Search

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]

Many researchers have widely applied shape descriptors to perform dimensional synthesis of mechanisms. This work investigates the path synthesis of planar four-bar linkages for closed and open curves using elliptical Fourier descriptors (EFDs). EFD is also a Fourier-based analysis method. Its Fourier coefficients of a coupler curve are obtained through separate Fourier expansion of the x and y components of the coupler curve rather than on a function. Elliptical Fourier descriptors are effective at describing complex curves with high curvature. A process has been developed for approximating non-periodic paths using EFD. By combining the process with the traditional EFD, a general method is established for the synthesis of four-bar linkages for open and closed curves in a single-step optimization process. The proposed approach offers an effective and efficient procedure in the path synthesis of four-bar linkages, providing a foundation for future research in the broader application of EFD in the dimensional synthesis of linkages. [ABSTRACT FROM AUTHOR]

Presently, several communities are employing renewable integrated combined heat-power (CHP) microgrids to optimally supply connected heat-power loads. Whilst microturbines are often employed in CHP microgrids, their operational flexibility as a CHP technology remains underexamined. The proposed work studies this perspective with acceptable penetrations of renewable energy sources (RES). Dynamic scheduling of combined heat-power islanded microgrid with RES and energy storage is presented for optimizing cost, emission, losses, and heat output considering RES and load uncertainties. RES uncertainties are modeled using Weibull distribution while load uncertainties are generated stochastically. To obtain the best solution for the contradictory multiple objectives and to reduce dependency on any specific tuning parameter, a fuzzy attainment module is integrated into the modified differential evolution algorithm with dynamic mutation rates. First, the operational flexibility of cogeneration units with RES and RES uncertainties is studied without storage. Secondly, with storage, and finally with storage under different load uncertainty scenarios. With both electrical and thermal storage, total operating costs are found to be reduced by 2.6%, emission by 1.2%, waste heat by 12.1%, and power losses by 25.4% per day. Random load scenarios in ± 25% uncertainty range were found to produce variations in cost in the range of − 2.49% to + 5.93% and emission from − 1.13 to 2.22% showing the capability of the proposed approach under practical conditions. This study demonstrates a cost-effective combined heat-power dispatch to do away with unwarranted auxiliary units with environmental, and climatic benefits. [ABSTRACT FROM AUTHOR]

To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to initialize the particle position matrix. Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the whale optimization algorithm (WOA) and the Differential Evolution (DE) mutation strategy in the later iteration to accelerate the convergence speed. The NDWPSO is further compared with other 8 well-known nature-inspired algorithms (3 PSO variants and 5 other intelligent algorithms) on 23 benchmark test functions and three practical engineering problems. Simulation results prove that the NDWPSO algorithm obtains better results for all 49 sets of data than the other 3 PSO variants. Compared with 5 other intelligent algorithms, the NDWPSO obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function ( f 1 - f 13 ) with 3 kinds of dimensional spaces (Dim = 30,50,100) and 80% of the best optimal solutions for 10 fixed-multimodal benchmark functions. Also, the best design solutions are obtained by NDWPSO for all 3 classical practical engineering problems. [ABSTRACT FROM AUTHOR]

The Western Xihu Basin (WXB), part of the East China Sea Shelf Basin (ECSSB), demonstrates distinct structural differences between different sub-areas. Understanding the origin and mechanism of these differences is critical for unravelling the formation and evolution of the Western Xihu Basin and the ECSSB. Based on high-resolution 2D and 3D seismic data, we investigate the structural characteristics and evolution of the hinged margin and discuss the underlying formation mechanisms. The results suggest that, while controlled by NNE-, NE- and NW-striking major faults, there are distinct differences in the fault geometry, margin structure, fault displacement rate, and margin evolution in different basin areas. In contrast to the conventional division scheme which divides the WXB into three general sub-areas, our results suggest that the WXB should be divided into seven sub-areas with different tectonic structures and stress histories. The evolution of the WXB can be divided into three stages: (1) the synrift stage in the Palaeocene and early Eocene, when NNE-, NE- and NW-striking major faults were highly active and controlled the initial formation of the hinged margin structure; (2) late synrift stage in the late Eocene, when the faulting activity diminished, and the control of faults on the margin evolution decreased; (3) postrift stage from the Oligocene onwards, when active faulting ceased and sedimentation and differential basement subsidence became the main factors controlling basin evolution. The formation and reactivation of NW-striking faults under influence of the Izanagi-Pacific ridge subduction during the Mesozoic provided the basis for the differential evolution of the WXB in Cenozoic. [ABSTRACT FROM AUTHOR]

The goal of this paper is to deal with a new dynamic system called a differential evolution hemivariational inequality (DEHVI) which couples an abstract parabolic evolution hemivariational inequality and a nonlinear differential equation in a Banach space. First, by applying surjectivity result for pseudomonotone multivalued mappins and the properties of Clarke's subgradient, we show the nonempty of the solution set for the parabolic hemivariational inequality. Then, some topological properties of the solution set are established such as boundedness, closedness and convexity. Furthermore, we explore the upper semicontinuity of the solution mapping. Finally, we prove the solution set of the system (DEHVI) is nonempty and the set of all trajectories of (DEHVI) is weakly compact in C(I, X). [ABSTRACT FROM AUTHOR]

The cooperative coevolution framework was introduced to address the shortcomings of metaheuristic algorithms in solving continuous large-scale global optimization problems. By dividing the problem into subcomponents which can be optimized separately, the framework can improve on both the solution's quality as well as the computational speed by exposing a degree of parallelism. Distributed computing platforms, such as Apache Spark, have long been used to improve the speed of different algorithms in solving computational problems. This work proposes a distributed implementation of the cooperative coevolution framework for solving large-scale global optimization problems on the Apache Spark distributed computing platform. By using a formerly outlined distributed variant of the cooperative coevolution framework, features of the Spark platform are utilized to enhance the computational speed of the algorithm while maintaining comparable search quality to other works in the literature. To test for the proposed implementation's improvement in computational speed, the CEC 2010 large-scale global optimization benchmark functions are used due to the diversity they offer in terms of complexity, separability and modality. Results of the proposed distributed implementation suggest that a speedup of up to ×3.36 is possible on large-scale global optimization benchmarks using the Apache Spark platform. [ABSTRACT FROM AUTHOR]

