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Although healthcare and medical technology have advanced significantly over the past few decades, heart disease continues to be a major cause of mortality globally. Electrocardiography (ECG) is one of the most widely used tools for the detection of heart diseases. This study presents a mathematical model based on transfer functions that allows for the exploration and optimization of heart dynamics in Laplace space using a genetic algorithm (GA). The transfer function parameters were fine-tuned using the GA, with clinical ECG records serving as reference signals. The proposed model, which is based on polynomials and delays, approximates a real ECG with a root-mean-square error of 4.7% and an R 2 value of 0.72 . The model achieves the periodic nature of an ECG signal by using a single periodic impulse input. Its simplicity makes it possible to adjust waveform parameters with a predetermined understanding of their effects, which can be used to generate both arrhythmic patterns and healthy signals. This is a notable advantage over other models that are burdened by a large number of differential equations and many parameters. [ABSTRACT FROM AUTHOR]

Respiratory conditions have been a focal point in recent medical studies. Early detection and timely treatment are crucial factors in improving patient outcomes for any medical condition. Traditionally, doctors diagnose respiratory conditions through an investigation process that involves listening to the patient's lungs. This study explores the potential of combining audio analysis with convolutional neural networks to detect respiratory conditions in patients. Given the significant impact of proper hyperparameter selection on network performance, contemporary optimizers are employed to enhance efficiency. Moreover, a modified algorithm is introduced that is tailored to the specific demands of this study. The proposed approach is validated using a real-world medical dataset and has demonstrated promising results. Two experiments are conducted: the first tasked models with respiratory condition detection when observing mel spectrograms of patients' breathing patterns, while the second experiment considered the same data format for multiclass classification. Contemporary optimizers are employed to optimize the architecture selection and training parameters of models in both cases. Under identical test conditions, the best models are optimized by the introduced modified metaheuristic, with an accuracy of 0.93 demonstrated for condition detection, and a slightly reduced accuracy of 0.75 for specific condition identification. [ABSTRACT FROM AUTHOR]

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Featured Application: The proposed models can help uncover the usage areas of geothermal waters by determining the reservoir temperatures in advance. Thus, they can be used as a decision support system to make the most appropriate selection. To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses by accurately predicting reservoir temperatures. In recent years, applying hybrid methods to real-world challenges has become increasingly prevalent over traditional machine learning methodologies. This study introduces a novel machine learning approach, named AOSMA-MLP, integrating the adaptive opposition slime mould algorithm (AOSMA) and multilayer perceptron (MLP) techniques, specifically designed for predicting the reservoir temperature of geothermal resources. Additionally, this work compares the basic artificial neural network and widely recognized algorithms in the literature, such as the whale optimization algorithm, ant lion algorithm, and SMA, under equal conditions using various evaluation regression metrics. The results demonstrated that AOSMA-MLP outperforms basic MLP and other metaheuristic-based MLPs, with the AOSMA-trained MLP achieving the highest performance, indicated by an R2 value of 0.8514. The proposed AOSMA-MLP approach shows significant potential for yielding effective outcomes in various regression problems. [ABSTRACT FROM AUTHOR]

Software testing is a crucial step in the development of software that guarantees the dependability and quality of software products. A crucial step in software testing is test case minimization, which seeks to minimize the number of test cases while ensuring maximum coverage of the system being tested. It is observed that the existing algorithms for test case minimization still suffer in efficiency and precision. This paper proposes a new optimization algorithm for efficient test case minimization in software testing. The proposed algorithm is designed on the base parameters of the metaheuristic algorithms, inspired by scientific principles. We evaluate the performance of the proposed algorithm on a benchmark suite of test cases from the literature. Our experimental results show that the proposed algorithm is highly effective in reducing the number of test cases while maintaining high coverage of the system under test. The algorithm outperforms the existing optimization algorithms in terms of efficiency and accuracy. We also conduct a sensitivity analysis to investigate the effect of different parameters on the performance of the proposed algorithm. The sensitivity analysis results show that the performance of the algorithm is robust to changes in the parameter values. The proposed algorithm can help software testers reduce the time and effort required for testing while ensuring maximum coverage of the system under test. [ABSTRACT FROM AUTHOR]

The task of ensuring the end-to-end quality of service of heterogeneous wireless (WW) networks with random multiple access is considered. The processes of the occurrence of conflicts between the fairness and optimality of route selection and resource allocation in BP networks are analyzed. A technique for eliminating conflicts between fairness and optimality of resource distribution has been developed. It is shown that in the rational definition of radio access technology in heterogeneous networks, the functions of each level and, accordingly, the information parameters of the traffic at this level can be attributed to one of two alternative groups: functions that depend on a specific technical implementation of the network or functions oriented to work with applications. Accordingly, a global approach to the optimal selection of a radio access technology (RAT) is formulated as a problem with super-polynomial complexity, and an approach for RAT selection using a distributed metaheuristic tabu search algorithm based on the peak rate that users are able to perceive is proposed. A distributed RAT selection system based on metaheuristic optimization of the set of routes with elimination of redundancy has been developed. A combined metaheuristic optimization method based on a dual approach - a combination of adaptability and learning from previously obtained results - has been developed. An original method of one-time overcoming the prohibition of tabu search paths has been introduced: when the next step is considered unpromising and should be rejected, but good results were obtained from the previous steps - this step is allowed, and the obtained result is counted as an initial one for further search. A general expression for the distribution of the access point’s stay time in a heterogeneous wireless network together with the access point boundary crossing probabilities is obtained. Numerical results and simulation results show satisfactory agreement. [ABSTRACT FROM AUTHOR]

