Elite Opposition-based Learning Research Articles

Multi-performance optimization for AWJ drilling process in cutting of ceramic tile: BBD with EOBL-GOA algorithm

PurposeThe goal of this study is to determine the values of the process parameters that should be used during the machining of ceramic tile using the abrasive water jet (AWJ) process in order to achieve the lowest possible values for surface roughness and kerf taper angle.Design/methodology/approachIn the present work, ceramic tile is processed by the AWJ process and experimental data were recorded using the RSM approach based Box–Behnken design matrix. The input process factors were water jet pressure, jet traverse speed, abrasive flow rate and standoff distance, to determine the surface roughness and kerf taper angle. ANOVA was used to check the adequacy of model and significance of process parameters. Further, the elite opposition-based learning grasshopper optimization (EOBL-GOA) algorithm was implemented to identify the simultaneous optimization of multiple responses of surface roughness and kerf taper angle in AWJ.FindingsThe suggested EOBL-GOA algorithm is suitable for AWJ of ceramic tile, as evidenced by the error rate of ±2 percent between experimental and predicted solutions. The surfaces were evaluated with an SEM to assess the quality of the surface generated with the optimal settings. As compared with initial setting of the SEM image, it was noticed that the bottom cut surface was nearly smooth, with less cracks, striations and pits in the improved optimal results of the SEM image. The results of the analysis can be used to control machining parameters and increase the accuracy of AWJed components.Originality/valueThe findings of this study present an innovative method for assessing the characteristics of the nontraditional machining processes that are most suited for use in industrial and commercial applications.

A Multi-Antenna Spectrum Sensing Method Based on CEEMDAN Decomposition Combined with Wavelet Packet Analysis

In many practical communication environments, the presence of uncertain and hard-to-estimate noise poses significant challenges to cognitive radio spectrum sensing systems, especially when the noise distribution deviates from the Gaussian distribution. This paper introduces a cutting-edge multi-antenna spectrum sensing methodology that synergistically integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), wavelet packet analysis, and differential entropy. Signal feature extraction commences by employing CEEMDAN decomposition and wavelet packet analysis to denoise signals collected by secondary antenna users. Subsequently, the differential entropy of the preprocessed signal observations serves as the feature vector for spectrum sensing. The spectrum sensing module utilizes the SVM classification algorithm for training, while incorporating elite opposition-based learning and the sparrow search algorithm with genetic variation to determine optimal kernel function parameters. Following successful training, a decision function is derived, which can obviate the need for threshold derivation present in conventional spectrum sensing methods. Experimental validation of the proposed methodology is conducted and comprehensively analyzed, conclusively demonstrating its remarkable efficacy in enhancing spectrum sensing performance.

An Enhanced Spotted Hyena Optimization Algorithm and its Application to Engineering Design Scenario

The Spotted Hyena Optimization (SHO) algorithm is inspired by simulating the predatory behavior of spotted hyenas. While the mathematical model of the SHO algorithm is simple and optimal, it is easy to fall into local optimization and causes premature convergence compared to some metaheuristic algorithms. To the end, we propose an enhanced Spotted Hyena Optimization algorithm, a hybrid SHO algorithm using Elite Opposition-Based Learning coupled with the Simplex Method called EOBL-SM-SHO. The EOBL-SM-SHO algorithm combines the characteristics of the simplex method’s geometric transformations (reflection, inside contraction, expansion, and outside contraction) with more practical information on elite opposition-based learning strategy. They can significantly strengthen the SHO algorithm’s search range and augment the hyena population’s diversity. Furthermore, we employ eleven benchmark functions and three engineering design issues to gauge the effectiveness of the EOBL-SM-SHO algorithm. Our extensive experimental results unveil that EOBL-SM-SHO achieves better accuracy and convergence rate than the state-of-the-art algorithms (e.g., Artificial Gorilla Troops Optimizer (GTO), Cuckoo Search (CS), Farmland Fertility Algorithm (FFA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Spotted Hyena Optimizer (SHO)).

PSO-Incorporated Hybrid Artificial Hummingbird Algorithm with Elite Opposition-Based Learning and Cauchy Mutation: A Case Study of Shape Optimization for CSGC-Ball Curves.