Scheduling tasks in the cloud system is the main issue that needs to be addressed in order to improve customer satisfaction and system performance. This paper proposes DCOHHOTS, a novel multi-objective task scheduling algorithm based on a modified Harris hawks optimizer. In overall, this paper has two main stages. As the first step, DCOHHO is introduced as a new version of Harris Hawks Optimizer. Using the Differential Evolution algorithm, an optimal configuration is selected from the chaotic map, the opposition-based learning, and the ratio of the population. In order to improve the performance of the Harris Hawks Optimizer, this optimal configuration is applied to initialize the hawk's position. In the second stage, DCOHHOTS, a DCOHHO-based Task Scheduling algorithm, is proposed. Multi-objective behavior in the proposed task scheduling algorithm optimizes resource utilization to decrease the makespan, energy consumption, and execution cost. Moreover, prioritizing tasks before submitting them to the scheduler is done using the hierarchical process in the DCOHHOTS algorithm. For the purpose of investigating the performance of the proposed DCOHHO algorithm, a number of experiments are conducted using 20 standard functions and twelve algorithms. The experimental results demonstrate that the DCOHHO algorithm is superior at determining the optimal test function solutions. Additionally, makespan, execution cost, resource utilization, and energy efficiency of DCOHHOTS task scheduling algorithms are analyzed. Compared to existing algorithms, the proposed algorithm saves up to 16% energy in heavy loads. Additionally, resource utilization has increased by 17%. Compared to the conventional algorithm, the proposed algorithm reduced makepan and execution cost by 26% and 8%, respectively. [ABSTRACT FROM AUTHOR]

Cleft lip and/or cleft palate (CL/P) are the common birth defects that result when facial structures developing in an unborn baby do not close completely. To design for treatment schedule, some constraints including hospital eligibility constraints, capacity limitations, treatment age limitations, multi-hospital assignment, and multidisciplinary care team assignment should be determined. However, efficient treatment scheduling is difficult owing to the complicated conditions of specific treatment. This paper presents a multi-objective mathematical model of the CL/P patient treatment scheduling problem in order to minimize the makespan, travel distance, and total least preference assignment score. Since the problem is NP-hard, a solution method is developed based on differential evolution (DE) with particular encoding and decoding schemes for solving the CL/P patient treatment scheduling problem. The performance of DE is evaluated and compared the results with those obtained from the modified particle swarm optimization. The results show that DE is capable of finding high-quality solutions with fast convergence. To apply the proposed approach for a case study, the CL/P patient treatment scheduling program is formulated. The program can be a decision support system in helping the administrators to schedule the patients in order to identify a list of selected treatments, assign each operation of patients to the selected hospital, and intelligently identify the period of the selected treatments. [ABSTRACT FROM AUTHOR]

Substantial deterioration of surface water quality, mainly caused by human activities and climate change, makes the assessment of water quality a global priority. Thus, in this study, four metaheuristic algorithms, namely the particle swarm optimization (PSO), differential evolution (DE), ant colony optimization algorithm (ACOR), and genetic algorithm (GA), were employed to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in the evaluation of surface water total dissolved solids (TDS). Monthly and annual TDS were considered as target variables in the analysis. In order to evaluate and compare the authenticity of the models, an economic factor (convergence time) and statistical indices of the coefficient of determination (R2), Kling Gupta efficiency (KGE), root mean squared error (RMSE), mean absolute error (MAE), and Nash-Sutcliff efficiency (NSE) were utilized. The results revealed that the hybrid methods used in this study could enhance the classical ANFIS performance in the analysis of the monthly and annual TDS of both stations. For more clarification, the models were ranked using the TOPSIS approach by simultaneously applying the effects of statistical parameters, temporal and spatial change factors, and convergence time. This approach significantly facilitated decision-making in ranking models. The ANFIS-ACOR annual model considering discharge had the best performance in the Vanyar Station; Furthermore, the ANFIS-ACOR monthly model ignoring discharge was outstanding in the Gotvand Station. In total, after utilizing two defined and proposed temporal and spatial change factors, the ANFIS-ACOR and ANFIS-DE hybrid models had the best and worst performance in TDS prediction, respectively. [ABSTRACT FROM AUTHOR]

Among the distribution network planning problems, feeder routing is one of the most important and challenging issues. Feeder routing is defined as the optimal determination of the feeders' routes by minimizing the investment cost and satisfying the network constraints. To the best knowledge of the authors, solving feeder routing problem in multi-objective framework is rarely investigated in the previous literature. This paper develops a bi-objective modeling and optimization framework to efficiently solve the feeder routing problem of distribution network considering cost and power losses. Since feeder routing is a complex, high-dimensional and combinatorial optimization problem, a multi-objective differential crow search algorithm (MO-CSAdif) is proposed to obtain a well-distributed and widely spread Pareto front. On a real large distribution network, simulation results confirm the usefulness of the proposed optimization framework. It can be drawn that MO-CSAdif can be a promising method to optimally solve the feeder routing problem. [ABSTRACT FROM AUTHOR]

This paper suggests day-ahead scheduling of microgrid with and without demand side management considering hydrogen storage and plug-in electric vehicles. This is a highly constrained mixed integer nonlinear programming problem which is solved by using quasi-oppositional fast convergence evolutionary programming (QOFCEP) technique. The concept of the quasi-opposition-based learning is incorporated in fast convergence evolutionary programming (FCEP) to improve the efficiency and quality of the solution. QOFCEP employs quasi-oppositional based learning (QOBL) for population initialization and generation jumping. The studied microgrid comprises three diesel generators, one mini-hydro power plant, two wind turbine generators, two solar PV plants, one hydrogen storage system and plug-in electric vehicles. Simulation outcomes of the suggested QOFCEP technique have been compared with those obtained by FCEP and differential evolution (DE). It is seen from numerical results that the cost obtained with DSM is about 0.81% lower than the cost obtained without DSM. It is also seen that the cost obtained from QOFCEP is about 0.52% and 0.78% lower than the cost obtained from FCEP and DE, respectively. The suggested QOFCEP technique has the ability to bestow superior-quality solution. [ABSTRACT FROM AUTHOR]

An improved algorithm based on tent chaotic map and difference mechanism is proposed to address the problem of poor population diversity and easily falling into local optimum in the lion swarm optimization algorithm. At first, an improved tent chaotic map is introduced to initialize the population position to increase diversity and ergodicity, thereby improving the global search capability. Second, a perturbation factor combined with a differential evolution mechanism is introduced to realize the adaptive positioning of the lioness and improve its ability to jump out of the local optimum. Following that, the proposed algorithm's superiority is tested on 10 multi-type benchmark functions and compared to six swarm intelligence algorithms. Finally, the improved algorithm is applied to optimize the initial weights and thresholds of BP neural networks. Then a new model is proposed and applied to the study of house price prediction. Experiments are conducted on two standard datasets, and results show that the proposed model's convergence speed, accuracy, and stability are better than the other three methods. On the Boston dataset, the mean square error of the training set and test set is 0.0016 and 0.0052, respectively, and the absolute error of more than 98% of the data is within 3%. On the Californian dataset, the mean square error of the training set and test set is 0.0149 and 0.0151, respectively, with more than 92.3% of the data within 5% absolute error, further validating the effectiveness, higher accuracy, and convergence performance of the proposed model. [ABSTRACT FROM AUTHOR]

