure at 96.02%. These findings, obtained using MATLAB, demonstrate a substantial performance improvement compared to current approaches. This development not only represents a significant addition to diagnostic imaging but also a crucial role in the prediction and treatment of brain cancers. [ABSTRACT FROM AUTHOR]

Accurately predicting lane changes, a crucial driving activity for preventing accidents and ensuring driver safety, is addressed in this study. An innovative predictive model that integrates game theory for precise lane change intention detection and an optimized Convolutional Neural Network (CNN) for trajectory prediction is proposed in this study. The CNN’s efficiency is enhanced through metaheuristic optimization of both the convolution and fully connected layers using the Whale Optimization Algorithm (WOA). Emphasizing robust data processing, a Wiener filter is applied for pre-processing, and the Cascaded Fuzzy C means (CFCM) technique is employed for segmentation. The resulting Whale Optimization Algorithm-based CNN (WOA-CNN) effectively forecasts the trajectory of lane-changing vehicles. Validation of the proposed approach in Python demonstrates exceptional accuracy, reaching 96.5%. This study showcases the effectiveness of the WOA-CNN model in advancing the prediction accuracy of lane-changing behaviour, contributing to enhanced driver safety and accident prevention. [ABSTRACT FROM AUTHOR]

The United Nations established the 17 Sustainable Development Goals (SDGs) in 2015 to address issues like gender equality, clean water, health, education, and hunger by 2030. Of the 17 SDGs, health and education have an outsized impact on countries’ socioeconomic development, so providing insights into progress on these two goals is crucial. Machine learning can help solve many real-world problems, including working towards the SDGs. This paper proposes using a metaheuristic ensemble of Cat Swarm Optimization algorithms with Feed Forward Extreme Learning Machines, called Sustainable Health And Educational Goal Development (SHEGD) Prediction, to effectively contribute to countries’ economic growth by achieving health and education SDGs through machine learning. The model is assessed using UN SDG datasets and performance metrics like accuracy, precision, recall, specificity, and F1-score. Comparisons to other machine learning models demonstrate this model’s superiority in designing a recommendation system for progressing towards the health and education SDGs. The proposed model outperforms the other approaches, proving its value for an SDG recommendation system design. [ABSTRACT FROM AUTHOR]

In the distribution system, proper placement of distributed generation (DG) is still a very challenging issue for getting their maximum potential benefits. Power system stability should be determined through load modeling, hybrid energy storage system (HESS), and photovoltaics (PV) modeling. This paper proposed the optimal sizing and location of photovoltaic using the Modified Harris Hawk Optimization (MHHO) algorithm. The energy generated from the photovoltaic (PV) is stored in the HESS, where the energy's charging and discharging from the storage unit is framed with a reduced power loss (active and reactive power). The optimal sizing and placement of the PV system will provide better voltage stability. The Modified Harris Hawk Optimization Algorithm (MHHO) frames the system's voltage stability. The Newton-Raphson load flow method is used to analyze the loading in the power system. The IEEE 33 and 69 bus systems are used to analyze the load flow to the consumers, thus analyzing the power system's dynamic performance. The performance and voltage stability of the presented model are compared with the existing optimization techniques, which include the Firefly Algorithm (FA), Crow Search Optimization (CSO), Particle Swarm Optimization (PSO), Analytical PSO (APSO), Improved Cayote Optimization Algorithm (COA) and Whale optimization algorithm (WOA) through the MATLAB/Simulink platform. [ABSTRACT FROM AUTHOR]

Scientific and accurate wind predictions are the basis for exploiting and utilizing wind energy. Combining the VMD and KELM, this research proposes a new hybrid model to capture the pattern of wind speed change, the parameters of which are optimized by the Spotted Hyena Optimizer(SHO) and Seagull Optimization Algorithm(SOA), respectively. The VMD is optimized by the SHO to achieve the purpose of adaptive decomposition of historical wind speed time series. After that, the parameters of the KELM are optimized by the SOA. Then, the decomposed wind speed series is input into the optimized KELM. The output prediction Intrinsic Mode Functions(IMFs) is added up to obtain the short-term wind speed prediction results. The measured wind speeds for four seasons were selected as the case study for the proposed model. The prediction results are compared with the measured wind speed series to verify the accuracy and reliability of the model. Besides, the prediction accuracy and stability of the proposed model are better than traditional prediction models, with stronger performance and lower computational cost. The results of case studies indicate that the proposed model can satisfy the need for actual applications. [ABSTRACT FROM AUTHOR]

