Differential Evolution Approach Research Articles

Inverse Kinematics of Robotic Manipulators Based on Hybrid Differential Evolution and Jacobian Pseudoinverse Approach

Robot manipulators play a critical role in several industrial applications by providing high precision and accuracy. To perform these tasks, manipulator robots require the effective computation of inverse kinematics. Conventional methods to solve IK often encounter significant challenges, such as singularities, non-linear equations, and poor generalization across different robotic configurations. In this work, we propose a novel approach to solve the inverse kinematics (IK) problem in robotic manipulators using a metaheuristic algorithm enhanced with a Jacobian step. Our method overcomes those limitations by selectively applying the Jacobian step to the differential evolution (DE) algorithm. The effectiveness and versatility of the proposed approach are demonstrated through simulations and real-world experimentation on a 5 DOF KUKA robotic arm.

An embedded differential evolution approach to adaptive covariance inflation with the ensemble Kalman filter

ABSTRACT The limited ensemble sizes and the neglected model errors in ensemble-based Data Assimilation (DA) usually lead to underestimation of the error variance, and covariance inflation ‘inflates’ the covariance of a forecast or analysis ensemble by some positive factor in each assimilation cycle. In practice, the value of the inflation factor is often set by trial and error. Thus, a variety of adaptive covariance inflation (ACI) algorithms have been proposed to improve assimilation performance by adjusting the optimal parameters. Most of these used traditional optimization algorithms, such as gradient methods and relaxation methods. However, Evolutionary Algorithms(EAs), which encompass a range of population-based stochastic search techniques widely used in various engineering optimization problems, are rarely seen in DA. In this study, we developed a new framework for adaptive inflation factors, introducing differential evolution (DE) algorithm into local ensemble Transform Kalman filter (LETKF) that can explore the most suitable/optimal inflation factors online. A ‘micro-DE’ based data assimilation method can obtain better assimilation results step by step without taking a much longer time. The feasibility and effectiveness of the algorithm is demonstrated in an idealized Lorenz-96 model with simulated observations. Under both perfect and imperfect-model scenarios, it is found that the ‘micro-DE’LETKF method is capable of outperforming the original LETKF with a considerable lower computational cost. The adaptive inflation factor is optimally updated at each assimilation cycle either around the upper bound or around the lower bound, which means that only an adaptive factor helps mitigate the ensemble collapse due to a lack of spread. However, the further study will consider the application of the methodology to more complex atmospheric models.

EBWO‐GE: An innovative approach to dynamic VM consolidation for cloud data centers

SummaryCloud data centers (CDCs) have revolutionized global computing by offering extensive storage and processing capabilities. Nevertheless, the environmental impact of these processes, including their substantial energy consumption and carbon emissions, calls for implementing more efficient techniques. Efficient virtual machine (VM) consolidation is crucial in optimizing resource utilization and reducing energy consumption. Current methods for enhancing energy efficiency often lead to issues such as service level agreements (SLAs) violations and quality of services (QoS) degradation. This study presents a novel approach to host selection using a grey‐extreme (GE) machine learning model, which accurately predicts over and underutilized hosts. In addition, a VM placement technique called enhanced black widow optimization (EBWO) utilizes black widow optimization heuristic techniques and a differential evolutionary approach to optimize VM placement. The proposed dynamic VM consolidation technique optimizes energy utilization while meeting strict SLA requirements and enhancing QoS metrics in CDCs. Extensive analyses were conducted using the Cloudsim toolkit to validate the approach's effectiveness. These analyses encompassed conditions such as random workloads in heterogeneous environments. The simulation results showed that GE‐EBWO outperforms other techniques and improves energy efficiency by 12%–15%. In addition, it significantly decreases VM migrations by 11%–14% compared to other advanced methods. The study validates the practicality of the proposed technique in moving towards environmentally friendly CDCs.