This work presents a new metaheuristic called treble opposite algorithm (TOA). It consists of three phases. There are two searches that are opposite to each other performed in each phase. In the first phase, the search toward and away from the best solution is carried out. In the second phase, the search toward and away from the middle between two randomly picked solutions is carried out. In the third phase, a neighborhood search around the narrow and large space is carried out. A candidate is selected among the two searches in every phase. TOA is challenged to solve theoretical and practical problems. The 23 functions represent theoretical problems, while the portfolio optimization of stocks in the banking sector listed in IDX30 represents the practical problem. TOA is compared with five metaheuristics: grey wolf optimization (GWO), golden search optimization (GSO), average subtraction-based optimization (ASBO), zebra optimization algorithm (ZOA), and coati optimization algorithm (COA). The result indicates that TOA is superior to its competitors as it is better than GWO, GSO, ZOA, ASBO, and COA in 22, 23, 19, 20, and 19 functions respectively, in handling 23 functions and produces the highest total capital gain in handling portfolio optimization problem. In the future, TOA can be utilized to handle many other realworld optimization problems. Moreover, TOA can be hybridized with other metaheuristics to improve its performance. [ABSTRACT FROM AUTHOR]

In real-world scheduling applications, machines may be unavailable during certain time periods for deterministic and stochastic reasons. This situation does not pose any problems if the jobs always have more than one machine available for processing. However, it becomes an issue if the only available machine is the one which more than one job needs for processing. Thus, the investigation of limited machine availability, along with the practical requirement to handle this feature of scheduling problems, are of huge significance. This paper examined a flexible job shop environment of a manufacturing firm to optimize different performance criteria related to makespan, due dates, priorities and penalties by using a metaheuristic approach, taking into account the precedence constraints. The work investigated the case in which all machines are available for processing and another case in which some of the machines are known in advance to be unavailable. From the results, the best schedules are analysed and the perspectives of the findings to decisionmakers are discussed with the purpose of achieving high machine utilization, cost reduction and customer satisfaction. [ABSTRACT FROM AUTHOR]

Phishing is a sort of cyberattack that refers to the practice of fabricating fake websites that imitate authentic websites in order to trick users into disclosing private information. Identifying these fake sites is challenging due to their deceptive nature as they often mimic legitimate websites, making it difficult for users to distinguish between the real and fake ones. Artificial Neural Network (ANN) is one popular method for website phishing detection. ANN is capable of detecting phishing websites by identifying patterns and characteristics connected to phishing websites through a network training phase. Technically, in the network training phase of ANN, neurons on the network must be passed over. There are multiple techniques in training the network, one of which is training with metaheuristic algorithms. Metaheuristic algorithms that aim to develop more effective hybrid algorithms by combining the good and successful aspects of more than one algorithm are algorithms inspired by nature. Therefore, this study proposed a hybrid Honey Badger Algorithm with Artificial Neural Network (HBA-ANN) classification model. HBA as metahueristic algorithm is used to optimize the network training process of ANN to improve their performances. Three main steps made up the proposed HBA-ANN classification model: setting up the experiment, optimizing HBA for network training, and network testing. Lastly, the performance of the proposed HBA-ANN classification model is assessed in terms of recall, precision, F1-score, accuracy and error rate using the confusion matrix that was generated for analysis. The proposed hybrid HBAANN was found to be effective in identifying the phishing website after conducting an experimental and statistical analysis.

This article presents a heuristic methodology to address the operation problem of PV-STATCOMs, focusing on the dynamic compensation of active and reactive power to minimize daily energy losses and costs associated with purchasing energy in distribution networks. The methodology is developed using a master-slave type optimization approach. In the master stage, potential solutions are generated using an optimization algorithm based on the salp swarm. In contrast, the slave stage evaluates these solutions using the power flow method by successive approximations. The methodology was evaluated in two test systems, with 33 and 69 nodes, varying the power factor of each PV-STATCOM. The results demonstrate a significant reduction in both energy losses in the network and the cost of purchasing energy at the substation. Furthermore, the efficiency of the salp swarm algorithm for effective resolution of the problem is confirmed. This approach contributes to optimizing the operation of PV-STATCOMs and highlights the proposed algorithm's applicability and effectiveness in practical distribution network environments.

