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Original lidar return signals are covered by high levels of noise that seriously affect the accuracy of subsequent data processing and inversion. Therefore, it is important to separate the effective signal from the returned signal with noise interference. In this paper, an efficient denoising method based on the variational mode decomposition (VMD) algorithm optimized using the global search strategy-based whale algorithm and the total variational stationary wavelet transform (GSWOA-VMD-SWTTV) is proposed, and this method is applied to denoising of lidar return signals. First, the global search strategy-based whale optimization algorithm (GSWOA) is used to acquire the optimal parameters of the VMD algorithm adaptively, and the lidar return signal is then decomposed by global search strategy-based whale optimization algorithm (GSWOA)-VMD. The effective modal components are then determined using the cross-correlation coefficient method from the decomposed modal components, and total variation stationary wavelet denoising is performed on each effective mode. Finally, the effective modes are reconstructed to obtain a clean lidar return signal. Moreover, to provide further verification of the effectiveness of the proposed method, it is compared with the ensemble empirical mode decomposition (EEMD) method, the complete EEMD with adaptive noise (CEEMDAN) method, the singular value decomposition (SVD) method, and the wavelet threshold method under sunny, cloudy, and dusty weather conditions. The experimental results demonstrate the superior noise reduction performance of the proposed algorithm, which can filter out strong noise from the signal while retaining the complete signal details without distortion; additionally, the proposed method has the highest signal-to-noise ratio and lowest mean square error. [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]

Bu çalışmada ilk olarak, betonarme sürekli kirişlerin detaylı tasarımlarının minimum maliyetle yapılabileceği bir süreç tasarlanmıştır. Ardından bu problem üzerinde FDB-TLABC, TLABC, TLBO ve ABC algoritmalarının performansları değerlendirilmiştir. Bu amaçlarla öncelikle Türk betonarme standardı ve deprem yönetmeliği dikkate alınarak optimizasyon problemi oluşturulmuştur. Bir, iki ve üç açıklıklı kiriş örneklerinden oluşan bir problem takımı hazırlanmıştır. Bu problem takımı üzerinde yapılan testlerden algoritmaların optimum sonuca ulaşma performansları ile belirlenen makul çözümlere ulaşma süreleri ve başarıları belirlenmiştir. Optimum sonuca ulaşmada, TLABC ve FDB-TLABC algoritmalarının en başarılı algoritmalar olduğu görülmüştür. Kararlılık analizinden, TLABC algoritmasının en yüksek kararlılığa ve hıza sahip olduğu görülmüştür.

Abstract Concrete-filled steel tube columns (CFSTCs) are important elements in the construction sector and predictive analysis of their behavior is essential. Recent works have revealed the potential of metaheuristic-assisted approximators for this purpose. The main idea of this paper, therefore, is to introduce a novel integrative model for appraising the axial compression capacity (P u ) of CFSTCs. The proposed model represents an artificial neural network (ANN) supervised by satin bowerbird optimizer (SBO). In other words, this metaheuristic algorithm trains the ANN optimally to find the best contribution of input parameters to the P u . In this sense, column length and the compressive strength of concrete, as well as the characteristics of the steel tube (i.e., diameter, thickness, yield stress, and ultimate stress), are considered input data. The prediction results are compared to five ANNs supervised by backtracking search algorithm (BSA), earthworm optimization algorithm (EWA), social spider algorithm (SOSA), salp swarm algorithm (SSA), and wind-driven optimization. Evaluating various accuracy indicators showed that the proposed model surpassed all of them in both learning and reproducing the P u pattern. The obtained values of mean absolute percentage error of the SBO-ANN was 2.3082% versus 4.3821%, 17.4724%, 15.7898%, 4.2317%, and 3.6884% for the BSA-ANN, EWA-ANN, SOSA-ANN, SSA-ANN and WDA-ANN, respectively. The higher accuracy of the SBO-ANN against several hybrid models from earlier literature was also deduced. Moreover, the outcomes of principal component analysis on the dataset showed that the yield stress, diameter, and ultimate stress of the steel tube are the three most important factors in P u prediction. A predictive formula is finally derived from the optimized SBO-ANN by extracting and organizing the weights and biases of the ANN. Owing to the accurate estimation shown by this model, the derived formula can reliably predict the P u of concrete-filled steel tube columns.