Assessment of Femoral Head Sphericity Using Coordinate Data through Modified Differential Evolution Approach

Coordinate measuring machines (CMMs) are utilized to acquire coordinate data from manufactured surfaces for inspection reasons. These data are employed to gauge the geometric form errors associated with the surface. An optimization procedure of fitting a substitute surface to the measured points is applied to assess the form error. Since the traditional least-squares approach is susceptible to overestimation, it leads to unreasonable rejections. This paper implements a modified differential evolution (DE) algorithm to estimate the minimum zone femoral head sphericity. In this algorithm, opposition-based learning is considered for population initialization, and an adaptive scheme is enacted for scaling factor and crossover probability. The coefficients of the correlation factor and the uncertainty propagation are also measured so that the result’s uncertainty can be determined. Undoubtedly, the credibility and plausibility of inspection outcomes are strengthened by evaluating measurement uncertainty. Several data sets are used to corroborate the outcome of the DE algorithm. CMM validation shows that the modified DE algorithm can measure sphericity with high precision and consistency. This algorithm allows for an adequate initial solution and adaptability to address a wide range of industrial problems. It ensures a proper balance between exploitation and exploration capabilities. Thus, the suggested methodology, based on the computational results, is feasible for the online deployment of the sphericity evaluation. The adopted DE strategy is simple to use, has few controlling variables, and is computationally less expensive. It guarantees a robust solution and can be used to compute different form errors.

Multi-population differential evolution approach for feature selection with mutual information ranking

Feature selection is a crucial aspect of data preprocessing because of the significant effect of redundant features on classification performance and the extensive computational resources required. Evolutionary algorithm-based feature-selection methods have shown remarkable results in determining the optimal feature subset. To further enhance classification performance, this paper proposes a novel multi-population differential evolution approach for feature selection with mutual information ranking (MI-MPODE). Firstly, population preprocessing guided by mutual information is employed to reduce the dimensionality of the initial feature space. Then, the feature subset obtained from mutual information serves as the initial population for MI-MPODE. MI-MPODE incorporates a novel multi-population information-sharing mechanism, with common individuals from three layers contributing to inter-subpopulation information sharing. Additionally, an individual enhancement strategy is proposed to handle the variations of individuals in the population, and a Lens imaging opposition-based learning method is adopted to improve the algorithm’s optimization capability. MI-MPODE is compared with several state-of-the-art evolutionary algorithm-based feature selection methods through experimental comparisons. The results show that MI-MPODE outperforms all comparison algorithms on more than half of the datasets, with a significant reduction in the number of features used, demonstrating a significant advantage over competitors.

Differential evolutionary optimization fuzzy entropy for gland segmentation based on breast mammography imaging

ObjectiveThe integration of fuzzy entropy into image segmentation techniques has proven effective in addressing issues related to ambiguous boundaries and uncertainties in complex image structures. This study aims to improve breast mammography image segmentation by optimizing minimum fuzzy entropy. The primary challenges identified are the presence of glandular burrs and irregular edges, as well as premature convergence and local optima in entropy optimization. MethodsWe propose an enhanced differential evolution algorithm combined with fuzzy entropy to address these challenges. This algorithm specifically targets glandular burrs, irregular edges, premature convergence, and local optimal entropy issues. The algorithm's performance was evaluated using breast mammography images representing four types of glands and benchmarked against five state-of-the-art algorithms. ResultsExperimental results demonstrate that the proposed algorithm significantly improves segmentation accuracy compared to the other advanced algorithms. It effectively resolves issues related to glandular burrs, irregular edges, premature convergence, and local optimal entropy, leading to superior segmentation outcomes. ConclusionThe proposed algorithm, which combines an improved differential evolution approach with fuzzy entropy, delivers the best segmentation results for breast gland segmentation. It successfully overcomes the challenges of glandular burrs, irregular edges, premature convergence, and local optimal entropy, thus enhancing breast mammography image segmentation.