With the rapid development of the geometric modeling industry and computer technology, the design and shape optimization of complex curve shapes have now become a very important research topic in CAGD. In this paper, the Hybrid Artificial Hummingbird Algorithm (HAHA) is used to optimize complex composite shape-adjustable generalized cubic Ball (CSGC-Ball, for short) curves. Firstly, the Artificial Hummingbird algorithm (AHA), as a newly proposed meta-heuristic algorithm, has the advantages of simple structure and easy implementation and can quickly find the global optimal solution. However, there are still limitations, such as low convergence accuracy and the tendency to fall into local optimization. Therefore, this paper proposes the HAHA based on the original AHA, combined with the elite opposition-based learning strategy, PSO, and Cauchy mutation, to increase the population diversity of the original algorithm, avoid falling into local optimization, and thus improve the accuracy and rate of convergence of the original AHA. Twenty-five benchmark test functions and the CEC 2022 test suite are used to evaluate the overall performance of HAHA, and the experimental results are statistically analyzed using Friedman and Wilkerson rank sum tests. The experimental results show that, compared with other advanced algorithms, HAHA has good competitiveness and practicality. Secondly, in order to better realize the modeling of complex curves in engineering, the CSGC-Ball curves with global and local shape parameters are constructed based on SGC-Ball basis functions. By changing the shape parameters, the whole or local shape of the curves can be adjusted more flexibly. Finally, in order to make the constructed curve have a more ideal shape, the CSGC-Ball curve-shape optimization model is established based on the minimum curve energy value, and the proposed HAHA is used to solve the established shape optimization model. Two representative numerical examples comprehensively verify the effectiveness and superiority of HAHA in solving CSGC-Ball curve-shape optimization problems.

Revolutionizing Vehicle Cruise Control: An Elite Opposition-Based Pattern Search Mechanism Augmented INFO Algorithm for Enhanced Controller Design

This paper presents a groundbreaking approach to enhance the performance of a vehicle cruise control system—a crucial aspect of road safety. The work offers two key contributions. Firstly, a state-of-the-art metaheuristic algorithm is proposed by augmenting the performance of the weighted mean of vectors (INFO) algorithm using pattern search and elite opposition-based learning mechanisms. The resulting boosted INFO (b-INFO) algorithm surpasses the original INFO, marine predators, and gravitational search algorithms in terms of performance on benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions. Secondly, a novel proportional, fractional order integral, derivative plus double derivative with filter (P{I}^{lambda }DN{D}^{2}{N}^{2}) controller is proposed as a more efficient control structure for vehicle cruise control systems. An objective function is utilized to determine the optimal values for the controller parameters, and the proposed method's performance is compared against a range of recent approaches. Results demonstrate that the b-INFO algorithm-based P{I}^{lambda }DN{D}^{2}{N}^{2} controller is the most efficient and superior method for controlling a vehicle cruise control system. Moreover, this work represents the first report of a P{I}^{lambda }DN{D}^{2}{N}^{2} controller’s implementation for vehicle cruise control systems, underscoring the novelty and significance of this research. The proposed method's exceptional ability is further confirmed by comparisons with the genetic algorithm, ant lion optimizer, atom search optimizer, arithmetic optimization algorithm, slime mold algorithm, Lévy flight distribution algorithm, manta ray foraging optimization, and hunger games search-based proportional–integral–derivative (PID), along with Harris hawks optimization-based PID and fractional order PID controllers. This work marks a remarkable milestone toward safer and more efficient vehicle cruise control systems.

Coal-rock drilling states recognition of drilling robot for rockburst prevention based on multi-sensor information fusion

Accurate recognition of coal-rock drilling sates is a prerequisite for achieving intelligent drilling pressure relief. In this paper, a novel coal-rock drilling states recognition method of drilling robot for rockburst prevention is proposed. Firstly, different coal-rock drilling signals are collected and processed by using improved antlion optimization (ALO) algorithm and variational mode decomposition (VMD). Meanwhile, the elite opposition-based learning (EOL) strategy is used to improve the global search ability and optimization performance of ALO, and the EOL-ALO is developed and employed to automatically search the optimal key parameters of VMD. Subsequently, the root mean square of frequency and kurtosis are used to extract the feature information from the decomposed signals and the singular value decomposition method is employed to reduce the dimensionality of high-dimensional feature vectors. Furthermore, an improved D-S evidence theory is developed to fuse the recognition results of support vector machine through a single sensor information and the fusion recognition framework of coal-rock drilling states is designed. Finally, a coal-rock drilling experimental platform is established and some experimental analysis is carried out. The experimental results indicate the feasibility and superiority of proposed coal-rock drilling states recognition method.