The Whale Optimisation Algorithm (WOA) is a meta-heuristic model inspired by the hunting behaviours of humpback whales. Similar to many other meta-heuristic models, e.g., Particle Swarm Optimisation (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimisation (ACO), the WOA is susceptible to the issues of slow convergence and local optima. In this study, we address these short comings by first proposing an Enhanced WOA (EWOA) model for tackling single-objective optimisation. Specifically, EWOA integrates the WOA and Differential Evolution (DE). DE is a population-based algorithm that generates new candidate solutions by combining the existing ones, employing a simple yet robust formula. This amalgamation aids in generating diverse solutions during the exploration stage by utilising a non-linear coefficient vector, adaptive weight, and sub-population strategies. Furthermore, fast non-dominated sorting and crowding distance techniques from the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are incorporated into EWOA, resulting in a multi-objective EWOA (MOEWOA) model. We evaluate both EWOA and MOEWOA with a broad spectrum of benchmark functions. The results from 51 single-objective optimisation problems indicate the usefulness of EWOA in terms of a fast convergence rate and with increased performance. On the other hand, MOEWOA demonstrates a better convergence rate and an effective balance between convergence and diversity in 12 multi-objective optimisation problems. In addition, MOEWOA successfully solves 21 complex multi-objective constrained mechanical design problems, outperforming other compared algorithms at the 95% confidence level. The empirical outcomes of our study indicate the potential of EWOA and MOEWOA for undertaking complex, real-world optimisation problems. [ABSTRACT FROM AUTHOR]

Manual calculation of the compressive strength of concrete (CSC) is an expensive and time-consuming process. Soft computing methods outperform the statistical methods used to resolve these problems. Nonetheless, complicated prediction models are still incomplete and require more exploration. Artificial neural networks (ANNs) provide a better and faster technique featuring solitary hidden layers and have improved the generalization capacity. The present paper presents three ANN-based (shuffled complex evolution, evaporation rate based water cycle algorithm (ERWCA), and Cuckoo optimization algorithm) prediction models to anticipate the compressive strength of concrete efficiently. An available database from the UCI repository is employed to develop and access the model performance. A comparison is made between the prediction accuracies of the above three techniques. Using all models, a comparative investigation has been conducted to predict the compressive strength of concrete at the curing ages of 91, 56, and 28 days. The experimental findings obtained from the ERWCA-MLP method indicate its capability of robust CSC prediction. On average, this method achieves the minimum RMSE of 0.55314 and 0.43329 and R2 of 0.99803 and 0.99824. The statistical significance test and the comparative analysis of simulation results indicate the superiority of ERWCA-MLP in predicting the compressive strength of concrete. [ABSTRACT FROM AUTHOR]

Connected cyber and physical elements exchange information through feedback in a cyber-physical system (CPS). Since CPS oversees the infrastructure, it is an integral part of modern living and is viewed as crucial to the development of cutting-edge smart devices. As the number of CPSs rises, so does the need for intrusion detection systems (IDS). The use of metaheuristic methods and Artificial Intelligence for feature selection and classification can offer solutions to some of the problems caused by the curse of dimensionality. In this research, we present a blockchain-based approach to data security in which blocks are generated using the RSA hashing method. Using Differential Evolution (DE), we first select the blockchain-secured data, and then we partition that data into train and testing datasets to use for training and testing our model. It is also permitted for the validated model to use a deep belief network (DBN) to predict attacks. The purpose of the simulation is to evaluate the safety and precision of the classifications. It turns out that the proposed strategy not only improves classification accuracy but also makes the data more resistant to attacks. [ABSTRACT FROM AUTHOR]

The Fennec Fox algorithm (FFA) is a new meta-heuristic algorithm that is primarily inspired by the Fennec fox's ability to dig and escape from wild predators. Compared with other classical algorithms, FFA shows strong competitiveness. The "No free lunch" theorem shows that an algorithm has different effects in the face of different problems, such as: when solving high-dimensional or more complex applications, there are challenges such as easily falling into local optimal and slow convergence speed. To solve this problem with FFA, in this paper, an improved Fenna fox algorithm DEMFFA is proposed by adding sin chaotic mapping, formula factor adjustment, Cauchy operator mutation, and differential evolution mutation strategies. Firstly, a sin chaotic mapping strategy is added in the initialization stage to make the population distribution more uniform, thus speeding up the algorithm convergence speed. Secondly, in order to expedite the convergence speed of the algorithm, adjustments are made to the factors of the formula whose position is updated in the first stage, resulting in faster convergence. Finally, in order to prevent the algorithm from getting into the local optimal too early and expand the search space of the population, the Cauchy operator mutation strategy and differential evolution mutation strategy are added after the first and second stages of the original algorithm update. In order to verify the performance of the proposed DEMFFA, qualitative analysis is carried out on different test sets, and the proposed algorithm is tested with the original FFA, other classical algorithms, improved algorithms, and newly proposed algorithms on three different test sets. And we also carried out a qualitative analysis of the CEC2020. In addition, DEMFFA is applied to 10 practical engineering design problems and a complex 24-bar truss topology optimization problem, and the results show that the DEMFFA algorithm has the potential to solve complex problems. [ABSTRACT FROM AUTHOR]

The Electrocardiogram (ECG) signal analysis is an important and mandatory requirement to detect different heart diseases. After the pre-processing of the raw ECG signal classification and feature extraction are done to detect cardiac arrhythmia. In this study ECG signal classification is done by Differential Evaluation Algorithm (DEA) with Long short-term memory (LSTM). Several deep learning concepts are used for classification of ECG signal and heart arrhythmia detection. Before signal classification ECG signals are pre-processed for feature extraction. Initially ECG signal features are selected and then based on classification requirement the features are reduced for feature extraction. Usually ECG beat data carries a great deal of imbalance category which are rectified by recurrent neural network model with LSTM. The DEA is used to optimize the focal loss (FL). For the classification complex variation of the imbalance ECG beats works with the LSTM with the recommended focal loss. LSTM network gives emphasis on different ECG features and the focal loss is used to solve category imbalance by reducing the weights of the normal ECG beats. The simulation of the experiment is done by MIT-BIH arrhythmia database where the experiment result shows that LSTM network optimized with DEA overcomes the focal loss up to a certain level to avoid the problem of imbalance ECG dataset. In this experiment the accuracy achieved is 98.23%. The study works with the concept of dual structure of recurrent neural network model with multilayered dilated neural network model. [ABSTRACT FROM AUTHOR]

Hyper-parameter tuning of machine learning models has become a crucial task in achieving optimal results in terms of performance. Several researchers have explored the optimisation task during the last decades to reach a state-of-the-art method. However, most of them focus on batch or offline learning, where data distributions do not change arbitrarily over time. On the other hand, dealing with data streams and online learning is a challenging problem. In fact, the higher the technology goes, the greater the importance of sophisticated techniques to process these data streams. Thus, improving hyper-parameter self-tuning during online learning of these machine learning models is crucial. To this end, in this paper, we present MESSPT, an evolutionary algorithm for self-hyper-parameter tuning for data streams. We apply Differential Evolution to dynamically-sized samples, requiring a single pass-over of data to train and evaluate models and choose the best configurations. We take care of the number of configurations to be evaluated, which necessarily has to be reduced, thus making this evolutionary approach a micro-evolutionary one. Furthermore, we control how our evolutionary algorithm deals with concept drift. Experiments on different learning tasks and over well-known datasets show that our proposed MESSPT outperforms the state-of-the-art on hyper-parameter tuning for data streams. [ABSTRACT FROM AUTHOR]