Over recent decades, the field of mobile robot path planning has evolved significantly, driven by the pursuit of enhanced navigation solutions. The need to determine optimal trajectories within complex environments has led to the exploration of diverse path planning methodologies. This paper focuses on a specific subset: Bio-inspired Population-based Optimization (BPO) methodologies. BPO methods play a pivotal role in generating efficient paths for path planning. Amidst the abundance of optimization approaches over the past decade, only a fraction of studies has effectively integrated these methods into path planning strategies. This paper’s focus is on the years 2014-2023, reviewing BPO techniques applied to mobile robot path planning challenges. Contributions include a comprehensive review of recent BPO methods in mobile robot path planning, along with an experimental methodology to compare them under consistent conditions. This encompasses the same environment, initial conditions, and replicates. A multi-objective function is incorporated to evaluate optimization methods. The paper delves into key concepts, mathematical models, and algorithm implementations of examined optimization techniques. The experimental setup, methodology, and benchmarking performance results are discussed. Based on the proposed experimental methodology, Improved Sparrow Search Algorithm (ISpSA) shows the best cost improvement percentage (7.87%), but suffers in terms of optimization time. On the other hand, Whale Optimization Algorithm (WOA) has lesser improvement percentage of 6.05% but better optimization time. In conclusion, the standardized approach for benchmarking BPO algorithms provides useful insights into their strengths and challenges in mobile robot path planning.

This study presents a comprehensive analysis of enhancing software effort estimation accuracy using a Self-Organizing Migration Algorithm (SOMA)-optimized Constructive Cost Model (COCOMO). By conducting a comparative study of traditional COCOMO models and SOMA-optimized variants across preprocessed datasets (NASA93, NASA63, NASA18, Kemerer, Miyazaki94, and Turkish), our research focuses on crucial evaluation metrics, including Mean Magnitude of Relative Error (MMRE), Prediction at 0.25 (PRED(0.25)), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The analysis encompasses various configurations of COCOMO models—basic, intermediate, and post-architecture COCOMO II, supplemented with additional statistical testing and residual analysis for in-depth insights. The results demonstrate that the SOMA-optimized COCOMO models generally surpass traditional models in predictive accuracy, especially notable in metrics such as MMRE where an improvement of up to 12%, PRED(0.25) with an enhancement of 15%, MAE reduction by 18%, and a decrease in RMSE by 20% were observed. However, performance variances were identified in specific scenarios, highlighting areas for further refinement, particularly in large-scale estimations where residual plots suggested the potential for underestimation or overestimation. The study concludes that integrating the SOMA optimization algorithm into COCOMO models significantly enhances the accuracy of software effort estimations, providing valuable insights for future research to optimise estimations for larger projects and advance prediction models. This advancement addresses the technical challenge of parameter accuracy and offers a methodological improvement in model selection and application, underscoring the potential of metaheuristic optimization in software effort estimation.

The Industrial Internet of Things (IIoT) is the recent innovation that had revolutionized the industries by enabling interconnected devices and systems to exchange intelligent data. However, implementing and operating such IIoT systems have various challenges. This article addresses those challenges pertained to task offloading in IIoT in which the resource-intensive tasks are transmitted and executed on remote cloud servers. To optimize the task offloading decisions this work propose the integration of Digital Twins, which are the computer-generated replicas of physical objects. By using the functionalities of Digital Twins along with real-time monitoring, and metaheuristic optimization algorithms this work presents a task offloading model for IIoT. Through this combined framework, the proposed model attempts to minimize the task execution time by considering the server capacity, bandwidth constraints, and device power consumption. The proposed Offloading with Digital Twins and Raindrop Algorithm (ODTRA) algorithm that is based on the water cycle metaphor and the Probabilistic Recursive Local (PRL) search algorithm had efficiently optimizes offloading performance which was proven through different experiment simulation and analysis.

To mitigate the rising threat of Android malware, researchers have been actively looking for mechanisms that will enable rapid and accurate malware detection. Recently, attention has been paid to the use of audio-based features derived through the use of music information retrieval techniques. Since the exploration of these features is still in the early stages, there is a need to continue experimentation, especially with features that have yet to be used for this task. In this paper, we present the results of an ongoing investigation into the use of audio-based features for Android malware detection. In addition to extracting new audio-based features, this research aims to find the most discriminative subset of audio-based features through a comparative evaluation of Wrapper-based metaheuristic optimization algorithms on two separate datasets. First, we sonified the Android APK datasets, then extracted 191 static audio-based features from the resultant audio datasets. Fourteen different nature-inspired Wrapper-based metaheuristic optimization algorithms were evaluated for feature selection, and the selected features were then used to train the light gradient-boosting machine (LGBM) classification model. Experimental results demonstrate that the proposed approach exhibits high discriminative capabilities that can outperform other state-of-the-art techniques. The best outcome for Android malware detection was obtained using features selected by the Genetic Algorithm, which achieved 50.26% feature reduction and an improved classification accuracy of 99.72%.

