Automated grape leaf nutrition deficiency disease detection and classification Equilibrium Optimizer with deep transfer learning model.

Our approach includes picture preprocessing, feature extraction utilizing the SqueezeNet model, hyperparameter optimisation utilising the Equilibrium Optimizer (EO) algorithm, and classification utilising a Stacked Autoencoder (SAE) model. Each of these processes is carried out in a series of separate steps. During the image preprocessing stage, contrast limited adaptive histogram equalisations (CLAHE) is utilized to improve the contrasts, and Adaptive Bilateral Filtering (ABF) to get rid of any noise that may be present. The SqueezeNet paradigm is utilized to obtain relevant characteristics from the pictures that have been preprocessed, and the EO technique is utilized to fine-tune the hyperparameters. Finally, the SAE model categorises the diseases that affect the grape leaf. The simulation analysis of the EODTL-GLDC technique tested New Plant Diseases Datasets and the results were inspected in many prospects. The results demonstrate that this model outperforms other deep learning techniques and methods that are more often related to machine learning. Specifically, this technique was able to attain a precision of 96.31% on the testing datasets and 96.88% on the training data set that was split 80:20. These results offer more proof that the suggested strategy is successful in automating the detection and categorization of grape leaf diseases.

An Approach of Vibration Compensation for Atomic Gravimeter under Complex Vibration Environment.

Atomic gravimeter has been more frequently applied under complex and dynamic environments, but its measurement accuracy is seriously hampered by vibration-induced noise. In this case, vibration compensation provides a way to enhance the accuracy of gravity measurements by correcting the phase noise that resulted from the vibration of a Raman reflector, and improving the fitting of an interference fringe. An accurate estimation of the transfer function of vibration between the Raman reflector and the sensor plays a significant role in optimizing the effect of vibration compensation. For this reason, a vibration compensation approach was explored based on EO (equilibrium optimizer) for estimating the transfer function simplified model of a Raman reflector, and it was used to correct the interference fringe of an atomic gravimeter. The test results revealed that this approach greatly restored the actual vibration of the Raman reflector in a complex vibration environment. With a vibration compensation algorithm, it achieved the correction and fitting of the original interference fringe. In general, it dramatically reduced the RMSE (root mean square error) at the time of fitting and significantly improved the residual error in the gravity measurement. Compared with other conventional algorithms, such as GA (genetic algorithm) and PSO (particle swarm optimization), this approach realized a faster convergence and better optimization, so as to ensure more accurate gravity measurements. The study of this vibration compensation approach could provide a reference for the application of an atomic gravimeter in a wider and more complex environment.

From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution.

Conventional wind speed sensors face difficulties in measuring wind speeds at multiple points, and related research on predicting rotor effective wind speed (REWS) is lacking. The utilization of a lidar device allows accurate REWS prediction, enabling advanced control technologies for wind turbines. With the lidar measurements, a data-driven prediction framework based on empirical mode decomposition (EMD) and gated recurrent unit (GRU) is proposed to predict the REWS. Thereby, the time series of lidar measurements are separated by the EMD, and the intrinsic mode functions (IMF) are obtained. The IMF sequences are categorized into high-, medium-, and low-frequency and residual groups, pass through the delay processing, and are respectively used to train four GRU networks. On this basis, the outputs of the four GRU networks are lumped via weighting factors that are optimized by an equilibrium optimizer (EO), obtaining the predicted REWS. Taking advantages of the measurement information and mechanism modeling knowledge, three EMD-GRU prediction schemes with different input combinations are presented. Finally, the proposed prediction schemes are verified and compared by detailed simulations on the BLADED model with four-beam lidar. The experimental results indicate that compared to the mechanism model, the mean absolute error corresponding to the EMD-GRU model is reduced by 49.18%, 53.43%, 52.10%, 65.95%, 48.18%, and 60.33% under six datasets, respectively. The proposed method could provide accurate REWS prediction in advanced prediction control for wind turbines.

Development and application of equilibrium optimizer for optimal power flow calculation of power system.