Many practical problems involve the optimization of computationally expensive blackbox functions. The computational cost resulting from expensive function evaluations considerably limits the number of true objective function evaluations allowed in order to find a good solution. In this paper, we propose a clustering-based surrogate-assisted evolutionary algorithm, in which a clustering-based local search technique is embedded into the radial basis function surrogate-assisted evolutionary algorithm framework to obtain sample points which might be close to the local solutions of the actual optimization problem. The algorithm generates sample points cyclically by the clustering-based local search, which takes the cluster centers of the ultimate population obtained by the differential evolution iterations applied to the surrogate model in one cycle as new sample points, and these new sample points are added into the initial population for the differential evolution iterations of the next cycle. In this way the exploration and the exploitation are better balanced during the search process. To verify the effectiveness of the present algorithm, it is compared with four state-of-the-art surrogate-assisted evolutionary algorithms on 24 synthetic test problems and one application problem. Experimental results show that the present algorithm outperforms other algorithms on most synthetic test problems and the application problem. [ABSTRACT FROM AUTHOR]

This paper presents general theoretical studies on asymptotic convergence rate (ACR) for finite dimensional optimization. Given the continuous problem function and discrete time stochastic optimization process, the ACR is the optimal constant for control of the asymptotic behaviour of the expected approximation errors. Under general assumptions, condition ACR < 1 implies the linear behaviour of the expected time of hitting the ε - optimal sublevel set with ε → 0 + and determines the upper bound for the convergence rate of the trajectories of the process. This paper provides general characterization of ACR and, in particular, shows that some algorithms cannot converge linearly fast for any nontrivial continuous optimization problem. The relation between asymptotic convergence rate in the objective space and asymptotic convergence rate in the search space is provided. Examples and numerical simulations with use of a (1+1) self-adaptive evolution strategy and other algorithms are presented. [ABSTRACT FROM AUTHOR]

The present study focusses on optimising a single supported excavation pit to achieve a more economical design using finite element analyses. Two methods for automating the derivation of the excavation pit's necessary embedment depth are presented, which involve either embedment depth reduction using additional calculation phases or adapting the entire model with renewed discretisation. The bending moments as well as the earth pressure distribution along the wall show good agreement, indicating that both methods are suitable for application. Subsequently, the feasibility of using optimisation algorithms (Particle Swarm Optimisation and Differential Evolution) for dimensioning the single supported excavation pit regarding stress analysis of the wall is investigated. Therefore, the embedment depth and the position of the strut are varied for five different sheet pile walls and three different strut profiles. The results demonstrate that both algorithms perform well, particularly with a higher number of calculation steps. After varying iteration steps and population size, the Differential Evolution approach shows better performance compared to Particle Swarm Optimisation by means of finding the optimal solution after a lower number of computational steps. [ABSTRACT FROM AUTHOR]

The cement industry is one of the main sources of NOx emissions, and automated denitration systems enable precise control of NOx emission concentration. With non-linearity, time delay and strong coupling data in cement production process, making it difficult to maintain stable control of the denitration system. However, excessive pursuit of denitration efficiency is often prone to large ammonia escape, causing environmental pollution. A multi-objective prediction model combining time series and a bi-directional long short-term memory network (MT-BiLSTM) is proposed to solve the data problem of the denitration system and achieve simultaneous prediction of NOx emission concentration and ammonia escape value. Based on this model, a model predictive control framework is proposed and a control strategy of denitration system with multi-index model predictive control (MI-MPC) is built based on neural networks. In addition, the differential evolution (DE) algorithm is used for rolling optimization to find the optimal solution and to obtain the best control variable parameters. The control method proposed has significant advantages over the traditional PID (proportional integral derivative) controller, with a 3.84% reduction in overshoot and a 3.04% reduction in regulation time. Experiments prove that the predictive control framework proposed in this paper has better stability and higher accuracy, with practical research significance. [ABSTRACT FROM AUTHOR]

We consider geometry parameter estimation in industrial sawmill fan-beam X-ray tomography. In such industrial settings, scanners do not always allow identification of the location of the source–detector pair, which creates the issue of unknown geometry. This work considers an approach for geometry estimation based on the calibration object. We parametrise the geometry using a set of 5 parameters. To estimate the geometry parameters, we calculate the maximum cross-correlation between a known-sized calibration object image and its filtered backprojection reconstruction and use differential evolution as an optimiser. The approach allows estimating geometry parameters from full-angle measurements as well as from sparse measurements. We show numerically that different sets of parameters can be used for artefact-free reconstruction. We deploy Bayesian inversion with first-order isotropic Cauchy difference priors for reconstruction of synthetic and real sawmill data with a very low number of measurements. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm named surrogate ensemble assisted differential evolution with efficient dual differential grouping (SEADECC-EDDG) to deal with large-scale expensive optimization problems (LSEOPs) based on the CC framework. In the decomposition phase, our proposed EDDG inherits the framework of efficient recursive differential grouping (ERDG) and embeds the multiplicative interaction identification technique of Dual DG (DDG), which can detect the additive and multiplicative interactions simultaneously without extra fitness evaluation consumption. Inspired by RDG2 and RDG3, we design the adaptive determination threshold and further decompose relatively large-scale sub-components to alleviate the curse of dimensionality. In the optimization phase, the SEADE is adopted as the basic optimizer, where the global and the local surrogate model are constructed by generalized regression neural network (GRNN) with all historical samples and Gaussian process regression (GPR) with recent samples. Expected improvement (EI) infill sampling criterion cooperated with random search is employed to search elite solutions in the surrogate model. To evaluate the performance of our proposal, we implement comprehensive experiments on CEC2013 benchmark functions compared with state-of-the-art decomposition techniques. Experimental and statistical results show that our proposed EDDG is competitive with these advanced decomposition techniques, and the introduction of SEADE can accelerate the convergence of optimization significantly. [ABSTRACT FROM AUTHOR]