This study examines the problem of crew pairing and rostering under COVID-19 conditions. To keep the cockpit crew members safe against COVID-19, the problem is investigated in such a way that each cockpit crew member spends less daily sit time period in the airports and returns to his/her home base at the end of the day to take a rest. This study aims to introduce a Mixed-Integer Linear Programming (MILP) formulation for the problem. In order to solve the problem, three meta-heuristic algorithms including Genetic Algorithm (GA), Firefly Algorithm (FA), and Particle Swarm Optimization (PSO) are applied based on a new chromosome representation. The proposed algorithms could obtain solutions with the least possible number of cockpit crew members to cover the existing flights by considering some rules and regulation related to employing cockpits. Moreover, the findings indicate that the algorithms can provide solutions near the optimal solutions (1.94%, 2.49%, and 2.43% gaps for the GA, FA, and PSO on average, respectively) for the small-scale instances extracted from the data sets. Additionally, the proposed GA can find lower-cost solutions in comparison to the FA and PSO in approximately similar CPU time for problem instances with different sizes. [ABSTRACT FROM AUTHOR]

To reduce the influence of temperature on difluoromethane (R32) gas sensors using non-dispersive infrared (NDIR), a temperature compensation method based on improved whale optimization algorithm (IWOA) and back propagation neural network (BPNN) is proposed. The IWOA that uses tent chaotic mapping to optimize the initial population of the whale optimization algorithm (WOA) and incorporates adaptive weights in the population iterations is used to optimize the weights and thresholds between nodes in each layer of the BPNN. The optimized BPNN is used to train the temperature compensation model. The experimental results show that the back propagation neural network optimized with improved whale optimization algorithm (IWOA-BPNN) reduces the maximum relative error to 1.33%, compared to 12.46 and 11.66% for the traditional BPNN and the back propagation neural network optimized with whale optimization algorithm (WOA-BPNN), respectively. IWOA-BPNN is also compared with previously reported algorithms, and the results show that the algorithm improves the accuracy of the sensor. [ABSTRACT FROM AUTHOR]

Brushless direct current (BLDC) motors are widely used in dynamic applications because of advantages such as high efficiency, wide speed range and low maintenance requirements. The classical Proportional-Integral (PI) control method is generally used for speed control of BLDC motor drivers. Although this method is easy to apply, the determined controller coefficients are generally constant and hence insufficient in dynamic changes. For this reason, methods that respond faster to dynamic changes, such as fuzzy-PI, have been proposed in the literature. Although the rule-based fuzzy controller increases its response ability to dynamic changes, determined rule-based coefficients affects the system performance completely. Therefore, the determination of the rule base values of the fuzzy controller is critical. In this paper, meta-heuristic Cuckoo Optimization Algorithm (COA) is proposed to determine the rule base values of the fuzzy controller for BLDC motor. Additionally, the rule-based table values of the fuzzy controller used for BLDC motor is determined using other meta-heuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Imperialist Competitive Algorithm (ICA), Invasive Weed Optimization (IWO) and the results are compared. Finally, experimental studies for Pittman44 series BLDC motor are also carried out and the results are obtained. [ABSTRACT FROM AUTHOR]

In wind energy systems, it is necessary to have a good knowledge of the used generator and its parameters. Despite the motor manufacturer's specifications, accurately estimating the machine parameter reference values is critical for the perfect control system. In this context, off-line identification tests of a 3 kW permanent magnet synchronous machine were conducted to determine the mechanical and electrical parameters. In addition, this research exploits a meta-heuristic optimization technic dubbed grey wolf optimizer in the on-line parameter's identification field. The estimate method is focused on the parameter's estimation linked to a reference model. The rule is based on the cancelation of the current error among the measured and the associated estimated current. For each measurement, the grey wolf estimator adapts the reference model parameters until the model response and the accurate response be similar. All were implemented using the real-time interface of the DSpace DS1104 Controller card while using the ControlDesk graphical user interface and the Simulink/MATLAB environments. Results from hardware simulation confirm the efficacy and benefits of the suggested strategy. [ABSTRACT FROM AUTHOR]