Galactic Swarm Optimization (GSO) is an optimization method inspired by the movements of stars and star clusters in the galaxy. This method aims to find the best solution in two phases using other known optimization methods. The first phase explores the search space, while the second phase tries to refine the best solution. In GSO, the population of the best individuals obtained from the first phase is used as the initial population for the second phase. This process is repeated until the stopping criterion is met. Although the knowledge obtained from the first phase is transferred to the second phase in GSO, there is no knowledge transfer from the second phase to the first phase. In this study, we propose a modification where the knowledge obtained in the second phase is transferred back to the first phase. Additionally, the Particle Swarm Optimization (PSO) method, used as the search method in the original study, has a fast convergence problem, which hinders an effective discovery process in the first phase of GSO. To address this, the proposed diversity-guided modification regulates population diversity and enhances exploration. To demonstrate the performance of the proposed method, twenty-six traditional benchmark functions and three engineering design problems were used. The proposed method was compared with the original GSO and six current optimization methods. The results of the experimental study were analyzed using statistical tests. The experimental results and analyses show that the proposed method performs successfully.

This paper presents an innovative approach to enhance the hybrid Differential Evolution (DE) algorithm by focusing on improved convergence speed, exploration capabilities, and solution precision. This is achieved through the integration of elitism in evolutionary algorithms, gradient methods, and the Metropolis acceptance criterion in Simulated Annealing (SA). We introduce the Gradient Differential Evolution-Simulated Annealing Hybrid (GDESA) framework, refined through parameter sensitivity analysis, which led to the development of two variants: Multi-Phase GDESA (M-GDESA) and Adaptive Elitist GDESA (A-GDESA). M-GDESA improves convergence by adding user-controlled phases but struggles with parameter fine-tuning and semi-static selection. In contrast, A-GDESA allows each individual to dynamically choose between elitist and SA offspring selection schemes at each iteration, enhancing adaptability to diverse problems. Our hybrid frameworks are highly flexible, accommodating any DE variant as the main program. Even with standard DE, our approach is highly competitive on the CEC2017 benchmark for 30-dimensional problems, statistically outperforming existing DE-SA hybrids like iSADE and HDESA, as well as state-of-the-art heuristics such as DE, PSO, ABC, and WOA. Not only does A-GDESA outperform each of the six baselines in at least 24 out of the 29 test functions, but it also achieves the fastest convergence. When using the advanced L-SHADE variant as the main program, our framework outperforms even well-known optimizers and top performers from CEC competitions like L-SHADE and LSHADE-cnEpSin on 100-dimensional problems. The obtained results highlight the effectiveness of the GDESA framework in enhancing DE-SA hybrid optimization and demonstrate the exceptional potential of the self-adaptive scheme with gradient methods.

The advancement in technology and the availability of specialized digital signal processing chips have made digital filter design and implementation more feasible in a variety of fields, including biomedical engineering. This paper makes two key contributions. First, it uses a genetic algorithm to optimize the coefficients of finite impulse response (FIR) filters. Second, it conducts a case study on using genetic algorithms to optimize FIR filters for electrocardiogram (ECG) biomedical signal noise removal. The goal of the proposed filter design approach is to achieve the desired signal bandwidth while minimizing the side lobe level and eliminating unwanted signals using a genetic algorithm. The results of a comprehensive analysis show that the genetic algorithm-based filter is more effective than conventional filter designs in terms of noise removal efficiency.

Internet of medical things (IoMT) network design integrates multiple healthcare devices to improve patient monitoring and real-time care operations. These networks use a wide range of devices to make critical patient care decisions. Thus, researchers have deployed multiple high-security frameworks with encryption, hashing, privacy preservation, attribute based access control, and more to secure these devices and networks. However, real-time monitoring security models are either complex or unreconfigurable. The existing models’ security depends on their internal configuration, which is rarely extensible for new attacks. This paper introduces a hybrid metaheuristic model to improve healthcare IoT security performance. The blockchain based model can be dynamically reconfigured by changing its encryption and hashing standards. The proposed model then continuously optimizes blockchain based IoMT deployment security and QoS performance using elephant herding optimization (EHO) and grey wolf optimization (GWO). Dual fitness functions improve security and QoS for multiple attack types in the proposed model. These fitness functions help reconfigure encryption and hashing parameters to improve performance under different attack configurations. The hybrid integration of EH and GW optimization models can tune blockchain based deployment for dynamic attack scenarios, making it scalable and useful for real-time scenarios. The model is tested under masquerading, Sybil, man-in-the-middle, and DDoS attacks and is compared with state-of-the-art models. The proposed model has 8.3% faster attack detection and mitigation, 5.9% better throughput, a 6.5% higher packet delivery ratio, and 10.3% better network consistency under attack scenarios. This performance enables real-time healthcare use cases for the proposed model.