This paper proposes an enhanced version of Equilibrium Optimizer (EO) called (EEO) for solving global optimization and the optimal power flow (OPF) problems. The proposed EEO algorithm includes a new performance reinforcement strategy with the Lévy Flight mechanism. The algorithm addresses the shortcomings of the original Equilibrium Optimizer (EO) and aims to provide better solutions (than those provided by EO) to global optimization problems, especially OPF problems. The proposed EEO efficiency was confirmed by comparing its results on the ten functions of the CEC'20 test suite, to those of other algorithms, including high-performance algorithms, i.e., CMA-ES, IMODE, AGSK and LSHADE_cnEpSin. Moreover, the statistical significance of these results was validated by the Wilcoxon's rank-sum test. After that, the proposed EEO was applied to solve the the OPF problem. The OPF is formulated as a nonlinear optimization problem with conflicting objectives and subjected to both equality and inequality constraints. The performance of this technique is deliberated and evaluated on the standard IEEE 30-bus test system for different objectives. The obtained results of the proposed EEO algorithm is compared to the original EO algorithm and those obtained using other techniques mentioned in the literature. These Simulation results revealed that the proposed algorithm provides better optimized solutions than 20 published methods and results as well as the original EO algorithm. The EEO superiority was demonstrated through six different cases, that involved the minimization of different objectives: fuel cost, fuel cost with valve-point loading effect, emission, total active power losses, voltage deviation, and voltage instability. Also, the comparison results indicate that EEO algorithm can provide a robust, high-quality feasible solutions for different OPF problems.

An Improved Equilibrium Optimizer with Application in Unmanned Aerial Vehicle Path Planning.

The unmanned aerial vehicle (UAV) path planning problem is a type of complex multi-constraint optimization problem that requires a reasonable mathematical model and an efficient path planning algorithm. In this paper, the fitness function including fuel consumption cost, altitude cost, and threat cost is established. There are also four set constraints including maximum flight distance, minimum flight altitude, maximum turn angle, and maximum climb angle. The constrained optimization problem is transformed into an unconstrained optimization problem by using the penalty function introduced. To solve the model, a multiple population hybrid equilibrium optimizer (MHEO) is proposed. Firstly, the population is divided into three subpopulations based on fitness and different strategies are executed separately. Secondly, a Gaussian distribution estimation strategy is introduced to enhance the performance of MHEO by using the dominant information of the populations to guide the population evolution. The equilibrium pool is adjusted to enhance population diversity. Furthermore, the Lévy flight strategy and the inferior solution shift strategy are used to help the algorithm get rid of stagnation. The CEC2017 test suite was used to evaluate the performance of MHEO, and the results show that MHEO has a faster convergence speed and better convergence accuracy compared to the comparison algorithms. The path planning simulation experiments show that MHEO can steadily and efficiently plan flight paths that satisfy the constraints, proving the superiority of the MHEO algorithm while verifying the feasibility of the path planning model.

A new hybrid equilibrium optimized SysFor based geospatial data mining for tropical storm-induced flash flood susceptible mapping.

Flash flood is one of the most dangerous hydrologic and natural phenomena and is considered as the top ranking of such events among various natural disasters due to their fast onset characteristics and the proportion of individual fatalities. Mapping the probability of flash flood events remains challenges because of its complexity and rapid onset of precipitation. Thus, this study aims to propose a state-of-the-art data mining approach based on a hybrid equilibrium optimized SysFor, namely, the HE-SysFor model, for spatial prediction of flash floods. A tropical storm region located in the Northwest areas of Vietnam is selected as a case study. For this purpose, 1866 flash-flooded locations and ten indicators were used. The results show that the proposed HE-SysFor model yielded the highest predictive performance (total accuracy = 93.8%, Kappa index = 0.875, F1-score = 0.939, and AUC = 0.975) and produced the better performance than those of the C4.5 decision tree (C4.5), the radial basis function-based support vector machine (SVM-RBF), the logistic regression (LReg), and deep learning neural network (DeepLNN) models in both the training and the testing phases. Among the ten indicators, elevation, slope, and land cover are the most important. It is concluded that the proposed model provides an alternative tool and may help for effectively monitoring flash floods in tropical areas and robust policies for decision making in mitigating the flash flood impacts.