Abstract Analyzing the stability of footings is a significant step in civil/geotechnical engineering projects. In this work, two novel predictive tools are suggested based on an artificial neural network (ANN) to analyze the bearing capacity of a footing installed on a two-layered soil mass. To this end, backtracking search algorithm (BSA) and equilibrium optimizer (EO) are employed to train the ANN for approximating the stability value (SV) of the system. After executing a set of finite element analyses, the settlement values lower/higher than 5 cm are considered to indicate the stability/failure of the system. The results demonstrated the efficiency of these algorithms in fulfilling the assigned task. In detail, the training error of the ANN (in terms of root mean square error—RMSE)) dropped from 0.3585 to 0.3165 (11.72%) and 0.2959 (17.46%) by applying the BSA and EO, respectively. Moreover, the prediction accuracy of the ANN climbed from 93.7 to 94.3% and 94.1% (in terms of area under the receiving operating characteristics curve—AUROC). A comparison between the elite complexities of these algorithms showed that the EO enjoys a larger accuracy, while BSA is a more time-effective optimizer. Lastly, an explicit mathematical formula is derived from the EO-ANN model to be conveniently used in predicting the SV.

Due to environmental concerns associated with conventional energy production, the use of renewable energy sources (RES) has rapidly increased in power systems worldwide, with photovoltaic (PV) and wind turbine (WT) technologies being the most frequently integrated. This study proposes a modified Bald Eagle Search Optimization Algorithm (LBES) to enhance the performance of the conventional BES optimizer and optimize the size and location of RES-based Distribution Generation (DG) and Battery Energy Storage Systems (BESS) in distribution systems (DS) to minimize power and energy losses. The modified BES algorithm enhances the exploration phase by utilizing both crossover and mutation techniques with the top three leaders. Moreover, a loss sensitivity factor (LSF) is applied to expedite the solution process by identifying appropriate candidate buses. The variability of solar irradiation and wind speed is modeled using Weibull and Beta probability distribution functions (PDF). To address issues related to high penetration of renewables and demand fluctuations, BESS is used to improve power supply continuity and mitigate fluctuations. The suggested approach is tested on typical 33- and 118-bus systems and compared to alternative methods. The results show significant reduction in energy losses (49.32%, 67.82%, and 64.89% for the 33-bus system and 41.9157%, 60.3766%, and 54.8317% for the 118-bus system) when integrating PV, WT-based DG, and PV + BESS units into the DS. [ABSTRACT FROM AUTHOR]

Medical image segmentation is crucial in using digital images for disease diagnosis, particularly in post-processing tasks such as analysis and disease identification. Segmentation of magnetic resonance imaging (MRI) and computed tomography images pose distinctive challenges attributed to factors such as inadequate illumination during the image acquisition process. Multilevel thresholding is a widely adopted method for image segmentation due to its effectiveness and ease of implementation. However, the primary challenge lies in selecting the optimal set of thresholds to achieve accurate segmentation. While Otsu's between-class variance and Kapur's entropy assist in identifying optimal thresholds, their application to cases requiring more than two thresholds can be computationally intensive. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. In this paper, we introduce an improved chameleon swarm algorithm (ICSA) to address these limitations. ICSA is designed for image segmentation and global optimization tasks, aiming to improve the precision and efficiency of threshold selection in medical image segmentation. ICSA introduces the concept of the "best random mutation strategy" to enhance the search capabilities of the standard chameleon swarm algorithm (CSA). This strategy leverages three distribution functions—Levy, Gaussian, and Cauchy—for mutating search individuals. These diverse distributions contribute to improved solution quality and help prevent premature convergence. We conduct comprehensive experiments using the IEEE CEC'20 complex optimization benchmark test suite to evaluate ICSA's performance. Additionally, we employ ICSA in image segmentation, utilizing Otsu's approach and Kapur's entropy as fitness functions to determine optimal threshold values for a set of MRI images. Comparative analysis reveals that ICSA outperforms well-known metaheuristic algorithms when applied to the CEC'20 test suite and significantly improves image segmentation performance, proving its ability to avoid local optima and overcome the original algorithm's drawbacks. Medical image segmentation is essential for employing digital images for disease diagnosis, particularly for post-processing activities such as analysis and disease identification. Due to poor illumination and other acquisition-related difficulties, radiologists are especially concerned about the optimal segmentation of brain magnetic resonance imaging (MRI). Multilevel thresholding is the most widely used image segmentation method due to its efficacy and simplicity of implementation. The issue, however, is selecting the optimum set of criteria to effectively segment each image. Although methods like Otsu's between-class variance and Kapur's entropy help locate the optimal thresholds, using them for more than two thresholds requires a significant amount of processing resources. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. Due to the aforementioned, we present an improved chameleon swarm algorithm (ICSA) in this paper for image segmentation and global optimization tasks to be able to address these weaknesses. In the ICSA method, the best random mutation strategy has been introduced to improve the searchability of the standard CSA. The best random strategy utilizes three different types of distribution: Levy, Gaussian, and Cauchy to mutate the search individuals. These distributions have different functions, which help enhance the quality of the solutions and avoid premature convergence. Using the IEEE CEC'20 test suite as a recent complex optimization benchmark, a comprehensive set of experiments is carried out in order to evaluate the ICSA method and demonstrate the impact of combining the best random mutation strategy with the original CSA in improving both the performance of the solutions and the rate at which they converge. Furthermore, utilizing the Otsu approach and Kapur's entropy as a fitness function, ICSA is used as an image segmentation method to select the ideal threshold values for segmenting a set of MRI images. Within the experiments, the ICSA findings are compared with well-known metaheuristic algorithms. The comparative findings showed that ICSA performs better than other competitors in solving the CEC'20 test suite and has a significant performance boost in image segmentation. [ABSTRACT FROM AUTHOR]