Design, simulation and energy dissipation evaluating of a new lead shear damper

This study presents a novel design for a lead shear damper (LSD) that features lead blocks encased in laminated steel and rubber plates, intersected by X-shaped steel sticks. It combines the excellent energy dissipation and fatigue resistance of lead with the uniform yielding capabilities of X-shaped steel sticks along their height. Cyclic loading testing and fatigue tests are conducted in order to determine the mechanical properties of the LSD. The test results reveal that the LSD has a variable yielding force, and exhibits stiffness hardening when the damper's deformation direction deviates from the origin. However, when the deformation direction returns to the origin, there is no stiffness hardening observed, and the resistant force remains constant. Furthermore, this LSD can work effectively despite the failure of energy dissipation parts, which improves hysteretic energy dissipation under large deformation. In order to replicate the hysteretic behaviour, a uniaxial constitutive model is formulated and its parameters are determined by the utilization of the differential evolution approach, based on test data. The observed strong concurrence between the experimental and simulated hysteresis curves serves as compelling evidence for the efficacy of the differential evolution method. Meanwhile, a three-story building from the third-generation benchmark problem is employed as the testbed structure to examine the energy dissipation capacity of the developed dampers. Nonlinear seismic analysis on the benchmark model equipped with the proposed dampers is also conducted, and the results are compared to those fitted with conventional dampers, such as mild dampers and friction dampers. The findings of this study indicate that the performance of the LSD in reducing structural displacements is superior to that of mild dampers and friction dampers.

Optimizing CP-ABE Decryption in Urban VANETs: A Hybrid Reinforcement Learning and Differential Evolution Approach

Optimizing CP-ABE Decryption in Urban VANETs: A Hybrid Reinforcement Learning and Differential Evolution Approach

Multiobjective Binary Differential Approach with Parameter Tuning for Discovering Business Process Models: MoD-ProM.

Process discovery approaches analyze the business data to automatically uncover structured information, known as a process model. The quality of a process model is measured using quality dimensions, completeness (replay fitness), preciseness, simplicity, and generalization. Traditional process discovery algorithms usually output a single process model. A single model may not accurately capture the observed behavior and overfit the training data. We have formed the process discovery problem in a multiobjective framework that yields several candidate solutions for the end user who can pick a suitable model based on the local environmental constraints (possibly varying). We consider the Binary Differential Evolution approach in a multiobjective framework for the task of process discovery. The proposed method employs dichotomous crossover/mutation operators. The parameters are tuned using grey relational analysis combined with the Taguchi approach. We have compared the proposed approach with the well-known single-objective algorithms and state-of-the-art multiobjective evolutionary algorithm-Nondominated Sorting Genetic Algorithm (NSGA-II). Additional comparison via computing a weighted average of the quality dimensions is also undertaken. Results show that the proposed algorithm is computationally efficient and produces diversified candidate solutions that score high on the fitness functions. It is shown that the process models generated by the proposed approach are superior to or at least as good as those generated by the state-of-the-art algorithms.

Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN

The COVID-19 disease caused by coronavirus is constantly changing due to the emergence of different variants and thousands of people are dying every day worldwide. Early detection of this new form of pulmonary disease can reduce the mortality rate. In this paper, an automated method based on machine learning (ML) and deep learning (DL) has been developed to detect COVID-19 using computed tomography (CT) scan images extracted from three publicly available datasets (A total of 11,407 images; 7397 COVID-19 images and 4010 normal images). An unsupervised clustering approach that is a modified region-based clustering technique for segmenting COVID-19 CT scan image has been proposed. Furthermore, contourlet transform and convolution neural network (CNN) have been employed to extract features individually from the segmented CT scan images and to fuse them in one feature vector. Binary differential evolution (BDE) approach has been employed as a feature optimization technique to obtain comprehensible features from the fused feature vector. Finally, a ML/DL-based ensemble classifier considering bagging technique has been employed to detect COVID-19 from the CT images. A fivefold and generalization cross-validation techniques have been used for the validation purpose. Classification experiments have also been conducted with several pre-trained models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier technique with fused feature has provided state-of-the-art performance with an accuracy of 99.98%.