In this paper, it is revealed that random exploration and attraction of the best (REAB) are two underlying procedures in many swarm intelligence algorithms. This is particularly shown in two of the most known swarm algorithms: the particle swarm optimization (PSO) and gray wolf optimizer (GWO) algorithms. From this observation, it is here proposed that instead of building algorithms based on a narrative derived from observing some animal behavior, it is more convenient to focus on algorithms that perform REAB procedures; that is, to build algorithms to make a wide and efficient explorations of the search space and then gradually make that the best-evaluated search agent to attract the rest of the swarm. Following this general idea, two REAB-based algorithms are proposed; one derived from the PSO and one derived from the GWO, called REAB-PSO and REAB-GWO, respectively. To easily and succinctly express both algorithms, variable-sized open balls are employed. A comparison of proposed procedures in this paper and the original PSO and GWO using a controller tuning problem as a test bench show a significant improvement of the REAB-based algorithms over their original counterparts. Ideas here exposed can be used to derive new swarm intelligence algorithms.

Abstract The paper discusses the orientation and positions of solar panels in a solar tree. Solar energy contributes to the most available and abundant energy source in Egypt. Hence, The PV system is considered a promising solution to generate power from this source. However, its energy density is low. Therefore, the solar tree is considered in this paper as it has higher density and fits the urban cities of Egypt. The design of the solar tree is divided into two sections: the study of the solar panel’s orientation and positions. For the solar tree orientation, a single-objective genetic algorithm is used to maximize the total incident irradiance on a tilted solar panel. The output of this optimization problem is 20 optimal angles for the tilt angle ( $$\beta$$ β ) and surface azimuth angle of the solar panel ( $$\gamma$$ γ ). These angles are inputted to the second methodology to determine their positions. In this methodology, a multi-objective genetic algorithm is used to trade between the area occupied by the solar tree representing the cost and the power loss due to the shading of the solar panels on each other; the output is the coordinates of the center of each solar panel. The proposed solar tree design increased the incident irradiance from 4427.389 to 4496.584 watts/m2/year using the optimal angles and the output power increased as the shadow loss decreased from 904.685 watts to 300 watts. The final design of the solar tree is 5 m in height and 1.13 m wide.

Transmission of information is an essential component in an IoT device for sending, receiving, and collecting data. The Smart devices in IoT architecture are designed as physical devices linked with computing resources that can connect and communicate with another smart device through any medium and protocol. Communication among various smart devices is a challenging task to exchange information and to guarantee the information reaches the destination entirely in real-time in the same order as sent without any data loss. Thus, this article proposes the novel Bat-based Deep Belief Neural framework (BDBN) method for the air pollution monitoring scheme. The reliability of the proposed system has been tested under the error condition in the transport layer and is validated with the conventional methods in terms of Accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson correlation coefficient (r), Coefficient of determination (R2) and Error rate.

Recently, Numerous metaheuristic techniques have been utilized for the expedient identification of Proton Exchange Membrane Fuel Cells ‎ (PEMFCs) models. The reported techniques can inspect fickle in a wide search space for finding optimal solutions at the appropriate time. In this paper, recent optimization techniques are intended to better identify the unknown parameters of various PEMFCs. Three neoteric metaheuristic techniques of the Gazelle optimization algorithm (GOA), Prairie Dog Optimization Algorithm (PDO), and Reptile Search Algorithm (RSA) have been applied and evaluated. The proposed optimization algorithms have been validated for identifying the parameters of three PEMFCs‎: BCS 500 W PEMFC, SR-12 500 W PEMFC, and 250 W PEMFC stack‎. The sum of the squared errors (SSE) between the estimated voltage and the corresponding measured data was formulated as the objective function (OF). MATLAB/Simulink has been employed to validate the proposed optimization methods. The results showed that the three optimization techniques can solve the Fuel Cell ‎(FC) parameters identification optimization problem. Moreover, there are insignificant distinctions between the three applied methods with regard to their optimal value of the objective function. The finest technique considering the average value of the objective function is GOA for BCS 500 W-PEM with 0.0115, while the worst algorithm is PDO with 0.0112. Additionally, the statistical results prove that the three algorithms have 100%, 99.99%, and 100% tracking efficiencies for GOA, PDO, and RSA, respectively, according to 30 individual launches of BCS 500 W-PEM. The results have been evaluated via those of published articles. The I/V curves achieved employing GOA, PDO, and RSA methods provided a good agreement with the corresponding measured ones with the superiority of the GOA relating to the convergence speed, tracking efficiency, statistical metrics, and estimation accuracy.