Reinforced concrete (RC) shear walls play a pivotal role in resisting seismic and lateral loads within structural frameworks. A thorough examination of the existing literature was undertaken, covering a range of experimental and theoretical studies related to the design of RC shear walls. It was emphasized that comprehending shear failure behavior and precisely predicting the shear strength of RC walls holds considerable significance. To address this, the study proposes two models that integrate the support vector regression method with meta-heuristic optimization algorithms (Bat and GOA), utilizing 228 sets of experimental data. In identifying the parameters influencing the shear strength of RC shear walls, the study focused on eight influential factors. The comparison of the two proposed models in the current research with existing models and experimental data demonstrated their commendable accuracy, surpassing the performance of suggested empirical formulations. The prediction errors associated with the proposed models, when compared to experimental data, were notably low. An innovative approach was introduced in the research, presenting a novel method for predicting shear strength using the support vector regression method and the Bat optimization algorithm. A notable advantage of this formulation lies in its capacity to predict the shear strength across various configurations, including squat, cylindrical, and thin RC shear walls. Unlike some existing equations for predicting shear strength, this formulation exhibits no limitations. Through a comparative analysis with established equations, the computational framework's results suggest its successful applicability in building codes and construction practices. The proposed method contributes to the accurate prediction of shear strength in diverse RC shear wall configurations, offering a valuable tool for structural engineering applications. [ABSTRACT FROM AUTHOR]

To enhance residential sustainability by providing decarbonized and locally produced energy to a residential district, the relevance of a grid-connected multi-energy system, including PV panels, a battery, an electrolyzer, a hydrogen tank, and a fuel cell, is studied. A model is developed that creates high-resolution, continuous yearly profiles. The model is then utilized in a metaheuristic multi-objective optimization based on total cost and life cycle greenhouse gas emissions to highlight sizing trends. In the results, the most cost-efficient solutions rely mostly on PV power with minimal battery storage capacity, whereas hydrogen systems are necessary to achieve the best emission reductions. The analysis does not identify a promising prospect for seasonal energy storage with the current tank and battery lifetime emissions. Producing more than 65 % of the energy demand locally is not deemed beneficial. [Display omitted] • Modeling of annual stochastic, high-resolution, and coherent domestic load profiles. • Modeling of a multi-energy system directly coupled with the domestic load model. • Metaheuristic cost and emission optimization of a hydrogen-based multi-energy system. • Seasonal energy storage does not perform well on the life cycle emission side. • Hydrogen systems perform well on the emission side but are not currently cost-effective. [ABSTRACT FROM AUTHOR]