The introduction of a multi-population structure in differential evolution (DE) algorithm has been proven to be an effective way to achieve algorithm adaptation and multi-strategy integration. However, in existing studies, the mutation strategy selection of each subpopulation during execution is fixed, resulting in poor self-adaptation of subpopulations. To solve this problem, a dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning (RLDMDE) is proposed in this paper. By employing reinforcement learning, each subpopulation can adaptively select the mutation strategy according to the current environmental state (population diversity). Based on the population state, this paper proposes an individual dynamic migration strategy to "reward" or "punish" the population to avoid wasting individual computing resources. Furthermore, this paper applies two methods of good point set and random opposition-based learning (ROBL) in the population initialization stage to improve the quality of the initial solutions. Finally, to evaluate the performance of the RLDMDE algorithm, this paper selects two benchmark function sets, CEC2013 and CEC2017, and six engineering design problems for testing. The results demonstrate that the RLDMDE algorithm has good performance and strong competitiveness in solving optimization problems. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have been extensively explored due to their incredible capabilities and ever-growing field of applications. In cluster-based WSNs, cluster heads (CHs) deplete their energy quickly due to the extra workload of data aggregation and data forwarding as relay nodes (RNs) from the member sensor nodes (SNs). While placing the RNs in the application areas to form the clusters, coverage of all the SNs and connectivity amongst the RNs are essential for the proper function of the networks. Moreover, reducing the intra-cluster distances between the RNs and SNs is important to save the transmission energy of the SNs. Moreover, the problem of placement of RNs in cluster-based WSNs is known as NP-Hard. To address the challenge of deploying RNs while managing multiple conflicting objectives, we present a range of evolutionary algorithms (EAs) as potential solutions. These include techniques such as differential evolution (DE), particle swarm optimization (PSO), multi-objective DE (MODE), and multi-objective PSO (MOPSO). First, a mathematical formulation of the problem is given. The solution vectors are efficiently encoded. All the objective functions are efficiently derived to evaluate the solution vectors. An extensive simulation is performed over the proposed algorithms. The results are analyzed to determine the robust algorithm to be recommended for the studied problem. The simulated results claimed that the MODE is comparably better than others for the studied problem. The analysis of variance (ANOVA) is also performed, followed by a post-hoc analysis using the least significant difference (LSD) method. [ABSTRACT FROM AUTHOR]

This paper discusses chaos in dynamical systems as a positive aspect. It can be used to solve the problem of a spacecraft angular reorientation. Chaos can generate such a phase trajectory that will ensure the transition from the initial zone of a phase space to required one. As objects generating dynamical chaos in the paper strange chaotic attractors are considering. The goal is to initiate chaotic attractors in spacecraft attitude dynamics phase space. It is possible to achieve the target angular position of the spacecraft using the initiated chaotic attractors. An algorithm for chaotic attractors creation in the spacecraft attitude dynamics is developed in the paper, and five new chaotic attractors are discovered. [ABSTRACT FROM AUTHOR]

In the real world, data often exhibits high-dimensional and complex characteristics. In addition, an object may correspond to multiple class labels. Therefore, filtering and processing such data has become a hot research topic. Multi-label feature selection is one of the key preprocessing techniques for effectively solving redundant information. However, due to the high complexity of multi-label learning feature selection and involving optimization of multiple objectives, careful consideration is required to achieve effective optimization. To address these issues, this paper proposes a multiple reference points-based multi-objective feature selection for multi-label learning. Firstly, the neighborhood crossover strategy utilizes individuals in the same neighborhood for crossover, thereby enhancing the search effect within that decision space. The distribution-based mutation strategy adjusts the mutation probability of different features based on their distribution in selected candidate solutions to increase the likelihood of potentially useful features being selected. Secondly, the multiple reference points-based selection strategy uses a scalar function to uniformly evaluate offspring individuals and control their distance from each other through multiple reference points. This approach helps in selecting convergent solutions with balanced diversity. Finally, experimental results demonstrate that the proposed algorithm attains an optimal performance of 83% across fourteen widely used datasets, outperforming ten state-of-the-art algorithms in terms of effectiveness and performance in multi-label feature selection. [ABSTRACT FROM AUTHOR]

In product family design (PFD), deciding on a platform design strategy can be viewed as a multidisciplinary optimization problem that involves several factors, such as design variables, manufacturing costs, customizability, supplier reliability, and customer satisfaction. In this study, a multi-objective based differential evolution (MO-based DE) algorithm has been proposed for tackling the module-based PFD problem. The MO-based DE aims to find the best balance between many objectives, such as total production cost, diversity index, and a combination of other objectives (performance attributes). These objectives include commonality, modularity, and suppliers' reliability and all are aggregated to provide a goodness score. To effectively improve the DE's efficiency while solving such a complex optimization problem, the proposed DE integrates new elements such as (i) a novel solution representation, (ii) an improved heuristic technique for platform development, (iii) a weighted aggregation to combine different objectives, and (iv) a proposed platform-based crossover. To validate its performance, the proposed MO-based DE has been compared with (1) the standard DE to assess the effect of the incorporated new elements on DE's performance, and (2) well-known fast non-dominant sorting genetic algorithms NSGA-II and (3) NSGA-III for solving a real case study of a family of kettles. The experimental results confirmed the efficacy of the proposed MO-based DE as follows: in terms of average cost value, MO-based DE outperformed standard DE and NSGA-II by 26.40% and 11.69%, respectively. While in terms of goodness score, it achieved 20.69% and 8.05% better scores compared to standard DE and NSGA-II, respectively. Moreover, the proposed MO-based DE attained a very competitive performance against NSGA-III as it reached a better average cost and goodness score of 1.74% and 0.82%, respectively. [ABSTRACT FROM AUTHOR]

In the field of expensive optimization, numerous papers have proposed surrogate-assisted evolutionary algorithms (SAEAs) for a few thousand or even hundreds of function evaluations. However, in reality, low-cost simulations suffice for a lot of real-world problems, in which the number of function evaluations is moderately restricted, e.g., to several thousands. In such moderately restricted scenario, SAEAs become unnecessarily time-consuming and tend to struggle with premature convergence. In addition, tuning the SAEA parameters becomes impractical under the restricted budgets of function evaluations—in some cases, inadequate configuration may degrade performance instead. In this context, this paper presents a fast and auto-tunable evolutionary algorithm for solving moderately restricted expensive optimization problems. The presented algorithm is a variant of adaptive differential evolution (DE) algorithms, and is called emulation-based adaptive DE or EBADE. The primary aim of EBADE is to emulate the principle of sample-efficient optimization, such as that in SAEAs, by adaptively tuning the DE parameter configurations. Specifically, similar to Expected Improvement-based sampling, EBADE identifies parameter configurations that may produce expected-to-improve solutions, without using function evaluations. Further, EBADE incepts a multi-population mechanism and assigns a parameter configuration to each subpopulation to estimate the effectiveness of parameter configurations with multiple samples carefully. This subpopulation-based adaptation can help improve the selection accuracy of promising parameter configurations, even when using an expected-to-improve indicator with high uncertainty, by validating with respect to multiple samples. The experimental results demonstrate that EBADE outperforms modern adaptive DEs and is highly competitive compared to SAEAs with a much shorter runtime. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a robust optimizer designed for solving complex domain research problems in the computational intelligence community. In the present work, a multi-hybrid DE (MHDE) is proposed for improving the overall working capability of the algorithm without compromising the solution quality. Adaptive parameters, enhanced mutation, enhanced crossover, reducing population, iterative division and Gaussian random sampling are some of the major characteristics of the proposed MHDE algorithm. Firstly, an iterative division for improved exploration and exploitation is used, then an adaptive proportional population size reduction mechanism is followed for reducing the computational complexity. It also incorporated Weibull distribution and Gaussian random sampling to mitigate premature convergence. The proposed framework is validated by using IEEE CEC benchmark suites (CEC 2005, CEC 2014 and CEC 2017). The algorithm is applied to four engineering design problems and for the weight minimization of three frame design problems. Experimental results are analysed and compared with recent hybrid algorithms such as laplacian biogeography based optimization, adaptive differential evolution with archive (JADE), success history based DE, self adaptive DE, LSHADE, MVMO, fractional-order calculus-based flower pollination algorithm, sine cosine crow search algorithm and others. Statistically, the Friedman and Wilcoxon rank sum tests prove that the proposed algorithm fares better than others. [ABSTRACT FROM AUTHOR]