There is increasing recognition that the order of administration of drugs in drug combination studies can markedly affect the outcome. Similarly, manufactured products are often sequentially produced and the final quality frequently depends on the order of assembly. Order-of-addition designs account for the order of administration of the components, and they are quite prevalent, yet research in this area is quite limited. Because of the large dimension of such optimization problems, analytical approaches are invariably very limited and apply to simple setups only. Numerical approaches are also seriously underdeveloped. To this end, we employ two exemplary nature-inspired metaheuristic algorithms, Differential Evolution (DE) and Particle Swarm Optimization (PSO), to search for efficient order-of-addition designs for two classes of important inferential problems: (a) estimating parameters in an imprecisely specified model, and (b) constructing space-filling designs without specifying a model. We evaluate the capability of DE and PSO to solve the two classes of order-of-addition design problems and compare their performance with other algorithms that have been used to tackle somewhat similar problems. Using different criteria, we demonstrate that DE and PSO clearly outperform current algorithms by a wide margin. containing codes to generate all results in this article are available online. [ABSTRACT FROM AUTHOR]

This study is a first attempt to provide an effective tool for crack prediction in beam-type structures using the peridynamics (PD) theory and metaheuristic-based optimization. The norm of the difference between the response of the tested beam and that of the trial model (analyzed using PD), is minimized to find the position and depth of crack. Model calibration is discussed thoroughly. Five well-known metaheuristics as well as an upgraded charged system search algorithm are considered. Results reveal that the proposed procedure can effectively localize and detect the crack severity, even in noise contaminated cases. [ABSTRACT FROM AUTHOR]

Flexible process planning (FPP) involves selecting and sequencing the requisite operations according to technological requirements, and meanwhile allocating a right machine, a right tool and a right access direction to each selected operation by a given criterion. In this article, the FPP problem is exactly and concisely formulated as linear integer programming models based on the topology of the AND/OR-network under two criteria: production cost minimisation and completion time minimisation. Distinctively, more flexible manufacturing elements and process plan evaluation criteria are considered; more complicated tool and access direction changeover identifications are linearly expressed without the big-M parameter. Compared with the latest mathematical programming models for process planning, the proposed models have lower complexity and better performance. The results from numerous comparative experiments indicate that (i) the number of decision variables of the proposed models reduces approximately by 68% and the number of constraints of the proposed models dramatically reduces by 99%; (ii) within the same running time, the proposed models can exactly solve more benchmark cases than the latest models; and (iii) the solutions obtained by the proposed models are also better than the best ones founded by some state-of-the-art meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a solution method for the multimission selective maintenance problem (SMP) that combines column-generation (CG) and genetic algorithms (GAs). The multimission SMP is an optimisation problem that arises when a system performs a sequence of missions separated by breaks of finite duration. During these finite breaks, only a subset of possible maintenance actions can be performed due to resource limitations. The problem is in deciding what actions to perform during each break duration such that the system meets or exceeds a minimum target reliability for all missions. The resulting optimisation problems are usually modelled as mixed integer nonlinear mathematical programmes, which are hard to solve. They are usually solved using metaheuristics. We propose a solution method based on CG framework in which the subproblems are solved using a GA. By integrating the GA within the classical CG framework, high-quality solutions can be obtained very quickly. The proposed solution method is capable of solving systems composed of both parallel and k-out-of-n:G subsystems. This hybrid CG algorithm is shown to obtain near optimal solutions and outperform other metaheuristic solution methods; it is also shown to be capable of solving large-scale systems composed of many subsystems and hundreds of components in a reasonable amount of time. [ABSTRACT FROM AUTHOR]

A cloud manufacturing (CMfg) system is presented as a novel service- and customer-oriented manufacturing paradigm that integrates the distributed manufacturing enterprises to share their manufacturing capabilities or resources and collaborate as an interconnected system in a dynamic environment. Since the high performance of this system depends on the formation of a suitable group of manufacturing service providers, this paper develops an integrated c onfiguration design and capacity planning problem for the CMfg system by considering the dynamic environment of this system. In this regard, dynamic service providers and dynamic demand are considered as two aspects of the dynamic nature of this system. A multi-period multi-objective mathematical model is proposed by maximising the utilities of all three stakeholders of the system. Moreover, three extensions of a discrete multi-objective grey wolf optimiser (DMOGWO) algorithm are devised to solve the medium- and large-scale instances. A comprehensive computational experiment is conducted to assess the performance of the developed meta-heuristic algorithms. Furthermore, by carrying out a sensitivity analysis, some managerial insight is suggested for the managers. [ABSTRACT FROM AUTHOR]