Multi-strategy synthetized equilibrium optimizer and application.

Background: Improvement on the updating equation of an algorithm is among the most improving techniques. Due to the lack of search ability, high computational complexity and poor operability of equilibrium optimizer (EO) in solving complex optimization problems, an improved EO is proposed in this article, namely the multi-strategy on updating synthetized EO (MS-EO). Method: Firstly, a simplified updating strategy is adopted in EO to improve operability and reduce computational complexity. Secondly, an information sharing strategy updates the concentrations in the early iterative stage using a dynamic tuning strategy in the simplified EO to form a simplified sharing EO (SS-EO) and enhance the exploration ability. Thirdly, a migration strategy and a golden section strategy are used for a golden particle updating to construct a Golden SS-EO (GS-EO) and improve the search ability. Finally, an elite learning strategy is implemented for the worst particle updating in the late stage to form MS-EO and strengthen the exploitation ability. The strategies are embedded into EO to balance between exploration and exploitation by giving full play to their respective advantages. Result and Finding: Experimental results on the complex functions from CEC2013 and CEC2017 test sets demonstrate that MS-EO outperforms EO and quite a few state-of-the-art algorithms in search ability, running speed and operability. The experimental results of feature selection on several datasets show that MS-EO also provides more advantages.

OptiCoord: Advancing directional overcurrent and distance relay coordination with an enhanced equilibrium optimizer.

In this article, an improved optimization technique is used to get a solution to the problem of coordination between directional overcurrent relays (DOCR) and distance relays. An enhanced version of an equilibrium optimization algorithm (EO), referred to as EEO is proposed to solve this problem. The suggested approach optimises the parameter that regulates the balance between exploration and exploitation to identify the potential optimum solution while enhancing the EO algorithm's exploration properties. The main task for the EEO is to get the best settings. Also, the proposed algorithm shall maintain operation in sequence between the main and backup relays. The capability of the suggested EEO algorithm is assessed in 8-bus, IEEE thirty-bus, and IEEE 39-bus systems. The obtained results prove the effectiveness of the EEO technique in solving the coordination problem of the combined directional overcurrent relays and distance relays. Also, the results show the ability of the suggested algorithm to overcome the drawbacks of the traditional EO algorithm and achieve faster protection (the reduction ratio reaches about 12 % compared to the traditional EO.

Meta-heuristic optimization algorithms based feature selection for joint moment prediction of sit-to-stand movement using machine learning algorithms.

The sit-to-stand (STS) movement is fundamental in daily activities, involving coordinated motion of the lower extremities and trunk, which leads to the generation of joint moments based on joint angles and limb properties. Traditional methods for determining joint moments often involve sensors or complex mathematical approaches, posing limitations in terms of movement restrictions or expertise requirements. Machine learning (ML) algorithms have emerged as promising tools for joint moment estimation, but the challenge lies in efficiently selecting relevant features from diverse datasets, especially in clinical research settings. This study aims to address this challenge by leveraging metaheuristic optimization algorithms to predict joint moments during STS using minimal input data. Motion analysis data from 20 participants with varied mass and inertia properties are utilized, and joint angles are computed alongside simulations of joint moments. Feature selection is performed using the Manta Ray Foraging Optimization (MRFO), Marine Predators Algorithm (MPA), and Equilibrium Optimizer (EO) algorithms. Subsequently, Decision Tree Regression (DTR), Random Forest Regression (RFR), Extra Tree Regression (ETR), and eXtreme Gradient Boosting Regression (XGBoost Regression) ML algorithms are deployed for joint moment prediction. The results reveal EO-ETR as the most effective algorithm for ankle, knee, and neck joint moment prediction, while MPA-ETR exhibits superior performance for hip joint prediction. This approach demonstrates potential for enhancing accuracy in joint moment estimation with minimal feature input, offering implications for biomechanical research and clinical applications.

A hybrid equilibrium algorithm and pattern search technique for wind farm layout optimization problem.