Exploring a novel HE image segmentation technique for glioblastoma: A hybrid slime mould and differential evolution approach

Glioblastoma is a primary brain tumor with high incidence and mortality rates, posing a significant threat to human health. It is crucial to provide necessary diagnostic assistance for its management. Among them, Multi-threshold Image Segmentation (MIS) is considered the most efficient and intuitive method in image processing. In recent years, many scholars have combined different metaheuristic algorithms with MIS to improve the quality of Image Segmentation (IS). Slime Mould Algorithm (SMA) is a metaheuristic approach inspired by the foraging behavior of slime mould populations in nature. In this investigation, we introduce a hybridized variant named BDSMA, aimed at overcoming the inherent limitations of the original algorithm. These limitations encompass inadequate exploitation capacity and a tendency to converge prematurely towards local optima when dealing with complex multidimensional problems. To bolster the algorithm’s optimization prowess, we integrate the original algorithm with a robust exploitative operator called Differential Evolution (DE). Additionally, we introduce a strategy for handling solutions that surpass boundaries. The incorporation of an advanced cooperative mixing model accelerates the convergence of BDSMA, refining its precision and preventing it from becoming trapped in local optima. To substantiate the effectiveness of our proposed approach, we conduct a comprehensive series of comparative experiments involving 30 benchmark functions. The results of these experiments demonstrate the superiority of our method in terms of both convergence speed and precision. Moreover, within this study, we propose a MIS technique. This technique is subsequently employed to conduct experiments on IS at both low and high threshold levels. The effectiveness of the BDSMA-based MIS technique is further showcased through its successful application to the medical image of brain glioblastoma. The evaluation of these experimental outcomes, utilizing image quality metrics, conclusively underscores the exceptional efficacy of the algorithm we have put forth.

The Markovian Multiagent Monte-Carlo method as a differential evolution approach to the SCF problem for restricted and unrestricted Hartree-Fock and Kohn-Sham-DFT.

In this paper we present the Markovian Multiagent Monte-Carlo Second Order Self-Consistent Field Algorithm (M3-SOSCF). This algorithm provides a highly reliable methodology for converging SCF calculations in single-reference methods using a modified differential evolution approach. Additionally, M3 is embarrassingly parallel and modular in regards to Newton-Raphson subroutines. We show that M3 is able to surpass contemporary SOSCFs in reliability, which is illustrated by a benchmark employing poor initial guesses and a second benchmark with SCF calculations which face difficulties using standard SCF algorithms. Furthermore, we analyse inherent properties of M3 and show that in addition to its robustness and efficiency, it is more user-friendly than current SOSCFs.

Numerical Optimisation of Excavation Pit Design Using Finite Element Analyses

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.

Parameter estimation in non-linear chemical processes: an opposite point-based differential evolution (OPDE) approach

Abstract In recent years, evolutionary algorithms have been gaining popularity for finding optimal solutions to non-linear multimodal problems encountered in many engineering disciplines. Differential evolution (DE), an evolutionary algorithm, is a novel optimization method capable of handling nondifferentiable, non-linear, and multimodal objective functions. DE is an efficient, effective, and robust evolutionary optimization method. Still, DE takes large computational time to optimize the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This paper introduces a modification to the original DE that enhances the convergence rate without compromising solution quality. The proposed opposite point-based differential evolution (OPDE) algorithm utilizes opposite point-based population initialization, in addition to random initialization. Such an improvement reduces computational effort. The OPDE has been applied to benchmark test functions and high-dimensional non-linear chemical engineering problems. The proposed method of population initialization accelerates the convergence speed of DE, as indicated by the results obtained using benchmark test functions and non-linear chemical engineering problems.

Simple parameterization method using differential evolution algorithm to estimate state of charge for Li‐ion batteries and packs for electric vehicles

AbstractAn accurate method is proposed to measure the electrical parameters describing the experimental charge‐discharge curves of Li‐ion batteries and a pack through an equivalent electric dynamic model based on heuristic optimization algorithms (eg, differential evolution approach). The accuracy of these parameters is critical for on‐board state of charge and Health (SOH) estimations of numerous applications and devices. The procedure is constructed, based on the simple Thévenin electrical model. Charge‐discharge experimental curves collected for different chemistry and capacity batteries are described with the model, revealing better estimation results with respect to other algorithms such as impedance‐based methodologies (decreasing the error until 64.19%) and particle swarm optimization based approaches (decreasing the error until 94.02%). The proposal was assessed with different chemistries, capacities and with a 28 battery pack (LiFeMnPO, 100 Ah) used in an electric vehicle.