Feature selection based on a multi-strategy African vulture optimization algorithm and its application in essay scoring

Reducing the dimensions of the original data set while preserving the information as much as possible is conducive to improving the accuracy and efficiency of the model. To achieve this, this paper presents a multi-strategy African vulture optimization algorithm that is the chaotic and elite opposition-based African vulture optimization with the simplex method and differential evolution strategy(CESDAVO). Three main improvements are introduced into African vultures optimization(AVO) to improve its capabilities in this study. Firstly, the chaotic elite opposition-based learning strategy is used to initialize and diversify individual positions of vultures. Secondly, the simplex method is used to optimize those poor individuals so as to further improve the local exploitation ability of the algorithm. Thirdly, the differential evolution strategy is used to make the algorithm escape from the local optimum and improve the global optimization capability of the algorithm. The results of the ablation experiments show that mixing the three strategies greatly improves the optimization performance of the algorithm. In addition, Nine algorithms are compared with CESDAVO on 15 benchmark functions, and this experimental result shows that its optimization capability is superior to the others. Then, the proposed CESDAVO is employed for feature selection, and 12 standard datasets are used for experiments. According to the experimental results, CESDAVO obtained the highest average classification accuracy on 11 datasets and the highest feature selection rate on 8 datasets, which is significantly better than other algorithms. Finally, CESDAVO is also applied to feature reduction for essays, removing 24 features and significantly improving the classification accuracy on multiple classifiers.

Evaluation of new sparrow search algorithms with sequential fusion of improvement strategies

Sparrow search algorithm (SSA) is a novel swarm intelligent algorithm inspired by foraging and anti-predation behaviors of the sparrow population. However, the population diversity of the basic SSA decreases in iterations and it tends to fall into the local optimum. In this paper, we propose five improved sparrow search algorithms (ISSAs 1–5) by sequentially integrating the five strategies of improved sine mapping, elite opposition-based learning, sine cosine algorithm, Lévy flight, and Gaussian mutation to enhance SSA performance. In other words, the nth ISSA adopts the first n above improvement strategies. An experiment based on 23 classical benchmark functions (F1-F23) is conducted to test the performance of proposed ISSAs. Based on a comprehensive comparison with existing algorithms, it is found that ISSA 5 which fuses all five improvement strategies has the best performance, and the ISSA performance increases as more improvement strategies are integrated. For F1-F13, ISSA 5 outperforms all other algorithms for 69.23% (9/13), 76.92% (10/13), 61.54% (8/13), 76.92% (10/13), 69.23% (9/13), 61.54% (8/13) of the functions at 10, 30, 50, 100, 500, 1000 dimensions respectively. For fix-dimension functions F14-F23, ISSA 5 is top-ranked for 70.00% (7/10) of the functions. Also, it is found that the sine cosine algorithm and Lévy flight strategies have greater impact on the improvement of SSA among the five strategies. Moreover, further analysis by Friedman test and Nemenyi post-hoc test reveals the superior performance of the proposed ISSAs in a statistical sense against other competing algorithms. As such, the results indeed postively demonstrate the efficiency, robustness, convergence and practical applicability of the proposed ISSAs.