Objective: The objective of this paper is to propose a methodology for the optimal location and sizing of D-STATCOMs within a distribution electrical system, with the aim to reduce the annualized operating costs related to the annual power energy losses and the investment costs associated with the installation of the D-STATCOM. Context: This paper presents a hybrid methodology based on a master-slave strategy and the genetic and particle swarm optimization algorithms for solving the problem of optimal location and sizing of Distribution Static Compensators (DSTATCOMs), for reactive compensation in electrical distribution systems. Methodology: In this paper was used a mathematical formulation that represents the effect of the location and sizing of D-STATCOMs in electrical distribution systems; by proposing a master-slave methodology combining the genetic algorithm and the particle swarm optimization algorithms as a solution method. Furthermore, with the aim to validate the effectiveness and robustness of the proposed methodology in this work, three comparison methods, two test systems, and multiple technical considerations were used to represent the electrical distribution systems in a distributed energy resource environment. Results: The results obtained show that the proposed methodology is the most effective solution method for solving the problem, by achieving the greatest reduction in relation to the investment and operating costs. This methodology will allow the grid operators to identify the location and size of the D-STATCOMs within the electrical energy distribution system, with the lowest investment and operating costs in relation to other works reported in specialized literature. Conclusions: The obtained results demonstrate that GA/PSO achieved the best performance, with the DCVSA comparison method in second place, and the GAMS solvers in third place. It is important to notice that it was not possible to evaluate the GAMS solvers on the 69 bus test system, because this solver failed the mathematical formulation that represented this electrical system. Based on previous results, it can be concluded that the GA/PSO is the most suitable optimization method used for solving the problem of optimal integration of D-STATCOMs in Distribution electrical systems for the grid.

A significant part of agricultural farms in the Kingdom of Saudi Arabia (KSA) are in off-grid sites where there is a lack of sufficient water supply despite its availability from groundwater resources in several regions of the country. Since abundant agricultural production is mainly dependent on water, farmers are forced to pump water using diesel generators. This investigation deals with the increase in the effectiveness of a solar photovoltaic water pumping system (SPVWPS). It investigated, from a distinct perspective, the nonlinear behavior of photovoltaic modules that affects the induction motor-pump because of the repeated transitions between the current and the voltage. A new chaotic Horse Herd Optimization (CHHO)-based Maximum Power Point Tracking technique (MPPT) is proposed. This algorithm integrates the capabilities of chaotic search methods to solve the model with a boost converter to maximize power harvest while managing the nonlinear and unpredictable dynamical loads. The analytical modeling for the proposed SPVWPS components and the implemented control strategies of the optimal duty cycle of the DC–DC chopper duty cycle and the Direct Torque Control (DTC) of the Induction Motor (IM) has been conducted. Otherwise, the discussions and evaluations of the proposed model performance in guaranteeing the maximum water flow rate and the operation at MPP of the SPVWPS under partial shading conditions (PSC) and changing weather conditions have been carried out. A comparative study with competitive algorithms was conducted, and the proposed control system's accuracy and its significant appropriateness to improve the tracking ability for SPVWPS application have been proven in steady and dynamic operating climates and PSC conditions. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of several metaheuristic optimization methods for generating trajectories of automated guided vehicles, which commonly operate in industrial environments. The goal is to address the challenge of efficient path planning for mobile robots, taking into account the specific capabilities and mobility limitations inherent to automated guided vehicles. To do this, three optimization techniques are compared: genetic algorithms, particle swarm optimization and pattern search. The findings of this study reveal the different efficiency of these trajectory optimization approaches. This comprehensive research shows the strengths and weaknesses of various optimization methods and offers valuable information for optimizing the trajectories of industrial vehicles using geometric occupancy maps. [ABSTRACT FROM AUTHOR]

The process of feature mapping is used to represent features on a map with their corresponding properties. The features are displayed visually and their associated information is made available. Various abstraction approaches, including alignment approaches, are introduced to reduce the computational complexity. However, previous works based on standard treatments of feature maps often suffer from the negative effects of noise and background details. To solve this problem, we present a simple and effective implementation approach for Filter Pruning via Attention Consistency (FPAC), which determines a revolutionary filter pruning mechanism. Feature maps that concentrate on a single layer are inconsistent, which can affect the spatial dimension. The feature map with minimum consistency is less significant and is experimentally demonstrated. This study presents a novel layer-wise pruning technique using the Aphid Ant Mutualism (AAM) algorithm, which considers the sensitivity of various convolutional network layers to model inference and sets the optimal pruning ratio. The accuracy of the compressed model is enhanced by eliminating high redundancy through pruning correlated filters. The performance of FPAC is confirmed through the Caltech 256 image dataset. With VGG-16 on the Caltech 256 image dataset, the classification accuracy is enhanced from 93.96 to 94.03%. With ResNet-50 on the Caltech 256 image dataset, 45% FLOPs are achieved with an accuracy loss of only 0.53%. [ABSTRACT FROM AUTHOR]

Quantum computing has opened up various opportunities for the enhancement of computational power in the coming decades. We can design algorithms inspired by the principles of quantum computing, without implementing in quantum computing infrastructure. In this paper, we present the quantum predator–prey algorithm (QPPA), which fuses the fundamentals of quantum computing and swarm optimization based on a predator–prey algorithm. Our results demonstrate the efficacy of QPPA in solving complex real-parameter optimization problems with better accuracy when compared to related algorithms in the literature. QPPA achieves highly rapid convergence for relatively low- and high-dimensional optimization problems and outperforms selected traditional and advanced algorithms. This motivates the application of QPPA to real-world application problems. [ABSTRACT FROM AUTHOR]