Taking a lot of time to solve optimization problems has become a challenge for metaheuristic algorithms. Due to independence of the metaheuristics components, parallel processing is a good option to reduce the computational time and to find high quality solutions that are close to the optimum with an acceptable cost. One of these metaheuristic algorithms is the Sailfish Optimizer (SFO) which is inspired by a group of hunting sailfish. The SFO algorithm uses a simple method to provide a dynamic balance between exploration and exploitation phases, creates a swarm diversity, avoids local optima, and guarantees high convergence speed. It has been shown that the SFO algorithm outperforms various state-of-art metaheuristic algorithms for multimodal and high dimensional benchmark functions and complicated real-world optimization problems in terms of accuracy and speed by CPU and GPU implementation. In this paper, to speedup this algorithm and increase its performance we propose a reconfigurable hardware version of SFO implemented on Field Programmable Gate Array (FPGA). The FPGA-based SFO can be a very good option in many applications with massive calculations. Due to the inherent parallelism and high computing capabilities of FPGA, the SFO algorithm gains optimum computational time despite the complexity of optimization problems. We have compared the performance of the proposed FPGA-based SFO with its CPU and GPU implementation and some other metaheuristic algorithms. The results show the FPGA implementation is considerably faster than the CPU and GPU implementation. Also, it outperforms other compared FPGA-based metaheuristic algorithms in terms of execution time and convergence speed. [ABSTRACT FROM AUTHOR]

Clustering is a popular data analysis technique that can explore the structure of data through cluster analysis. Similar data are put into the same cluster, while dissimilar data allocate to other clusters. The similarity/dissimilarity among data objects is determined using a distance function. Further, clustering algorithms aim to choose the optimal set of centroids for obtaining better partitioning, but clustering accuracy is always susceptible. This issue of clustering is addressed through meta-heuristic algorithms. This research also aims to handle the accuracy issue and presents a new algorithm for effective cluster analysis. The proposed clustering algorithm is inspired by a water flow optimizer (WFO). The WFO algorithm performance is validated on the well-defined clustering problems based on SSE, accuracy (AR) and detection rate (DR) parameters. The results indicate that the WFO algorithm gets higher clustering results in terms of SSE, AR and DR than the same class of algorithms. The performance is also validated using Friedman statistical test followed by a post hoc test. Results indicated that the proposed WFO gets better statistical results than other clustering algorithms. [ABSTRACT FROM AUTHOR]

Single-solution-based optimization algorithms are computationally cheap yet powerful methods that can be used on various optimization tasks at minimal processing expenses. However, there is a considerable shortage of research in this domain, resulting in only a handful of proposed algorithms over the last four decades. This study proposes the Prism Refraction Search (PRS), a novel, simple yet efficient, single-solution-based metaheuristic algorithm for single-objective real-parameter optimization. PRS is a physics-inspired algorithm modeled on a well-known optimization paradigm in ray optics arising from the refraction of light through a triangular prism. The key novelty lies in its scientifically sound background that is supported by the well-established laws of physical optics. The proposed algorithm is evaluated on several numerical objectives, including 23 classical benchmark functions, the CEC-2017 test suite, and five standard real-world engineering design problems. Further, the results are analyzed using standard statistical tests to prove their significance. Extensive experiments and comparisons with state-of-the-art metaheuristic algorithms in the literature justify the robustness and competitive performance of the PRS algorithm as a lightweight and efficient optimization strategy. [ABSTRACT FROM AUTHOR]

The advent of Internet of Things (IoT) in agriculture has revolutionized the way farmers monitor and manage their crops. IoT-enabled sensors can provide real-time data on various environmental parameters such as temperature, humidity, soil moisture, and crop growth, which can be used to make informed decisions and optimize crop yield. However, the vast amount of data generated by these sensors poses a significant challenge in terms of data processing and communication. To address this challenge, clustering is often used to group the sensors into clusters and elect a cluster head (CH) to communicate with the gateway node. The selection of an appropriate CH and the optimal path for data transmission are critical factors that affect the performance of the IoT system. In this paper, we propose a novel approach to optimize the CH selection and path selection using modified fuzzy logic, Whale optimization algorithm (WOA) and enhanced crow swarm optimization (ECSO). Fuzzy logic is used to evaluate the relevant parameters such as energy, distance, overhead, trust, and node degree to select the most suitable CH. ECSO is then employed to find the optimal path for data transmission based on the selected CH. We evaluate the proposed approach using simulation experiments in a smart agriculture scenario. The results show that the proposed approach outperforms existing approaches in terms of throughput, packet delivery ratio, delay, and energy efficiency. The proposed system demonstrates substantial advantages in terms of throughput, with a remarkable throughput value of 68 Mbps, outperforming the other methods, including EQSR (12.5 Mbps), E2S-DRL (60 Mbps), and OSEAP. The packet delivery ratio (PDR) is also significantly higher for the proposed system, reaching 90.9%. The proposed approach can significantly improve the performance of IoT-enabled smart agriculture systems, leading to better crop yield and higher profitability for farmers. The results of the simulation experiments demonstrate the superiority of the proposed approach over the existing one's throughput, packet delivery ratio (PDR), delay, and energy consumption efficiency is found in the result section. [ABSTRACT FROM AUTHOR]