In this research work, a differential evolution-based method has been used to compress the deep neural network architectures. The compression is achieved by selecting the most dominant filters/nodes during the training of the model. The usefulness of filters is established by the test accuracy of the model. The experimental results demonstrate that the performance of proposed model compares fairly well with other state of art model compression techniques. Moreover, the compression achieved with VGG16 on MNIST, CIFAR-10 and CIFAR-100 datasets was 98.32, 98.5 and 93.54%, respectively. The corresponding compression achieved on ResNet50 was 85.24, 85.38 and 79.37%, while SqueezeNet which is already compressed model could also be compressed by 72.94, 73.77 and 44.59%, respectively. MobileNet, which is already a compact model developed for mobile applications, could also be compressed by 93.04, 93.74 and 76.37% on MNIST, CIFAR-10 and CIFAR-100 datasets. The loss in accuracy in compressed models turns out to be less than 2%. Further, the compressed models report acceleration in inference time being 80.79% on VGG16, 74.14% on ResNet50, 42.96% on MobileNet and 11.79% on SqueezeNet. [ABSTRACT FROM AUTHOR]

The explosion of wireless network services has led to a need for new technologies and resource allocation schemes. Device-to-device (D2D) can reduce the access pressure of base station (BS) equipment, and non-orthogonal multiple access (NOMA) can multiplex network spectrum resources. The integration of them can further improve the performance of multi-cell networks. However, users still face more interference in multi-cell networks. This paper analyzes the relationship between transmission power and serial interference cancellation decoding order. Aiming at maximizing the sum rate of D2Ds, we propose three feasible frameworks based on the differential evolution (DE) algorithm. First, we invoke a coevolution DE-based resource allocation (CDRA) framework, which encodes the RB assignment and power allocation into the same individual for evolution. Second, we propose an iteration-combining DE-based resource allocation (IDRA) framework, which adopts a two-step DE algorithm to solve optimal power allocation and RB assignment scheme iteratively. Lastly, we invoke the power-repairing DE-based resource allocation (PDRA) framework, which can perform power repair on individuals with failed evolution. Simulation results demonstrate that: (1) the integration of D2D and NOMA techniques is capable of enhancing the achievable sum rate of D2Ds; (2) the three proposed frameworks for multi-cell NOMA networks can effectively optimize the objective function. Compared with CDRA and IDRA, PDRA performs better in terms of convergence speed and maximum D2D rate. [ABSTRACT FROM AUTHOR]

In the real market, the item's demand is substantially affected by the item's selling price and frequency of advertising. This study focuses on an optimal ordering policy followed by advertising, pricing, and preservation policies. The present study incorporates the quantity-dependent ordering cost and time-dependent holding cost. A partial prepayment scheme is developed for inventory purchase decisions. The spoilage impact can be effectively reduced by an optimal investment in refrigeration. The optimal decision policy has been proposed by using three metaheuristic algorithms, namely particle swarm optimization, real-coded genetic algorithm, and differential evolution algorithm. A comparison is made for these metaheuristic schemes based on the numerical illustrations. The parameter sensitivity is performed to get insights of the variability in the indicators of the inventory model. [ABSTRACT FROM AUTHOR]

Vortex search (VS) algorithm is a recently proposed swarm intelligence or evolutionary algorithm for solving continuous optimization problems inspired by the behavior of whirlpool. In this study, an approach based on VS algorithm is proposed to deal with uncapacitated facility location problem (UFLP) which is a pure problem in binary domain. The update mechanism of VS algorithm is not sufficiently useful for solving the binary optimization problems; therefore, a binary form of VS method called modified binary vortex search (for short MBVS) is proposed for solving UFLPs. Three important changes have been carried out on basic VS algorithm such as (1) converting continuous values to binary values; (2) using genetic mutation operators for enhancing the exploration ability and (3) a local search mechanism for extending the exploitation ability. Based on these changes, MBVS has been tested on fifteen different UFLP instances. The UFLPs dealt with in this study is one of the famous binary optimization problems. It is widely used for comparing the performance of superior algorithms. Once an analysis of 10 different transfer functions, a genetic mutation operator, a local search parameter and population size have been made on proposed method; then, it has been compared with some binary metaheuristic methods and their variants: genetic algorithm (GA)-based approaches such as GA-SP, GA-TP and GA-UP; binary particle swarm optimization algorithm (BPSO); binary versions of artificial bee colony (ABC) algorithm such as binABC, DisABC and ABCbin; binary versions of differential evolution (DE) algorithm such as DisDe/rand and binDE and binary variants of the artificial algae algorithm (AAA) such as AAA-Tanh, AAA-Sig and binAAA methods. The experimental results and comparisons reveal that MBVS algorithm is highly competitive and robust optimizer for the problem addressed in this study. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to study an abstract system which consists of a parabolic hemivariational inequality with a Hilfer fractional evolution equation involving history-dependent operators, which is called a Hilfer fractional differential hemivariational inequality. We first show existence and a priori estimates for the parabolic hemivariational inequality. Then, by using the well-known Bohnenblust-Karlin fixed point theorem, we prove an existence theorem for the Hilfer fractional differential hemivariational inequality. [ABSTRACT FROM AUTHOR]

Benefiting from cloud computing's elasticity, scalability, and pay-per-use model, more and more scientific applications are deployed in or migrated to the cloud. Workflow scheduling still faces many challenges due to the growing scales of workflows and the diversified user QoS requirements. In this work, we propose a Mutation-driven and population Grouping Poor and Rich Optimization algorithm (MG-PRO) for scheduling workflows in the cloud to minimize makespan while satisfying the budget constraints. Specifically, we first adopt the middle-class sub-population into the original Poor and Rich Optimization algorithm (PRO), and develop the update strategies for rich and middle-class sub-populations to increase the randomness and search diversity. Secondly, the update mechanism for rich individuals is enriched, and the middle-class sub-population is guided by elite rich individuals, which enhances the information exchange and sharing among sub-populations. Finally, an evolution-aware mutation strategy is designed, where the mutation probability is adjusted adaptively as the dynamic monitoring of the population update process, and the two-point and triangular crossover-based mutations are used alternately to intervene the evolution trajectory according to the degree of objective optimization, resulting in a better balance between exploration and exploration. Extensive experiments are conducted on well-known scientific workflows with different types and scales through WorkflowSim. The experimental results show that, in most cases, MG-PRO outperforms existing algorithms in terms of constraint satisfiability, solution quality and stability. It can generate near-optimal solutions with the different budget constraints satisfied in a relatively short time, for example, the makespan resulting from MG-PRO is at most 59.95% shorter than other meta-heuristic algorithms, and at least 7.33% shorter than all its peers. [ABSTRACT FROM AUTHOR]