Handling imbalanced class in melanoma: Kemeny–Young rule based optimal rank aggregation and Self-Adaptive Differential Evolution Optimization

Melanoma, despite its relatively low incidence compared to other types of skin cancer, accounts for a significant proportion of skin cancer-related deaths. Early detection of melanoma is crucial for improving patient survival rates. Deep learning algorithms, which heavily rely on data, are widely used in melanoma detection. However, the performance of these algorithms is greatly influenced by the distribution of the dataset, particularly class imbalance. In this manuscript, the authors present a novel method based on the Kemeny–Young rule for optimal rank aggregation to address the class imbalance problem in melanoma detection. The proposed approach aims to reduce class bias and enhance overall classification accuracy. Furthermore, a cost-sensitive learning approach is introduced to improve the classifier’s ability to handle class imbalance effectively. This novel cost-sensitive learning method utilizes Self-Adaptive Differential Evolution Optimization to determine optimal weights for each class. Our approach differs from traditional methods that assign weights based on predefined criteria. To evaluate the effectiveness of the proposed methods, extensive experiments and ablation studies are conducted on the highly imbalanced ISIC 2020 dataset, which is widely used in melanoma detection research. The Kemeny–Young rule-based majority voting achieves an overall error rate of 2.44%, while the cost-sensitive learning based on the Self-Adaptive Differential Evolution approach achieves an even lower error rate of 1.99%. Moreover, the proposed method achieves a sensitivity of 87.93% and a specificity of 98.19%. These experimental results demonstrate the competitiveness and effectiveness of the proposed methods in addressing the challenges posed by class imbalance and improving the accuracy of melanoma detection. By effectively mitigating class imbalance, these methods improve the accuracy and reliability of melanoma detection, thus offering valuable insights for developing advanced computer-aided diagnosis systems in dermatology. The relevant codes for our proposed approach are publicly available at: https://github.com/ctrl-gaurav/Handling-Imbalanced-Class-in-Melanoma.

Multiobjective Differential Evolution for Feature Selection in Classification.

Feature selection aims to reduce the number of features and improve the classification accuracy, which is an essential step in many real-world problems. Multiple feature subsets with different features selected can achieve similar or even the same objective values (e.g., maximize the classification accuracy and minimize the number of selected features). This means the optimal feature subsets of a classification problem may not be unique. However, most existing feature selection methods do not take into consideration finding multiple optimal feature subsets. In this article, a multiobjective differential evolution approach is developed to search for multiple optimal feature subsets. The contributions are three-fold. First, to provide a good starting point, an initialization method considering feature relevance is proposed. Second, a clustering method is used to divide the whole population into multiple subpopulations. In each of these subpopulations, a subarchive utilizes a developed crowding distance to ensure diversity by considering both the search space and the objective space. Finally, the nondominated solutions from all the subarchives are retained in another archive to guide the evolutionary feature selection process, together with an improved hypervolume contribution indicator. The experiments on 14 datasets of varying difficulty show that the proposed approach can evolve a better Pareto front of feature subsets compared with seven other state-of-the-art methods as well as find different feature subsets with similar or the same classification performance.

Enhanced Differential Evolution and Particle Swarm Optimization Approaches for Discovering High Utility Itemsets