Since late 2019, most efforts to control the COVID-19 pandemic have focused on developing vaccines. By mid-2020, some vaccines fulfilled international regulations for their application. However, these vaccines have shown a decline in effectiveness several weeks after the last dose, highlighting the need to optimize vaccine administration due to supply chain limitations. While methods exist to prioritize population groups for vaccination, there is a lack of research on how to optimally define the time between doses when two-dose vaccines are administrated to such groups. Under such conditions, modeling the real effect of each vaccine on the population is critical. Even though several efforts have been made to characterize vaccine effectiveness profiles, none of these initiatives enable characterization of the individual effect of each dose. Thus, this paper presents a novel methodology for estimating the vaccine effectiveness profile. It addresses the vaccine characterization problem by considering a deconvolution of relevant data profiles, treating them as an optimization process. The results of this approach enabled the independent estimation of the effectiveness profiles for the first and second vaccine doses and their use to find sweet spots for designing efficient vaccination strategies. Our methodology can enable a more effective and efficient contemporary response against the COVID-19 pandemic, as well as for any other disease in the future. [ABSTRACT FROM AUTHOR]

A unique hybrid meta-heuristic combining the salp swarm algorithm and the sine cosine algorithm (SSCA) is established in this study to improve convergence speed while outperforming existing conventional algorithms. The sine cosine position equations are utilized to update the position of the salp leader in search space while a weighting factor updates the position of the salp follower so that the best and possible optimal solutions are obtained using the sine or cosine and weighting function. Particle swarm optimization PSO inspires this weighting factor. Each salp uses the information-sharing approach of sine and cosine functions during this process to strengthen their exploration and exploitation abilities. The goal of incorporating modifications to the salp swarm optimizer algorithm is to help the standard approach avoid premature convergence that leads the search to the most likely search space. The proposed algorithm is tested on classical optimization benchmark functions and eight real engineering applications. The goal is to investigate and validate the SSCA's proper behaviour while finding the optimum solutions. The results of the comparison demonstrated that the SSCA method achieves the best accuracies. [ABSTRACT FROM AUTHOR]

The unplanned development of agricultural land and urban areas poses threats to water quality, which can lead to the death of the aquatic species in rivers. The present study developed a novel framework by combining a soil and water assessment tool (SWAT) and evolutionary algorithms to optimize the cultivation pattern at the catchment scale in the Tajan River basin, with the aim of mitigating the environmental impacts of surface runoff from farms. This river basin is located in northern Iran, where quick agricultural development is one of the environmental challenges. We utilized a SWAT to simulate the nitrate concentrations for different crops at the river basin scale by applying the Nash–Sutcliffe model efficiency coefficient (NSE) as a measurement index. Then, a novel model was developed to optimize the cultivation pattern by applying different metaheuristic algorithms. Fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) was applied as a decision-making system to select the best optimization algorithm. The results demonstrated that using a SWAT in the optimization model structure is a robust method for the design of an optimal cultivation pattern. The Nash–Sutcliffe model efficiency coefficient (NSE) was 0.74, demonstrating the robust predictive skills of the water quality model. The decision-making system indicated that particle swarm optimization and shuffled complex evolution were the best evolutionary algorithms to optimize the cultivation pattern using the proposed method. The proposed method opens a new window regarding the optimization of cultivation patterns in agriculture and provides an environmental-based optimization to design cultivation patterns on the catchment scale. [ABSTRACT FROM AUTHOR]

Owing to the stochastic behavior of renewable energy activity and the multiple design considerations, the advancement of hybrid renewable energy-based microgrid (HREMG) systems has become a complex task. This study proposes a design optimization algorithm for the long-term operation of an autonomous HREMG along with the optimal system capacities. The investigated energy system comprises photovoltaic panels, wind turbines, diesel generators, and batteries. It aims to energize a remote coastal community with a daily load demand of 400 kWh in Marsa Matruh, Egypt. Since most studies utilize commercial tools in the design optimization procedure, the African vultures optimization approach (AVOA) is developed to find the optimal energy alternative and determine the optimal component's capacity considering achieving the minimum energy cost and loss of power supply probability. Moreover, an adequate energy management strategy is suggested to coordinate the power flow within the energy system in which renewable energy sources are fully penetrated. To check the AVOA robustness and efficacy, its performance is compared with the HOMER Pro most popular commercial tool as well as with new metaheuristic algorithms, namely the grasshopper optimization algorithm (GOA) and Giza pyramid construction (GPC) under the same operating environment. The results revealed that the proposed AVOA achieved superior economic results toward the least net present cost ($346,614) and energy price (0.0947 $/kWh). Moreover, over 20 independent runs, the AVOA showed a better performance in terms of convergence and execution time compared to other tools/algorithms. The obtained findings could be a useful benchmark for researchers in the sizing problem of hybrid energy systems. [ABSTRACT FROM AUTHOR]