The fast growth of under developing mobile applications in recent years has emerged a diversity of delay-sensitive applications such as multimedia streaming, virtual reality, augmented reality, and online gaming applications to facilitate daily activities in different aspects of human life. Edge computing has been raised as an Internet-based distributed computing model to enable mobile devices to offload tasks to nearby edge servers rather than transfer them to remote cloud servers. A joint auto-scaling and task offloading approach in edge/cloud computing is proposed in this paper. Due to dynamic changes in usage and access to mobile applications over time, it requires addressing their workload fluctuations as challenging issues. The future workload is predicted using long short-term memory (LSTM) network, supported with the differential evolution (DE) algorithm for selecting the LSTM hyperparameters. A fuzzy Q-learning technique is also utilized to make scaling decisions at runtime, and a learning automata-based technique is used to make decisions on offloading tasks of mobile devices to edge/cloud layers. The proposed approach is validated using the iFogSim simulator under synthetic and real-world patterns. The results show that it achieves better performance in terms of execution time, energy consumption, and delay violation compared to the baseline approaches. [ABSTRACT FROM AUTHOR]

The substantial challenge posed by inherent hydrological forecasting uncertainty has critical implications for the optimization of real-time reservoir operations. In response, this study introduces a stochastic framework explicitly devised to comprehensively quantify the ramifications of hydrological forecasting uncertainty, notably its temporal correlations, on the outcomes of real-time reservoir optimization and risk assessment. Furthermore, this framework seeks to delineate the pivotal influence of incorporating or neglecting these temporal dynamics on the eventual results, while concurrently elucidating the underlying mechanisms governing these discernible influences. The framework adopts a comprehensive approach to simulating hydrological forecast uncertainty through ensemble forecasts and scenario trees, employing three methods (two Monte Carlo sampling-based methods and one Gaussian copula method) to generate inflow forecast ensembles. To improve the adaptability to uncertainties in inflow forecasts, the framework incorporates a transformation of the generated ensembles into scenario trees, serving as input for a stochastic optimization model that derives the final optimal decision based on optimizing the expected value of the objective function for all scenarios. Additionally, a parallel differential evolution algorithm is proposed to solve the stochastic optimization model efficiently. Risk assessment is performed to capture the uncertainty and corresponding risk associated with the reservoir optimal decision. The proposed framework is demonstrated in a flood control reservoir system in China, where several numerical experiments are conducted to explore the effect of forecast uncertainty level and temporal correlation on real-time reservoir optimal operation. Results show that the temporal correlation of inflows must be considered in both inflow stochastic simulation and reservoir stochastic optimization to avoid overestimating or underestimating operational risk, potentially leading to operation failures. By examining the risk simulation surface, reservoir operators can evaluate the robustness of operational decisions and make more reliable final decisions. [ABSTRACT FROM AUTHOR]

The tire pattern structure has a serious influence on automobile performance. In order to improve the tire performance, the strain energy density of tire tread under different working conditions was analyzed. The finite element model of longitudinal groove pattern of automobile tire was established to analyze the strain energy density under different working conditions, and the influence of parameter changes on the strain energy density of tire on dry road and slippery road was analyzed respectively. The multi-objective optimization model was established based on the finite element analysis results, and the function constraint conditions were set. Differential evolution method (DE) and non-dominated sorting genetic optimization method (NSGA-II) are used to design a multi-objective optimization program, which can quickly determine the optimization scheme after the input data. The finite element model was modified according to the optimization scheme and the strain energy density was analyzed. The results show that the performance of the optimized tire is greatly improved. The combination of finite element analysis technology and multi-objective optimization method can effectively reduce a lot of manpower, material resources and financial resources in mechanical product structure optimization. [ABSTRACT FROM AUTHOR]

Due to the mental stress of the human, the negative effects are known to be recent decades. Early detections of high level stresses are necessary to stop harmful consequences. Studies have proposed on wearable technologies which detect human stress. This study proposes stress detection systems which use physiological signals of people collected by wearable technologies and attached to human bodies. They can carry it during their daily routine. This work's proposed system includes removal of artifacts in bio signals and feature extractions from these cleaned signals. Since, DL (deep learning) based models are proven to be the best for these analyses, this article uses a random differential GWO (Grey wolf optimization) algorithm for feature extraction and a ML (machine learning) algorithm called RF (random forest) has been used for classification of the human body parameters like activities of the heart, conductance in skins and corresponding accelerometer signals. The proposed stress detection system is implemented with the real time data gathered from 21 participants. This approach can detect the stress of a human and prevent it from early stages with necessary lectures to avoid the negative effects. [ABSTRACT FROM AUTHOR]

The influence maximization problem that has caused great attention in social network analysis aims at selecting a small set of influential spreaders so that the information cascade triggered by the seed set is maximized. The majority of the existing works mainly focus on developing single-stage seeding strategies that would ignite all the seeds before the influence spread. However, it cannot depict the scenarios of the practical, where ones would like to make further decisions based on observed activation. In this paper, we investigate the policies for the intractable sequential influence maximization problem. A Q-learning-driven discrete differential evolution algorithm based on the reinforcement Q-learning model, which is treated as a parameter controller to adaptively adjust the parameters during the evolution of the algorithm, is proposed. The policy distributes the seeding actions over the spreading process by estimating the latest node status of the network dynamically. Extensive simulations are conducted on six social networks of the practical, and the findings demonstrate the superiority and effectiveness of the hybrid meta-heuristic algorithm compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The whale optimization algorithm has received much attention since its introduction due to its outstanding performance. However, like other algorithms, the whale optimization algorithm still suffers from some classical problems. To address the issues of slow convergence, low optimization precision, and susceptibility to local convergence in the whale optimization algorithm (WOA). Defining the optimization behavior of whale individuals as quantum mechanical behavior, a whale optimization algorithm based on atom-like structure differential evolution (WOAAD) is proposed. Enhancing the spiral update mechanism by introducing a sine strategy guided by the electron orbital center. Improving the random-walk foraging mechanism by applying mutation operations to both the electron orbital center and random individuals. Performing crossover operations between the newly generated individuals from the improved mechanisms and random dimensions, followed by a selection process to retain superior individuals. This accelerates algorithm convergence, enhances optimization precision, and prevents the algorithm from falling into local convergence. Finally, implementing a scouting bee strategy, where whale individuals progressively increase the number of optimization failures within a limited parameter L. When a threshold is reached, random initialization is carried out to enhance population diversity. Conducting simulation experiments to compare the improved algorithm with the whale optimization algorithm, other optimization algorithms, and other enhanced whale optimization algorithms. The experimental results indicate that the improved algorithm significantly accelerates convergence, enhances optimization precision, and prevents the algorithm from falling into local convergence. Applying the improved algorithm to five engineering design problems, the experimental results demonstrate that the improved algorithm exhibits good applicability. [ABSTRACT FROM AUTHOR]