Machine learning classifiers have been widely used for crop mapping; however, employing numerous features can reduce classifier accuracy. Feature selection is a widely recognized technique that addresses the issue of high dimensionality by choosing the most relevant subset of features with the highest degree of relevance and practicality. Therefore, the optimal combination of feature selection methods and classifiers for high-precision crop mapping remains an open problem. In this study, we introduce a novel hybrid feature selection approach to obtain the optimal subset of features for effective crop mapping. Initially, spectro-temporal remote sensing features from the dataset were ranked using two distinct feature selection techniques: Mutual Information (MI) and ReliefF filter. The most relevant features from each filter-based approach were combined into a union subset. Subsequently, the Gray Wolf Optimization (GWO), a metaheuristic optimization technique, was applied to enhance the feature set generated in the initial step. In the final stage, a random forest classifier leveraged the optimized feature subset for accurate crop type prediction. The effectiveness of the proposed approach was evaluated in Behiera governorate, Egypt. Comparative analysis against existing crop mapping methods demonstrated the superior performance of the proposed approach, achieving an accuracy rate of 82%. Furthermore, this approach showcased a significant reduction in the feature count, streamlining the set from 308 down to only 26 features. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are particularly useful for maximum power point tracking (MPPT) applications, because they can adapt to changes in operating conditions and effectively handle partial shading conditions. However, metaheuristic algorithms also have some limitations that need to be addressed to make them suitable for MPPT applications. The problems associated with metaheuristic algorithm-based MPPT applications include being trapped in local optima, slow convergence speed, shading condition variability, computational complexity and robustness. These problems lead to reduced efficiency in MPPT applications. In the literature, the solution of the aforementioned problems is partially addressed and some of them are solved via an additional irradiation sensor. The motivation of this study is to develop a control algorithm that covers all problems that have been partially solved in the literature and includes an original re-initialization modeling method in accordance with visual programing concept, without using any additional radiation sensor. The proposed control algorithm has the flexibility to be easily adapted to other metaheuristic algorithms and does not require any radiation sensors. The re-initialization model created via Matlab/Simulink and "Embedded Coder Support Package for TI C2000 Processors" allows easy tracking of the global maximum power point (GMPP) by detecting variable radiation conditions. The proposed model was implemented on the Cuckoo Search Algorithm (CSA) and verified through experimental studies carried out with a TI-TMS320f28069 microcontroller and PV emulator. The experimental results confirm that issue 1 is solved with 100%, issue 2 is solved with 99.5%, issue 3 is solved with 99.84%, and issue 4 is solved with 100% MPPT efficiency. [ABSTRACT FROM AUTHOR]

Renewable energy is undergoing significant advancements through the utilization of new technologies. Among these technologies, fixed solar panels serve as a fundamental type of solar photovoltaic energy generator. Monitoring and optimizing the performance of these panels is achieved through the implementation of controller algorithms. However, there is a growing emphasis on axis tracking systems for solar panels, as that has the feasible to extract more power compared to fixed panels. In this context, the integration of the Internet of Things (IoT), big data analytics, deep learning, and cloud computing presents a contemporary solution for addressing various challenges associated with monitoring renewable solar energy systems. By employing sensors and actuators grouped within an IoT framework, real-time data can be extracted and stored in a cloud server, allowing remote access from anywhere. Furthermore, the use of meta-heuristic optimization algorithms, such as the Whale Optimization Algorithm, has become commonplace in solving engineering issues. In this proposed system, the performance of horizontal axis trackers is analyzed and compared with fixed panels using the WOA-based axis tracking analysis. Through the application of a control algorithm, optimal values can be obtained under typical weather conditions. The system's simulation and performance are thoroughly assessed and evaluated. [ABSTRACT FROM AUTHOR]