A disassembly line is an effective disassembly system to recover end-of-life products. In real life, as end-of-life products are subject to varying degrees of wear and tear, task failure may occur in the disassembly process. In this paper, the task failure risks are considered, and an expected profit-based stochastic disassembly line balancing problem is studied. First, a mathematical model is presented to maximise the expected recovering profit with task failures. Then, a hybrid metaheuristic approach is developed to efficiently solve the proposed model, which is integrated with a variable neighbourhood descent method and an artificial bee colony algorithm. Finally, the effectiveness and robustness of the proposed algorithm are verified by three cases, and experiment results show that the solution performance of the proposed approach is superior to the other three existing methods. [ABSTRACT FROM AUTHOR]

Due to the complexity and the magnitude of optimisation models that appeared in sustainable supply chain management (SSCM), the use of meta-heuristic algorithms as competent solution approaches is being increased in recent years. Although a massive number of publications exist around SSCM, no extant paper explicitly investigates the role of meta-heuristics in the sustainable (forward) supply chain. To fill this gap, a literature review is provided on meta-heuristic algorithms applied in SSCM by analyzing 160 rigorously selected papers published by the end of 2020. Our statistical analysis ascertains a considerable growth in the number of papers in recent years and reveals the contribution of 50 journals in forming the extant literature. The results also show that in the current literature the use of hybrid meta-heuristics is overtaking pure meta-heuristics, the genetic algorithm (GA) and the non-dominated sorting GA (NSGA-II) are the most-used single- and multi-objective algorithms, the aspects of sustainability are mostly addressed in connection with product distribution and routing of vehicles as pivotal operations in supply chain management, and last but not least, the economic-environmental category of sustainability has been further noticed by the scholars. Finally, a detailed discussion of findings and recommendations for future research are provided. [ABSTRACT FROM AUTHOR]

Distributed hybrid flow shop scheduling (DHFS) problem has attracted much attention in recent years; however, DHFS with actual processing constraints like assembly is seldom considered and reinforcement learning is hardly embedded into meta-heuristic for DHFS. In this study, a distributed assembly hybrid flow shop scheduling (DAHFS) problem with fabrication, transportation and assembly is considered and a mathematic model is constructed. A new shuffled frog-learning algorithm with Q-learning (QSFLA) is proposed to minimise makespan. A three-string representation is used. A newly defined Q-learning process is embedded into QSFLA to select a search strategy dynamically for memeplex search. It is composed of four actions based on the combination of global search, neighbourhood search and solution acceptance rule, six states depicted by population evaluation on elite solution and diversity, and a newly defined reward function. A number of experiments are conducted. The computational results demonstrate that QSFLA can provide promising results on the considered DAHFS. [ABSTRACT FROM AUTHOR]

Given the significant proportion of the outsourced parts, components, and the complex assembly structure of the automobiles, agriculture machinery and heavy industry equipment, distributed production and flexible assembly are much-needed production scheduling settings to optimise their global supply chains. This research extends the distributed assembly permutation flowshop scheduling problem to account for flexible assembly and sequence-independent setup times (DPFSP_FAST) in a supply chain-like setting. For this purpose, an original mixed-integer linear programming (MILP) formulation to the DPFSP_FAST problem is first investigated. Considering makespan as the optimisation criterion, constructive heuristic and customised metaheuristic algorithms are then proposed to solve this emerging scheduling extension. Through extensive computational experiments, it is shown that the proposed algorithms outperform the existing best-performing algorithms to solve the DPFSP_FAST problem, yielding the best-found solutions in nearly all of the benchmark instances. Narrowing the gap between theory and practice, this study helps integrate the production planning scheduling across the supply chain. [ABSTRACT FROM AUTHOR]