The layout optimization-based model is a significant issue for increasing the utilization rate of the wind farm and minimizing its cost per unit of power. For the accurate and reliable wind farm layout optimization design, a novel algorithm based on the hybridization of equilibrium optimizer (EO) and pattern search (PS) technique, named EO-PS, is proposed in this paper. The proposed EO-PS operates in two phases. The first phase implements the EO to explore the search space and reach the promising regions by using an equilibrium pool of elite particles, which contributes to maintaining the diversity of solutions. The second phase integrates the PS to guide the searching towards better vicinities and achieve a high-quality solution by using its detecting and pattern movements to boost the exploitation ability of the proposed method in the last steps. The presented EO-PS algorithm is implemented to deal with single and multi-objective optimization aspects of wind farm layout optimization using different wind speed scenarios. Furthermore, the proposed algorithm is investigated on irregular land space in the Gulf of Suez-Red Sea in Egypt to achieve the optimal layout configuration of the wind farm, which is vital for possible practical planning trends. The comprehensive results and analyses have affirmed that the proposed EO-PS can achieve competitive performance compared to the other state-of-the-state methods, especially in terms of the solution' quality and reliability.

A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection.

Feature selection (FS) is a popular data pre-processing technique in machine learning to extract the optimal features to maintain or increase the classification accuracy of the dataset, which is a combinatorial optimization problem, requiring a powerful optimizer to obtain the optimum subset. The equilibrium optimizer (EO) is a recent physical-based metaheuristic algorithm with good performance for various optimization problems, but it may encounter premature or the local convergence in feature selection. This work presents a self-adaptive quantum EO with artificial bee colony for feature selection, named SQEOABC. In the proposed algorithm, the quantum theory and the self-adaptive mechanism are employed into the updating rule of EO to enhance convergence, and the updating mechanism from the artificial bee colony is also incorporated into EO to achieve appropriate FS solutions. In the experiments, 25 benchmark datasets from the UCI repository are investigated to verify SQEOABC, which is compared with several state-of-the-art metaheuristic algorithms and the variants of EO. The statistical results of fitness values and accuracy demonstrate that SQEOABC has better performance than the compared algorithms and the variants of EO. Finally, a real-world FS problem from COVID-19 illustrates the effectiveness and superiority of SQEOABC.

A chaos-based adaptive equilibrium optimizer algorithm for solving global optimization problems.

The equilibrium optimizer (EO) algorithm is a newly developed physics-based optimization algorithm, which inspired by a mixed dynamic mass balance equation on a controlled fixed volume. The EO algorithm has a number of strengths, such as simple structure, easy implementation, few parameters and its effectiveness has been demonstrated on numerical optimization problems. However, the canonical EO still presents some drawbacks, such as poor balance between exploration and exploitation operation, tendency to get stuck in local optima and low convergence accuracy. To tackle these limitations, this paper proposes a new EO-based approach with an adaptive gbest-guided search mechanism and a chaos mechanism (called a chaos-based adaptive equilibrium optimizer algorithm (ACEO)). Firstly, an adaptive gbest-guided mechanism is injected to enrich the population diversity and expand the search range. Next, the chaos mechanism is incorporated to enable the algorithm to escape from the local optima. The effectiveness of the developed ACEO is demonstrated on 23 classical benchmark functions, and compared with the canonical EO, EO variants and other frontier metaheuristic approaches. The experimental results reveal that the developed ACEO method remarkably outperforms the canonical EO and other competitors. In addition, ACEO is implemented to solve a mobile robot path planning (MRPP) task, and compared with other typical metaheuristic techniques. The comparison indicates that ACEO beats its competitors, and the ACEO algorithm can provide high-quality feasible solutions for MRPP.

Adaptive Guided Equilibrium Optimizer with Spiral Search Mechanism to Solve Global Optimization Problems.

The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems. Although the algorithm shows excellent exploitation capability, it still has some drawbacks, such as the tendency to fall into local optima and poor population diversity. To address these shortcomings, an enhanced EO algorithm is proposed in this paper. First, a spiral search mechanism is introduced to guide the particles to more promising search regions. Then, a new inertia weight factor is employed to mitigate the oscillation phenomena of particles. To evaluate the effectiveness of the proposed algorithm, it has been tested on the CEC2017 test suite and the mobile robot path planning (MRPP) problem and compared with some advanced metaheuristic techniques. The experimental results demonstrate that our improved EO algorithm outperforms the comparison methods in solving both numerical optimization problems and practical problems. Overall, the developed EO variant has good robustness and stability and can be considered as a promising optimization tool.

Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images.

The enlargement of the prostate gland in the reproductive system of males is considered a form of prostate cancer (PrC). The survival rate is considerably improved with earlier diagnosis of cancer; thus, timely intervention should be administered. In this study, a new automatic approach combining several deep learning (DL) techniques was introduced to detect PrC from MRI and ultrasound (US) images. Furthermore, the presented method describes why a certain decision was made given the input MRI or US images. Many pretrained custom-developed layers were added to the pretrained model and employed in the dataset. The study presents an Equilibrium Optimization Algorithm with Deep Learning-based Prostate Cancer Detection and Classification (EOADL-PCDC) technique on MRIs. The main goal of the EOADL-PCDC method lies in the detection and classification of PrC. To achieve this, the EOADL-PCDC technique applies image preprocessing to improve the image quality. In addition, the EOADL-PCDC technique follows the CapsNet (capsule network) model for the feature extraction model. The EOA is based on hyperparameter tuning used to increase the efficiency of CapsNet. The EOADL-PCDC algorithm makes use of the stacked bidirectional long short-term memory (SBiLSTM) model for prostate cancer classification. A comprehensive set of simulations of the EOADL-PCDC algorithm was tested on the benchmark MRI dataset. The experimental outcome revealed the superior performance of the EOADL-PCDC approach over existing methods in terms of different metrics.

Classification of Vogt-Koyanagi-Harada disease using feature selection and classification based on wide-field swept-source optical coherence tomography angiography.

Background: Vogt-Koyanagi-Harada (VKH) disease is a common and easily blinded uveitis entity, with choroid being the main involved site. Classification of VKH disease and its different stages is crucial because they differ in clinical manifestations and therapeutic interventions. Wide-field swept-source optical coherence tomography angiography (WSS-OCTA) provides the advantages of non-invasiveness, large-field-of-view, high resolution, and ease of measuring and calculating choroid, offering the potential feasibility of simplified VKH classification assessment based on WSS-OCTA. Methods: 15 healthy controls (HC), 13 acute-phase and 17 convalescent-phase VKH patients were included, undertaken WSS-OCTA examination with a scanning field of 15 × 9 mm2. 20 WSS-OCTA parameters were then extracted from WSS-OCTA images. To classify HC and VKH patients in acute and convalescent phases, two 2-class VKH datasets (HC and VKH) and two 3-class VKH datasets (HC, acute-phase VKH, and convalescent-phase VKH) were established by the WSS-OCTA parameters alone or in combination with best-corrected visual acuity (logMAR BCVA) and intraocular pressure (IOP), respectively. A new feature selection and classification method that combines an equilibrium optimizer and a support vector machine (called SVM-EO) was adopted to select classification-sensitive parameters among the massive datasets and to achieve outstanding classification performance. The interpretability of the VKH classification models was demonstrated based on SHapley Additive exPlanations (SHAP). Results: Based on pure WSS-OCTA parameters, we achieved classification accuracies of 91.61% ± 12.17% and 86.69% ± 8.30% for 2- and 3-class VKH classification tasks. By combining the WSS-OCTA parameters and logMAR BCVA, we achieved better classification performance of 98.82% ± 2.63% and 96.16% ± 5.88%, respectively. Through SHAP analysis, we found that logMAR BCVA and vascular perfusion density (VPD) calculated from the whole field of view region in the choriocapillaris (whole FOV CC-VPD) were the most important features for VKH classification in our models. Conclusion: We achieved excellent VKH classification performance based on a non-invasive WSS-OCTA examination, which provides the possibility for future clinical VKH classification with high sensitivity and specificity.

A new meta-heuristic optimization-based MPPT control technique for green energy harvesting from photovoltaic systems under different atmospheric conditions.