A hybrid intelligence model for predicting dissolved oxygen in aquaculture water

Dissolved oxygen is an important water quality indicator that affects the health of aquatic products in aquaculture, and its monitoring and prediction are of great significance. To improve the prediction accuracy of dissolved oxygen water quality series, a hybrid prediction model based on variational mode decomposition (VMD) and a deep belief network (DBN) optimized by an improved slime mould algorithm (SMA) is proposed in this paper. First, VMD is used to decompose the nonlinear dissolved oxygen time series into several relatively stable intrinsic mode function (IMF) subsequences with different frequency scales. Then, the SMA is improved by applying elite opposition-based learning and nonlinear convergence factors to increase its population diversity and enhance its local search and global convergence capabilities. Finally, the improved SMA is used to optimize the hyperparameters of the DBN, and the aquaculture water quality prediction VMD-ISMA-DBN model is constructed. The model is used to predict each IMF subsequence, and the ISMA optimization algorithm is used to adaptively select the optimal hyperparameters of the DBN model, and the prediction results of each IMF are accumulated to obtain the final prediction result of the dissolved oxygen time series. The dissolved oxygen data of aquaculture water from 8 marine ranches in Shandong Province, China were used to verify the prediction performance of the model. Compared with the stand-alone DBN model, the prediction performance of the model has been significantly improved, MAE and MSE have been reduced by 43.28% and 40.43% respectively, and (R2) has been increased by 8.37%. The results show that the model has higher prediction accuracy than other commonly used intelligent models (ARIMA, RF, TCN, ELM, GRU and LSTM); hence, it can provide a reference for the accurate prediction and intelligent regulation of aquaculture water quality.

Sunflower optimization algorithm for multi-strategy-assist parameter identification of solar cell models

A novel optimization method, namely, the elite opposition learning and polynomial steps-based sunflower optimization (EOPSFO) algorithm, has been proposed to solve engineering problems. To speed up the convergence, the elite opposition-based learning and polynomial steps strategy is applied to automatically determine the search step adapted in each iteration. To verify the performance of EOPSFO, the feasibility of EOPSFO is first verified using various benchmarking and some standard optimization problems. In addition, EOPSFO is used to determine the parameters of the single diode model and double diode model. Results show that EOPSFO can be regarded as a competitive algorithm in optimization problems.

Two-step approach based multi-objective groundwater remediation using enhanced random vector functional link integrated with evolutionary marine predator algorithm

We here propose a two-step approach-based simulation-optimization model for multi-objective groundwater remediation using enhanced random vector functional link (ERVFL) and evolutionary marine predator algorithm (EMPA). In this study, groundwater flow and solute transport models are developed using MODFLOW and MT3DMS. The ERVFL network is used to approximate the flow and transport models, enhancing the computational performance. This study also improves the robustness of the ERVFL network using a kernel density estimator (KDE) based weighted least square approach. We further develop the EMPA by modifying the marine predator algorithm (MPA) using elite opposition-based learning, biological evolution operators, and elimination mechanisms. In the multi-objective version of EMPA, the non-dominated/Pareto-optimal solutions are stored in an external repository using an archive controller and adaptive grid mechanism to promote better convergence and diversity of the Pareto front. The proposed methodologies are applied for multi-objective groundwater remediation of a hypothetical unconfined aquifer based on the two-step method. The first step directly integrates flow and transport models with EMPA and finds the optimal locations of pumping wells by minimizing the percent of contaminant mass remaining in the aquifer. In the second step, the ERVL-based proxy model is integrated with EMPA and used for multi-objective optimization while explicitly using the pumping well locations obtained in the first step. The multi-objective optimization generates a Pareto-optimal solution representing the relationship between the rate of pumping and the amount of contaminant mass in the aquifer. Further analyses show a significant advantage of the two-step approach over a traditional method for multi-objective groundwater remediation.

Boosted arithmetic optimization algorithm with elite opposition-based pattern search mechanism and its promise to design microstrip patch antenna for WLAN and WiMAX

ABSTRACT This paper investigates the performance of a novel artificial intelligence optimization technique in terms of designing a small size antenna that can be used for WLAN and WiMAX applications. In this regard, a boosted version of the arithmetic optimization algorithm is constructed as a novel artificial intelligence optimization technique with the aid of pattern search and elite opposition-based learning mechanisms. The proposed boosted arithmetic optimization algorithm is demonstrated for its superior explorative and exploitative behavior using classical fixed-dimensional, multimodal, and unimodal benchmark functions. The performance of the boosted arithmetic optimization algorithm is then presented for a real-world engineering optimization problem. For the latter challenge, a small size antenna that can be used for WLAN and WiMAX applications is designed. The obtained simulation results show that a compact small-sized patch antenna operating at WLAN and WiMAX frequencies can successfully be designed with the proposed boosted arithmetic optimization algorithm. Comparative evaluation against the state-of-the-art shows that efficiency is increased significantly since a bandwidth increase of up to is achieved even with a more than reduction in size.