In the resource levelling problem (RLP) under uncertainty, existing studies focus on obtaining an open-loop activity list that is not updated during project execution. In project management practice, it is also necessary to address more situations, such as activity overlaps and resource breakdowns. In this paper, we extend the uncertain RLP by proposing a resource levelling problem with multiple uncertainties (RLP-MU) that simultaneously considers uncertainties in activity durations, activity overlaps and resource availabilities. We formulate the RLP-MU as a Markov decision process model. Aimed at levelling resource usage by dynamically scheduling activities at each decision point based on the observed information, we develop a hybrid open–closed-loop approximate dynamic programming algorithm (HOC-ADP). In the HOC-ADP, we devise a closed-loop rollout policy to approximate the cost-to-go function and use the concept of the average project to avoid time-consuming simulation. A greedy-decoding-based estimation of distributed algorithm is also devised to construct an open-loop policy that is embedded in the HOC-ADP to further improve it. We additionally develop a simulation algorithm to evaluate the resource levelling performance of the HOC-ADP. Computational experiments on a benchmark dataset consisting of 540 problem instances are conducted to analyze the performance of the HOC-ADP, and the impact of various factors on resource levelling are investigated. The comparison experimental results indicate that our HOC-ADP outperforms the state-of-the-art meta-heuristics. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to test the performance of the recently proposed weighted superposition attraction-repulsion algorithms (WSA and WSAR) on unconstrained continuous optimization test problems and constrained optimization problems. WSAR is a successor of weighted superposition attraction algorithm (WSA). WSAR is established upon the superposition principle from physics and mimics attractive and repulsive movements of solution agents (vectors). Differently from the WSA, WSAR also considers repulsive movements with updated solution move equations. WSAR requires very few algorithm-specific parameters to be set and has good convergence and searching capability. Through extensive computational tests on many benchmark problems including CEC’2015 and CEC’2020 performance of the WSAR is compared against WSA and other metaheuristic algorithms. It is statistically shown that the WSAR algorithm is able to produce good and competitive results in comparison to its predecessor WSA and other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

To optimize radial distribution systems, this study suggests the utilization of the Grey Wolf Optimizer (GWO), a hybrid metaheuristic optimization method, combined with the Sine Cosine method (SCA). The primary objective of this work is to enhance the distribution system by determining the most efficient network reconfiguration, sizing, and placement of various distributed energy sources in distribution system. The energy sources considered include capacitors, solar cells, wind turbines, biomass-based distributed generation units, and battery storage units. To achieve this goal, the proposed strategy incorporates the power loss sensitivity technique, which assists in identifying suitable candidate buses and accelerates the resolution process. Moreover, the model considers fluctuations in solar irradiance and wind speed using Weibull and Beta probability distribution functions, compensating for the intermittent nature of renewable energy sources and the variability in demand. To address power fluctuations, voltage surges, significant losses, and inadequate voltage stability challenges, battery energy storage, diesel generators, and dispatchable biomass DGs are employed to mitigate variability and enhance supply continuity. The proposed approach is evaluated and validated by comparing it to existing optimization strategies using IEEE 69-bus and 84-bus RDSs. The results demonstrate that the suggested technique achieves faster convergence to near-optimal solutions. The proposed methodology yields a significant reduction of up to 80% in power losses in the 69-bus system and a 35% reduction in the 84-bus system, signifying higher performance than existing methods. [ABSTRACT FROM AUTHOR]

Melanoma is rare and can shorten a patient's life. It can be cured if found early. Early cost, pain and timing prediction affect patients. Thus, many researchers use Artificial Intelligence (AI) to identify Melanoma skin cancer. However, complicated skin lesions, illumination, and a lot of training data make diagonalising Melanoma skin cancer diagnosis tough. The Spiking Herd Search Learning (SHSL) approach for optimal feature selection-based classification promotes the category to overcome this issue. The method combines a unique hybrid metaheuristic algorithm, Horse herd Optimisation Algorithm (HOA), and Squirrel Search Optimisation (SSO) with deep hybrid learning as Spiking Neural Network (SNN) and Convolutional Neural Network (CNN) classifiers. In preprocessing, a Gaussian filter removes skin lesion noise using Image Processing techniques. The k-means clustering method performs the segmentation and excellent feature extraction techniques locate the lesion exactly. The Horse herd Optimisation Algorithm (HOA) and Squirrel Search Optimisation (SSO) algorithms and techniques are used toimprove Spike Neural Network classification (SNN) accurate features. Finally, using the DermIS Image dataset, the SHSL algorithm is evaluated and compared to other methods. The proposed method detects Melanoma Skin Disease with 98.91% accuracy. [ABSTRACT FROM AUTHOR]