With the rapid development of next-generation wireless communications, there is a growing demand for high-quality integrated circuits (ICs), particularly analog ICs, which play a pivotal role for the full roll-out sixth-generation (6G) technology. So far, the IC design has been performed through manual approaches which sometimes results in time-consuming turnaround, especially the sizing phase of analog IC design. In order to make the IC design process much faster, recently automated methods for optimizing IC design has gained a lot of attention. From this perspective, this paper aims at providing a survey of the most recent works on optimization strategies for analog IC sizing, as well as a related categorization into two main categories: analytical methods and simulation-based methods. A further sub-classification within the realm of simulation-based methods is also provided by dividing the core mathematical principles into three major sub-methods: Bayesian-based, metaheuristic-based, and reinforcement-learning-based techniques. In addition, with the main aim of providing insights on the utilization of optimization algorithms for the IC sizing process, we present a case study involving the utilization of various metaheuristic algorithms in the design of a bandgap reference circuit - an essential analog IC component. The paper is concluded by highlighting potential future research directions in the field of analog IC design optimization and automation, which include exploration of multi-agent reinforcement learning, integration of quantum computing, and further development of full-flow automated tools for analog IC design. [ABSTRACT FROM AUTHOR]

The paper discusses the orientation and positions of solar panels in a solar tree. Solar energy contributes to the most available and abundant energy source in Egypt. Hence, The PV system is considered a promising solution to generate power from this source. However, its energy density is low. Therefore, the solar tree is considered in this paper as it has higher density and fits the urban cities of Egypt. The design of the solar tree is divided into two sections: the study of the solar panel’s orientation and positions. For the solar tree orientation, a single-objective genetic algorithm is used to maximize the total incident irradiance on a tilted solar panel. The output of this optimization problem is 20 optimal angles for the tilt angle (β ) and surface azimuth angle of the solar panel (γ ). These angles are inputted to the second methodology to determine their positions. In this methodology, a multi-objective genetic algorithm is used to trade between the area occupied by the solar tree representing the cost and the power loss due to the shading of the solar panels on each other; the output is the coordinates of the center of each solar panel. The proposed solar tree design increased the incident irradiance from 4427.389 to 4496.584 watts/m2/year using the optimal angles and the output power increased as the shadow loss decreased from 904.685 watts to 300 watts. The final design of the solar tree is 5 m in height and 1.13 m wide.Article Highlights: The paper optimizes the solar tree through the orientation and position of the solar panels increasing its energy output while minimizing cost and shadow loss caused by shading of solar panels on each other. Simulink model verifies the solar tree’s optimization effectiveness, and finite element analysis validates its applicability under real-life conditions. [ABSTRACT FROM AUTHOR]

The advancement in technology and the availability of specialized digital signal processing chips have made digital filter design and implementation more feasible in a variety of fields, including biomedical engineering. This paper makes two key contributions. First, it uses a genetic algorithm to optimize the coefficients of finite impulse response (FIR) filters. Second, it conducts a case study on using genetic algorithms to optimize FIR filters for electrocardiogram (ECG) biomedical signal noise removal. The goal of the proposed filter design approach is to achieve the desired signal bandwidth while minimizing the side lobe level and eliminating unwanted signals using a genetic algorithm. The results of a comprehensive analysis show that the genetic algorithm-based filter is more effective than conventional filter designs in terms of noise removal efficiency. [ABSTRACT FROM AUTHOR]

Metaheuristic optimization techniques are a powerful tool to decide the optimal gains for underactuated systems such as quadrotors, considering the multiple controllers involved in the inner and outer loops of the system. This paper deals with the evaluation of performance of the set of controllers (PI and SMC) set by three optimization methods: particle swarm optimization (PSO), grey wolf optimization (GWO), and equilibrium optimization (EO). The disparity of the control gains obtained during the optimization is a sign of the distinction between performance reached by each controller. The proposed study investigates this difference through a robustness test by gradually including faults to the quadrotor actuators. We observed degradation in the agility of quadrotor (stabilization of altitude and attitude) for the faulty case; however, the three controllers showed different tolerances to the fault. The simulation results show that the parameters optimized by EO algorithm, outperform both the PSO and the GWO algorithm, especially for the considered unfavorable cases. [ABSTRACT FROM AUTHOR]

The digital content exchange on the Internet is associated with information security risks. Hiding data in digital images is a promising direction in data protection and is an alternative to cryptographic methods. Steganography algorithms create covert communication channels and protect the confidentiality of messages embedded in cover images. Watermarking algorithms embed invisible marks in images for further image authentication and proof of the authorship. The main difficulty in the development of data hiding schemes is to achieve a balance between indicators of embedding quality, including imperceptibility, capacity, and robustness. An effective approach to solving this problem is the use of metaheuristic optimization algorithms, such as genetic algorithm, particle swarm optimization, artificial bee colony, and others. In this paper, we present an overview of data hiding techniques based on metaheuristic optimization. We review and analyze image steganography and image watermarking schemes over the past 6 years. We propose three levels of research classification: embedding purpose level, optimization purpose level, and level of metaheuristics. The results demonstrate the high relevance of using metaheuristic optimization in the development of new data hiding algorithms. Based on the results of the review, we formulate the main problems of this scientific field and suggest promising areas for further research. [ABSTRACT FROM AUTHOR]