An improved gorilla troops optimizer for global optimization problems and feature selection

The Artificial Gorilla Groups Optimizer (GTO) is a novel metaheuristic algorithm that takes its cues from the collective intelligence of wild gorilla troops. Although it has shown promise in solving various real-world applications, it may still get stuck at local optima and premature convergence when dealing with more complex optimization tasks. Because of these shortcomings, this research proposes a new modified Gorilla Groups Optimizer (mGTO) method, employing a set of operators to achieve a more stable balance between exploitation and exploration. These operators are the elite Opposition Based-Learning (EOBL), Cauchy Inverse Cumulative (CICD) Distribution Operator, and tangent Flight (TFO). Each operator is used to achieve a specific task during the search process. The EOBL aims to enhance the diversity of the population, which leads to discovering the feasible region. Whereas the integration between CICD and TFO is used to improve the population’s exploitation ability, which leads to an increase in the convergence rate. To validate the efficiency of the presented method, called mGTO, a set of experimental series is conducted using the CEC2020 benchmark and constraint design engineering problems. In addition, its applicability is assessed by implementing mGTO as a feature selection technique and applying it to improve the classification accuracy of sixteen datasets. The results of mGTO are also compared with those produced by other well-known meta-heuristic techniques. The statistical validity of the performance is also verified using Wilcoxon’s rank-sum test. The experimental results and comparison analysis reveal the consistent and better performance of the proposed mGTO method to solve optimization problems.

A High-Robust Displacement Prediction Model for Super-High Arch Dams Integrating Wavelet De-Noising and Improved Random Forest

We present a novel deformation prediction model for super-high arch dams based on the prototype monitoring displacement field. The noise reduction processing of the monitoring data is conducted by a wavelet technique. The performance-improved random forest intelligent regression approach is then established for constructing the arch dam deformation statistical models, whose hyper-parameters are intelligently optimized in terms of the improved salp swarm algorithm. In total, three enhancement strategies are developed into the standard salp swarm algorithm to improve the global searching ability and the phenomenon of convergence precocious, including the elite opposition-based learning strategy, the difference strategy, and the Gaussian mutation strategy. A prediction example for super-high arch dams is presented to confirm the feasibility and applicability of the prediction model based on five evaluation criteria. The prediction results show that the proposed model is superior to other standard models, and exhibits high-prediction accuracy and excellent generalization performance. The stability of the proposed prediction model is investigated by artificially introducing noise strategies, which demonstrates the high-robust prediction features and provides a promising tool for predicting carbon emissions, epidemics, and so forth.

Improved Multi-Strategy Harris Hawks Optimization and Its Application in Engineering Problems

In order to compensate for the low convergence accuracy, slow rate of convergence, and easily falling into the trap of local optima for the original Harris hawks optimization (HHO) algorithm, an improved multi-strategy Harris hawks optimization (MSHHO) algorithm is proposed. First, the population is initialized by Sobol sequences to increase the diversity of the population. Second, the elite opposition-based learning strategy is incorporated to improve the versatility and quality of the solution sets. Furthermore, the energy updating strategy of the original algorithm is optimized to enhance the exploration and exploitation capability of the algorithm in a nonlinear update manner. Finally, the Gaussian walk learning strategy is introduced to avoid the algorithm being trapped in a stagnant state and slipping into a local optimum. We perform experiments on 33 benchmark functions and 2 engineering application problems to verify the performance of the proposed algorithm. The experimental results show that the improved algorithm has good performance in terms of optimization seeking accuracy, the speed of convergence, and stability, which effectively remedies the defects of the original algorithm.

Inversion of TEM measurement data via a quantum particle swarm optimization algorithm with the elite opposition-based learning strategy