Purpose: This work is dedicated to disk brake rotor optimization using parametric and topological optimizations considering three conflicting objectives: mass, temperature variation, and breaking time. The rotor had explicit equations modeled and the Multi-objective Lichtenberg Algorithm (MOLA), which is executable in Matlab®, performed a parametric optimization to find the general rotor design parameters. Several optimization techniques have been developed last few years, however, the ones that have presented better results are meta-heuristics associated with posteriori decision-making techniques. Thus, in this work, this powerful and recently created multi-objective meta-heuristic was applied. The MOLA found more than 3000 solutions and the TOPSIS was used for decision-making. Then, the rotor was designed in the SolidWorks® 3D software and the ANSYS software was applied to perform topological optimization, where more mass was removed and analysis regarding the work stresses was done. To the best author's knowledge, this is the first work to consider multi-objective parametric and topological optimization for this structure at the same time in the literature. A considerable mass reduction was obtained. It was possible to find a rotor weighing only 164.8 g with the lowest safety factor across the entire rotor equals 2.02. Therefore, a rotor optimized with reliable, lightweight, and that allows a low braking time was found. Also, these results show that the methodology used can be applied in other structures with complex parameterization. [ABSTRACT FROM AUTHOR]

Reservoir operations involve determining the quantity of water to be released or stored from the reservoir at any given time, based on the reservoir's current condition. This research presents optimizing techniques for reservoir operating policies on Ravishankar Sagar reservoir during monsoon season. Metaheuristics algorithms – GA, NSGA II, NSGA III, and Eps MOEA were used to optimize the objective function for maximization of storage along with a different set of constraints in Ravishankarsagar reservoir. Sensitivity analysis on all algorithms was performed to calibrate different evaluation parameters. The results were analyzed to find the effectiveness of soft computing algorithms in real-world problems. The standard deviation of 359.52, 351.93,349.68 and 361.09 and Median of 512.5, 525.36,568.96 and 433.79 was observed in GA, NSGA II, Eps MOEA, and NSGA IIII so all the algorithms performed well and were in close approximation, with the least standard deviation and the most optimum storage in Eps MOEA. [ABSTRACT FROM AUTHOR]

This research introduces innovative approaches to enhance intrusion detection systems (IDSs) by addressing critical challenges in existing methods. Various machine-learning techniques, including nature-inspired metaheuristics, Bayesian algorithms, and swarm intelligence, have been proposed in the past for attribute selection and IDS performance improvement. However, these methods have often fallen short in terms of detection accuracy, detection rate, precision, and F-score. To tackle these issues, the paper presents a novel hybrid feature selection approach combining the Bat metaheuristic algorithm with the Residue Number System (RNS). Initially, the Bat algorithm is utilized to partition training data and eliminate irrelevant attributes. Recognizing the Bat algorithm's slower training and testing times, RNS is incorporated to enhance processing speed. Additionally, principal component analysis (PCA) is employed for feature extraction. In a second phase, RNS is excluded for feature selection, allowing the Bat algorithm to perform this task while PCA handles feature extraction. Subsequently, classification is conducted using naive bayes, and k-Nearest Neighbors. Experimental results demonstrate the remarkable effectiveness of combining RNS with the Bat algorithm, achieving outstanding detection rates, accuracy, and F-scores. Notably, the fusion approach doubles processing speed. The findings are further validated through benchmarking against existing intrusion detection methods, establishing their competitiveness. [ABSTRACT FROM AUTHOR]

The micro-channel fabrication in carbon fiber-reinforced polymer (CFRP) composite through electrical discharge machining (EDM) process followed by the comprehensive study of dimensional deviation in the conceived micro-channels are presented in this work. The factors namely, voltage (V), tool rotational speed (TRS), and feed rate (FR) were considered for assessing their impact on channel width deviation (CWD). In addition to the variance analysis,, two potential metaheuristic optimizers, the Butterfly optimization algorithm (BOA) and the Spotted Hyena optimization algorithm (SHO) were used to identify the optimal operating parameters of the aforementioned process. The BOA revealed that the optimal process variables were FR = 5.99 µm/s, V = 150 volt, and TRS = 577.08 rpm for a CWD = 74.96 µm. In contrary, SHO revealed that FR = 5.99 µm/s, V = 150 volt, TRS = 542.96 rpm, and CWD = 74.53 µm were the corresponding optimal values. Overcuts were observed in the fabricated micro-channels and a detailed illustration of the mechanism of overcut formation was provided. [ABSTRACT FROM AUTHOR]