Summary: This article introduces Crystal Structure Algorithm (CryStAl), a novel metaheuristic optimization algorithm, for fine‐tuning microstrip patch antennas (MPA) in C‐band applications, which utilizes CryStAl with dielectric constant and resonant frequency inputs to optimize MPA dimensions, addressing limited bandwidth and gain issues. Achieves superior antenna performance with 32.71 dB return loss, 5.09 dB gain, and 1.2 VSWR, demonstrating improved convergence speed, radiation pattern, and suitability for diverse C‐band conditions. [ABSTRACT FROM AUTHOR]

The Manta Ray Foraging Optimization Algorithm (MRFO) is a metaheuristic algorithm for solving real-world problems. However, MRFO suffers from slow convergence precision and is easily trapped in a local optimal. Hence, to overcome these deficiencies, this paper proposes an Improved MRFO algorithm (IMRFO) that employs Tent chaotic mapping, the bidirectional search strategy, and the Levy flight strategy. Among these strategies, Tent chaotic mapping distributes the manta ray more uniformly and improves the quality of the initial solution, while the bidirectional search strategy expands the search area. The Levy flight strategy strengthens the algorithm's ability to escape from local optimal. To verify IMRFO's performance, the algorithm is compared with 10 other algorithms on 23 benchmark functions, the CEC2017 and CEC2022 benchmark suites, and five engineering problems, with statistical analysis illustrating the superiority and significance of the difference between IMRFO and other algorithms. The results indicate that the IMRFO outperforms the competitor optimization algorithms. [ABSTRACT FROM AUTHOR]

Precisely locating the voltage sag sources is significant for improving power quality and clarifying the responsibilities of both power supply and consumption. However, the existing location models are complex, computationally intensive, and influenced by the measurement error and fault type, which have low positioning efficiency and large errors. Therefore, this paper proposes a method for locating voltage sag sources based on power polarity and twin characteristics matching. The method firstly uses the polarity of the sequence sag component disturbance reactive power at the monitoring point to determine the sag line. Secondly, the basic realization method of the twin power system is discussed, and the twin characteristics are defined by combining the positive and negative sequence sag component disturbance reactive power and the twin database is established. Thirdly, the spectral norm and Pearson product‐moment correlation coefficient are used to define the data matching degree, and the precise location model of the sag source is established with the objective function of maximizing the data matching between the twin characteristics of the preset and actual sag. Finally, the Improved Whale Optimization Algorithm is used to solve the location model. The correctness and superiority of the proposed method is verified based on IEEE33 node modelling. [ABSTRACT FROM AUTHOR]

This paper suggests a Recalling Enhanced Recurrent Neural Network (RERNN) method for accurately predicting the performance and emissions of spark ignition (SI) engines. The RERNN method is an efficient neural network technique. The RERNN model is also trained using a better conjugate method that speeds up convergence by using a generalized Armijo search technique. The proposed method analyzes combustion phasing, exhaust temperature, engine-out emissions, burn duration, mean effective pressure, ignition lag, and maximum pressure rise rate. By then, the proposed technique is implemented on the MATLAB platform and is evaluated for its performance against existing techniques. The proposed RERNN method achieves a better outcome than other existing Seagull Optimization Algorithm (SOA), Grasshopper Optimization Algorithm (GOA), and Wild Horse Optimizer (WHO) techniques. The proposed method shows the error in carbon monoxide (CO) is 0.18% and carbon dioxide (CO2) is 0.29% at a high speed of 900 rpm compared with other existing approaches. • RERNN method designed for accurately predicting performance in SI engines. • The RERNN method is highlighted as an efficient neural network technique. • The RERNN model is trained using an improved conjugate method. • This approach contributes to a more holistic evaluation of engine performance. • The proposed RERNN method, implemented on the MATLAB platform. [ABSTRACT FROM AUTHOR]