The reservoir computing (RC) is increasingly used to learn the synchronization behavior of chaotic systems as well as the dynamical behavior of complex systems, but it is scarcely applied in studying synchronization of non-smooth chaotic systems likely due to its complexity leading to the unimpressive effect. Here proposes a simulated annealing-based differential evolution (SADE) algorithm for the optimal parameter selection in the reservoir, and constructs an improved RC model for synchronization, which can work well not only for non-smooth chaotic systems but for smooth ones. Extensive simulations show that the trained RC model with optimal parameters has far longer prediction time than those with empirical and random parameters. More importantly, the well-trained RC system can be well synchronized to its original chaotic system as well as its replicate RC system via one shared signal, whereas the traditional RC system with empirical or random parameters fails for some chaotic systems, particularly for some non-smooth chaotic systems. [ABSTRACT FROM AUTHOR]

The epidermal growth factor receptor (EGFR) Thr790 Met (T790M) mutation is responsible for approximately half of the acquired resistance to EGFR-tyrosine kinase inhibitor (TKI) in non-small-cell lung cancer (NSCLC) patients. Identifying patients at diagnosis who are likely to develop this mutation after first- or second-generation EGFR-TKI treatment is crucial for better treatment outcomes. This study aims to develop and validate a radiomics-based machine learning (ML) approach to predict the T790M mutation in NSCLC patients at diagnosis. We collected retrospective data from 210 positive EGFR mutation NSCLC patients, extracting 1316 radiomics features from CT images. Using the LASSO algorithm, we selected 10 radiomics features and 2 clinical features most relevant to the mutations. We built models with 7 ML approaches and assessed their performance through the receiver operating characteristic (ROC) curve. The radiomics model and combined model, which integrated radiomics features and relevant clinical factors, achieved an area under the curve (AUC) of 0.80 (95% confidence interval [CI] 0.79–0.81) and 0.86 (0.87–0.88), respectively, in predicting the T790M mutation. Our study presents a convenient and noninvasive radiomics-based ML model for predicting this mutation at the time of diagnosis, aiding in targeted treatment planning for NSCLC patients with EGFR mutations. [ABSTRACT FROM AUTHOR]

Oriented by the demands on high-speed and large-load picking up/handling in textile, electronics and other industries, a novel parallel manipulator (PM) achieving SCARA motion was designed. First, the mobility of the PM was obtained by dual space theory. Thereafter, the kinematic model was deduced in detail, and a dynamics model was developed using the Lagrangian formulation. The validity of the above models was verified by ADAMS software. Furthermore, the singularity existing in the workspace was analyzed. Subsequently, a homogeneous Jacobian matrix was constructed by the characteristic length factor, based on which dexterity index was defined. Aiming at the optimal performance in the singularity-free task workspace, a dimensional synthesis model incorporating kinematics and dynamics performance is proposed, which is efficiently tackled by differential evolution (DE) algorithm. The obtained result provides a sound foundation for the development of an engineering prototype. [ABSTRACT FROM AUTHOR]

The selection method of individuals is a crucial step in the mutation operator of differential evolution (DE). Typically, methods that select individuals with better fitness values are used to increase the exploitation ability of the algorithm. However, some researches have shown that incorporating distribution information of target space to measure diversity can improve the exploration ability of the algorithm. With this concept in mind, this paper presents an innovative approach called the adaptive stochastically ranking-based tournament selection method (ASR-TS). ASR-TS initially uses tournament selection and subsequently stochastically ranks selected individuals based on fitness and diversity measurements, leading to the determination of the tournament's winner. Furthermore, the stochastic ranking parameter is adaptively set based on the success rate of the previous generation to strike a balance between the exploitation and exploration abilities of the algorithm. The proposed ASR-TS method was tested on CEC 2013 benchmark functions in several original and improved DEs. To further validate the effectiveness of this method, the ASR-TS method was also tested on CEC 2022 benchmark functions as well as real-world problems. The experimental results demonstrate that the proposed ASR-TS method outperformed various other methods by a significant margin, which proves its efficiency and effectiveness in balancing exploration and exploitation. [ABSTRACT FROM AUTHOR]

In practical applications, the optimal solutions of multi-objective optimization are not unique. Some problems exist different Pareto Sets (PSs) in the decision space mapped to the same Pareto Front (PF) in the objective space, which are called multimodal multi-objective problems (MMOPs). To tackle this issue, this paper proposes a multimodal multi-objective optimization based on a local optimal neighborhood crowding distance differential evolution algorithm. First, an adaptive partitioning strategy in the initialization phase is proposed by using the characteristics of the heuristic stochastic search. That ensures the local optimal solution is quickly found among multiple PSs. Second, opposition-based learning is combined with differential mutation to generate vectors, which accelerate the convergence of the population to the optimal solution. Finally, a method for neighborhood crowding distances on different Pareto ranks is designed. The distance is computed by a weighted sum of Euclidean distances for the nearest neighbors. While reducing computational complexity, this strategy reflects realistic crowding degree. With these methods, balances the diversity performance of the decision and the objective space, while improving the search capability. Multiple PSs reveal the problem's potential characteristics and meet the needs of the decision-maker. The practical significance is verified by the application of actual distance minimization problem. According to experimental results, the proposed method can achieve a high level of comprehensive performance. [ABSTRACT FROM AUTHOR]

Automatic generation control is extensively used to regulate power plants in a modern area of the power system network. In this paper, automatic generation and frequency control in interconnected power system is presented. A multisource such as thermal, hydro, and gas-based power plant is considered in this study, which is carried out by incorporating nonlinearity like generation rate constants, HVDC link, and the conventional PID controller design. Further, an optimal setting of the PID controller is performed by employing evolutionary, and metaheuristic algorithm-based approaches such as differential evolution and firefly algorithm, respectively. With these algorithms, the proposed model has been tested with their performance evaluation and comparison characteristics are discoursed. The robustness of the proposed controllers is assessed based on comparative analyses to regulate the interconnected power network's frequency profile under different loading conditions. The stability analysis is performed using the Eigen and Nyquist plots to assess the proposed controllers' efficacy. Besides, the frequency control study is summarized with comparative assessment through various performance indices such as settling time, peak overshoot and undershoots under different operating conditions. Finally, the proposed control scheme, in the interconnected power system, is validated through a real-time digital simulation platform, i.e., OPAL-RT 5142. The comparison of simulation and real-time results demonstrates the effectiveness of the FA-optimized PID controller in comparison with that of the DE-optimized PID controller. [ABSTRACT FROM AUTHOR]