The fine interpretation and inversion of transient electromagnetic method measurement data have the problems of nonlinearity, multi-solution, and ill condition. However, the conventional particle swarm optimization (PSO) nonlinear inversion methods suffer from prematurity, slow convergence, and low calculation accuracy. To solve these problems, a quantum PSO (QPSO) algorithm based on the elite opposition-based learning (EOL) strategy is proposed. Firstly, three performances tests of the EOL-QPSO algorithm are carried out with Peaks, Schaffer and Rastrigin functions. The results show that the EOL-QPSO algorithm has excellent solution accuracy, efficient calculation speed and balanced exploitation and exploration capability. Secondly, the conventional PSO algorithm and the EOL-QPSO algorithm are used to compare the inversion of the theoretical model and the synthetic data with noise, and combined with Bayesian method, the posterior model probability statistics of the synthetic data are carried out. The research shows that the EOL-QPSO inversion algorithm is improved in terms of calculation accuracy, calculation efficiency, anti-noise performance and exploitation and exploration capability, and it can accurately obtain the posterior estimates of the real model. Finally, the inversion of field-measured data demonstrates that the EOL-PSO inversion method accurately reflects the position of the water-accumulated goaf.

Research on camera calibration optimization method based on improved sparrow search algorithm

Accurate calibration of camera internal parameters is an important step in realizing three-dimensional object reconstruction and target location. Large reprojection error is a frontier problem in the field of camera calibration. A real calibration image dataset and a reprojection error model are established. In combination with the fast iteration of elite opposition-based learning strategy and the population diversity of sparrow search algorithm, the calibration parameters were optimized to improve the accuracy of calibration parameters and reduce the reprojection error. Experimental results demonstrate the effectiveness of the proposed algorithm. It can effectively reduce the reprojection error.

Adaptive infinite impulse response system identification using an enhanced golden jackal optimization

Golden jackal optimization (GJO) is inspired by the cooperative attacking behavior of golden jackals and mainly simulates searching for prey, stalking and enclosing prey, and pouncing on prey to solve complicated optimization problems. However, the basic GJO has the disadvantages of premature convergence, a slow convergence rate and low computation precision. To enhance the overall search and optimization abilities, an enhanced golden jackal optimization (EGJO) method with the elite opposition-based learning technique and the simplex technique is proposed to address adaptive infinite impulse response system identification. The intention is to minimize the error fitness value and obtain the appropriate control parameters. The elite opposition-based learning technique boosts population diversity, enhances the exploration ability, extends the search range and avoids search stagnation. The simplex technique accelerates the search process, enhances the exploitation ability, improves the computational precision and increases the optimization depth. EGJO can not only achieve complementary advantages to avoid search stagnation but also balance exploration and exploitation to arrive at the best value. Three sets of experiments are used to verify the effectiveness and feasibility of EGJO. The experimental results clearly demonstrate that the optimization efficiency and recognition accuracy of EGJO are superior to those of AOA, GTO, HHO, MDWA, RSO, WOA, TSA and GJO. EGJO has a faster convergence rate, higher computation precision, better control parameters and better fitness value, and it is stable and resilient in solving the IIR system identification problem.

A sheep dynamic counting scheme based on the fusion between an improved-sparrow-search YOLOv5x-ECA model and few-shot deepsort algorithm

In order to improve the accuracy of sheep counting and avoid the interference of mutual occlusion caused by different moving speed among sheep, the concept of fusion between the improved YOLOv5x model based on the attention mechanism and DeepSort algorithm is proposed. First, the ECA structure that is channel attention mechanism is used to optimize YOLOv5x model to strengthen the ability of capturing global information. Secondly, the sparrow search algorithm based on elite opposition-based learning strategy is used to optimize the learning rate of the detection model, so as to get the weight information of the optimal group to further improve the recognition rate of sheep. In the experiment, 800 high-resolution sheep images augmented by SRGAN network and data augmentation are used as model datasets, and the best weight information obtained by the YOLOv5x-ECA-SSA* model is used to accurately recognize sheep. According to the DeepSort algorithm, the recognized sheep are tracked, predicted and matched optimally. The experimental results show that the test precision of YOLOv5x*, YOLOv5x-ECA* and YOLOv5x-ECA-SSA* based on the SRGAN and data enhancement to train are respectively 95.74%, 96.50% and 97.10%. The error rate of each model combined with DeepSort algorithm to complete sheep dynamic counting is respectively 13%, 12% and 5%. Among them, the YOLOv5x-ECA-SSA* model has the highest mAP and best effect of sheep counting. The result can provide a new theorical method for realizing intelligent dynamic counting and tracking in the grazing process and provide a new technical application for intelligent animal husbandry.

Special issue on “Optimal design and operation of energy systems”

Special issue on “Optimal design and operation of energy systems”