Several real-world scientific and industrial workflow applications adopt elastic and cost-efficient cloud services to fulfill their requirement. There are two stakeholders in the system, namely the user and the provider each of which tries to maximize its profit and at the same time minimize their possible overall costs. Since task scheduling algorithms for workflow executions determine which task should be possibly executed on what resources, they have a drastic impact on both the user's quality of experiences and on the underlying resource utilization. Indeed, an efficient task scheduling algorithm can meet service-level agreement (SLA) for both sides. For the sake of the importance of the issue, this survey presents a subjective taxonomy on the task scheduling schemes in the literature for workflow executions in cloud computing environments to be a guideline for future improvement. It classifies the literature based on the proposed algorithms in the literature, objectives, stakeholders' requirements, and evaluation metrics. This survey highlights research trends, challenges, research gaps, potential solutions, and future direction. It can also pave the way for further processing, improvement and strengthening of existing approaches or devising novel ones for interested researchers in the field of task scheduling problems. [ABSTRACT FROM AUTHOR]

Arithmetic optimization algorithm (AOA) is a meta-heuristic optimization method based on mathematical operators proposed in recent years. Although it has good performance, it can also lead to insufficient local search ability and falling into local optima when solving complex optimization problems. In order to make up for the above shortcomings, the optimization performance of AOA is further improved. This paper proposes a hybrid algorithm based on AOA and particle swarm optimization (PSO) called HAOAPSO. Firstly, a compound opposition-based learning (COBL) strategy is introduced to broaden the scope of finding optimal solutions to help the algorithm better jump out of local optima. Secondly, PSO is combined with AOA that integrates COBL to improve the algorithm's local search ability, so as to improve the overall search efficiency of the algorithm. In addition, experiments are performed on 23 classical benchmark functions with different characteristics and five engineering design optimization problems, and the experimental results of HAOAPSO are compared with those of other well-known optimization algorithms to comprehensively evaluate the performance of the proposed algorithm. The simulation results show that HAOAPSO can provide better solutions in most cases when solving global optimization problems such as engineering, with better convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

Alternative energy sources are needed for a sustainable world due to rapidly increasing energy consumption, fossil fuels, and greenhouse gases worldwide. A hybrid renewable energy system (HRES) must be optimally dimensioned to be responsive to sudden load changes and cost-effective. In this study, the aim is to reduce the carbon emissions of a university campus by generating electricity from a hybrid energy production system with solar panels, wind turbine, a diesel generator, and battery components. On the university campus where the hybrid energy system will be installed, the ambient temperature, solar radiation, wind speed, and load demands have been recorded in our database. Optimization algorithms were used to select the power values of the system components to be installed using these data in an efficient and inexpensive manner according to the ambient conditions. For optimal sizing of HRES components, gray wolf optimizer combined with cuckoo search (GWOCS) technique was investigated using MATLAB/Simulink. In this way, it has been tried to increase their efficiency by combining current optimization techniques. The cornerstone of our optimization efforts for both on-grid and off-grid models pivots on a constellation of critical decision variables: the power harvested from wind turbines, the productivity of solar panels, the capacity of battery storage, and the power contribution of diesel generators. In our pursuit of minimizing the annual cost metric, we employ a tailor-made function, meticulously upholding an array of constraints, such as the quotient of renewable energy and the potential risk of power disruption. A robust energy management system is integral to our design, orchestrating the delicate power flow balance among micro-grid components—vital for satisfying energy demand. Upon analyzing the outcomes of the study, it is apparent that the proposed Scenario 1 HRES effectively utilizes solar and battery components within the off-grid model, surpassing the efficiency of four other hybrid scenarios under consideration. Regarding optimization processes, the off-grid model exhibits superior results with the implementation of the GWOCS algorithm, delivering faster and more reliable solutions relative to other methodologies. Conversely, the optimization of the on-grid model reaches its optimal performance with the application of the cuckoo search algorithm. A comprehensive comparison from both technical and economic view points suggests the on-grid model as the most feasible and suitable choice. Upon completion of the optimization process, the load demand is catered to by a combination of a 2963.827-kW solar panel, a 201.8896-kW battery, and an additional purchase of 821.9 MWh from the grid. Additionally, an energy surplus sale of 1379.8 MWh to the grid culminates in an annual cost of system (ACS) of 475782.8240 USD, a total net present cost of 4815520.2794 USD, and a levelized cost of energy of 0.12754 USD/kWh. Solar panels cover the entire system, and the renewable energy fraction is 100%. [ABSTRACT FROM AUTHOR]