At present, a photovoltaic (PV) system takes responsibility to reduce the risk of global warming and generate electricity. However, the PV system faces numerous problems to track global maximum peak power (GMPP) owing to the nonlinear nature of the environment especially due to partial shading conditions (PSC). To solve these difficulties, previous researchers have utilized various conventional methods for investigations. Nevertheless, these methods have oscillations around the GMPP. Hence, a new metaheuristic method such as an opposition-based equilibrium optimizer (OBEO) algorithm is used in this work for mitigating the oscillations around GMPP. To find the effectiveness of the proposed method, it can be evaluated with other methods such as SSA, GWO, and P&O. As per the simulation outcome, the proposed OBEO method provides maximum efficiency against all other methods. The efficiency for the proposed method under dynamic PSC is 95.09% in 0.16 s, similarly, 96.17% for uniform PSC and 86.25% for complex PSC.

Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization.

Salp swarm algorithm (SSA) is a simple and effective bio-inspired algorithm that is gaining popularity in global optimization problems. In this paper, first, based on the pinhole imaging phenomenon and opposition-based learning mechanism, a new strategy called pinhole-imaging-based learning (PIBL) is proposed. Then, the PIBL strategy is combined with orthogonal experimental design (OED) to propose an OPIBL mechanism that helps the algorithm to jump out of the local optimum. Second, a novel effective adaptive conversion parameter method is designed to enhance the balance between exploration and exploitation ability. To validate the performance of OPLSSA, comparative experiments are conducted based on 23 widely used benchmark functions and 30 IEEE CEC2017 benchmark problems. Compared with some well-established algorithms, OPLSSA performs better in most of the benchmark problems.

A hybrid equilibrium optimizer algorithm for multi-level image segmentation.

Threshlod image segmentation is a classic method of color image segmentation. In this paper, we propose a hybrid equilibrium optimizer algorithm for multi-level image segmentation. When multi-level threshold method calculates the neighborhood mean and median of color image, it takes huge challenge to find the optimal threshold. We use the proposed method to optimize the multi-level threshold method and get the optimal threshold from the color image. In order to test the performance of the proposed method, we select the CEC2015 dataset as the benchmark function. The result shows that the proposed method improves the optimization ability of the original algorithm. And then, the classic images and wood fiber images are taken as experimental objects to analyze the segmentation result. The experimental results show that the proposed method has a good performance in Uniformity measure, Peak Signal-to-Noise Ratio and Feature Similarity Index and CPU time.

An efficient Equilibrium Optimizer for parameters identification of photovoltaic modules.

The use of solar photovoltaic systems (PVs) is increasing as a clean and affordable source of electric energy. The Pv cell is the main component of the PV system. To improve the performance, control, and evaluation of the PV system, it is necessary to provide accurate design and to define the intrinsic parameters of the solar cells. There are many methods for optimizing the parameters of the solar cells. The first class of methods is called the analytical methods that provide the model parameters using datasheet information or I-V curve data. The second class of methods is the optimization-based methods that define the problem as an optimization problem. The optimization problem objective is to minimize the error metrics and it is solved using metaheuristic optimization algorithms. The third class of methods is composed of a hybrid of both the analytical and the metaheuristic approaches, some parameters are computed by the analytical approach and the rest are found using metaheuristic optimization algorithms. Research in this area faces two challenges; (1) finding an optimal model for the parameters of the solar cells and (2) the lack of data about the photovoltaic cells. This paper proposes an optimization-based algorithm for accurately estimating the parameters of solar cells. It is using the Improved Equilibrium Optimizer algorithm (IEO). This algorithm is improved using the Opposition Based Learning (OBL) at the initialization phase of EO to improve its population diversity in the search space. Opposition-based Learning (OBL) is a new concept in machine learning inspired by the opposite relationship among entities. There are two common models for solar cells; the single diode model (SDM) and double diode model (DDM) have been used to demonstrate the capabilities of IEO in estimating the parameters of solar cells. The proposed methodology can find accurate solutions while reducing the computational cost. Compared to other existing techniques, the proposed algorithm yields less mean absolute error. The results were compared with seven optimization algorithms using data of different solar cells and PV panels. The experimental results revealed that IEO is superior to the most competitive algorithms in terms of the accuracy of the final solutions.