Mining patterns from High-utility itemsets (HUIs) have been exploited recently in place of frequent itemset mining (FIMs) or association-rule mining (ARMs) as they highlight profitability of products where quantity and profits are taken into account. Several techniques for HUIs have been proposed and they encounter exponential search spaces which have more distinct items or voluminous databases. Alternatively, Evolutionary Computations (ECs)-based meta-heuristics algorithms can be effective in solving issues in HUIs since a set of near-optimal solutions can be obtained within restricted periods. Current ECs-based techniques consume more time to identify HUIs in transactional databases, discover unacceptable combinations of HUIs, and finally fail to discover HUIs when neighborhood searches are not executed locally and globally. To overcome these challenges, a HUI mining algorithm based on Differential Evolution (DE) and Particle Swarm Optimization (PSO) using multiple strategies including elitism, population diversifications, exclusive preservations, and neighborhood exploration techniques has been proposed. Thus, this work defines mining patterns based on DE and PSO to identify HUIs in voluminous transactional databases. The HUIM-DE-PSO-DE algorithm proposed in this work discovers more number of HUIs which is revealed in experimental results obtained from a set of benchmark data instances. Results are compared with existing approaches using several performance metrics including convergence speeds, minimum utility threshold values, and execution time consumed.

Community Agricultural Reservoir Construction and Water Supply Network Design in Ubon Ratchathani, Thailand, Using Adjusted Variable Neighborhood Strategy Adaptive Search

Agricultural sectors all over the world are facing water deficiencies as a result of various factors. Countries in the Greater Mekong Subregion (GMS) in particular depend on the production of agricultural products; thus, drought has become a critical problem in such countries. The average water level in the lower part of the Mekong River has been decreasing dramatically, resulting in the wider agricultural area of the Mekong watershed facing a lack of water for production. The construction of community reservoirs and associated water supply networks represents a strategy that can be used to address drought problems in the GMS. This study aims to solve the agricultural community reservoir establishment and water supply network design (CR–WSND) problem in Khong Chiam, Ubon Ratchathani, Thailand—a city located in the Mekong Basin. The CR–WSND model is formulated using mixed-integer programming (MIP) in order to minimize the cost of reservoir construction and water irrigation. An adjusted variable neighborhood strategy adaptive search (A-VaNSAS) is applied to a real-world scenario involving 218 nodes, and its performance is compared with that of the original variable neighborhood strategy adaptive search (VaNSAS), differential evolution (DE), and genetic algorithm (GA) approaches. An improved box selection formula and newly designed improvement black boxes are added to enhance the quality beyond the original VaNSAS. The results reveal that the quality of the solution from A-VaNSAS is significantly better than those of GA, DE, and VaNSAS (by 6.27%, 9.70%, and 9.65%, respectively); thus, A-VaNSAS can be used to design a community reservoir and water supply network effectively.

Multi-Objective Modified Differential Evolution Methods for the Optimal Parameters of Aluminum Friction Stir Welding Processes of AA6061-T6 and AA5083-H112

This study introduces a modified differential evolution approach (MoDE) for evaluating the optimal objective and parameter values of the friction stir welding (FSW) process of dissimilar materials: AA5083 and AA6061. The aim of this study is to investigate the ultimate (UTS), maximum hardness (MH), and minimum heat input (HI) of the weld zone. The controlled welding parameters were shoulder diameter, rotation speed, welding speed, tilt angle, pin type, reinforcement particle type, and tool pin movement direction. The D-optimal experimental design method was used to create the experiment and obtain the mathematical model for optimizing the targeted objectives. The optimal rotational speed, welding speed, shoulder diameter, tilt angle, pin-type, additive type, and tool pin movement are 1162.81 rpm, 52.73 mm/min, 21.17 mm, 2.37 degrees, straight cylindrical, silicon carbide, and straight movement direction, respectively. The optimal values for UTS, MH, and HI are 264.68 MPa, 105.56 HV, and 415.26 °C, respectively. The MoDE outcome exceeded particle swarm optimization (PSO), the original differential evolution algorithm (DE), and the D-optimal design (experiment) results. The MoDE provides better UTS, MH, and HI than other approaches by an average of 8.04%, 4.44%, and 2.44%, respectively. In particular, when comparing results produced by using various approaches, we discovered that the MoDE results are 7.45%, 4.45%, and 3.50% better than PSO, DE, and the experimental results, respectively. All methods were evaluated for their reliability by comparing the results of actual experiments to those predicted by theory, and we discovered that the MoDE yielded the smallest percentage difference between the two, at 1.49%, while PSO and DE yielded differences of 5.19% and 3.71%, respectively.