Opposition-based Learning Strategy Research Articles

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.

Preemptive multi-skill resource-constrained project scheduling of marine power equipment maintenance tasks1

The shipbuilding industry, characterized by its high complexity and remarkable comprehensiveness, deals with large-scale equipment construction, conversion, and maintenance. It contributes significantly to the development and national security of countries. The maintenance of large vessels is a complex management engineering project that presents a challenge in lowering maintenance time and enhancing maintenance efficiency during task scheduling. This paper investigates a preemptive multi-skill resource-constrained project scheduling problem and a task-oriented scheduling model for marine power equipment maintenance to address this challenge. Each task has a minimum capability level restriction during the scheduling process and can be preempted at discrete time instants. Each resource is multi-skilled, and only those who meet the required skill level can be assigned tasks. Based on the structural properties of the studied problem, we propose an improved Moth-flame optimization algorithm that integrates the opposition-based learning strategy and the mixed mutation operators. The Taguchi design of experiments (DOE) approach is used to calibrate the algorithm parameters. A series of computational experiments are carried out to validate the performance of the proposed algorithm. The experimental results demonstrate the effectiveness and validity of the proposed algorithm.

Opposition-based learning boosted orca predation algorithm with dimension learning: a case study of multi-degree reduction for NURBS curves

AbstractThe approximate degree reduction of ball non-uniform rational B-splines (NURBS) curves is a pivotal and knotty technique in computer-aided design/computer-aided manufacture. As we all know, the multi-degree reduction of NURBS ones is a mathematical optimization problem that a swarm intelligence algorithm can deal with. This paper uses an improved orca predation algorithm (IOPA) to accomplish the optimal multi-degree reduction of NURBS curves. Firstly, by incorporating a dimension learning strategy and opposition-based learning strategy into the orca predation algorithm (OPA), an IOPA is developed to increase the population diversity and enhance its capability of jumping out of the local minima. Secondly, the superiority of the proposed IOPA is comprehensively verified by comparing it with the original OPA and numerous celebrated and newly developed algorithms on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite and IEEE CEC2017 benchmark functions, respectively. Meanwhile, the practicability of IOPA is also highlighted by solving three real-world engineering design problems. Furthermore, statistical testing of IOPA has been conducted to validate its significance. Finally, the optimization model of multi-degree reduction for NURBS curves is established by minimizing the distance between the original curve and the approximate curve. The IOPA is utilized to solve the optimization model, and the optimal approximate NURBS curves are obtained. Some representative numerical examples illustrate the ability of the proposed IOPA to effectively solve the multi-degree reduction problem of NURBS curves in terms of precision, robustness, and convergence characteristics.

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.

Enhanced Teaching–Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem

This research proposes an enhanced teaching–learning based optimization (ETLBO) algorithm to realize an efficient path planning for a mobile robot. Four strategies are introduced to accelerate the teaching–learning based optimization (TLBO) algorithm and optimize the final path. Firstly, a divide-and-conquer design, coupled with the Dijkstra method, is developed to realize the problem transformation so as to pave the way for algorithm deployment. Secondly, the interpolation method is utilized to smooth the traveling route as well as to reduce the problem dimensionality. Thirdly, an opposition-based learning strategy is embedded into the algorithm initialization to create initial solutions with high qualities. Finally, a novel, individual update method is established by hybridizing the TLBO algorithm with differential evolution (DE). Simulations on benchmark functions and MRPP problems are conducted, and the proposed ELTBO is compared with some state-of-the-art algorithms. The results show that, in most cases, the ELTBO algorithm performs better than other algorithms in both optimality and efficiency.

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.

EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for Engineering Applications

The jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a newly developed metaheuristic algorithm that solves complex and real-world optimization problems. The global exploration capability and robustness of the JS algorithm are strong, but the JS algorithm still has significant development space for solving complex optimization problems with high dimensions and multiple local optima. Therefore, in this study, an enhanced jellyfish search (EJS) algorithm is developed, and three improvements are made: (i) By adding a sine and cosine learning factors strategy, the jellyfish can learn from both random individuals and the best individual during Type B motion in the swarm to enhance optimization capability and accelerate convergence speed. (ii) By adding a local escape operator, the algorithm can skip the trap of local optimization, and thereby, can enhance the exploitation ability of the JS algorithm. (iii) By applying an opposition-based learning and quasi-opposition learning strategy, the population distribution is increased, strengthened, and more diversified, and better individuals are selected from the present and the new opposition solution to participate in the next iteration, which can enhance the solution’s quality, meanwhile, convergence speed is faster and the algorithm’s precision is increased. In addition, the performance of the developed EJS algorithm was compared with those of the incomplete improved algorithms, and some previously outstanding and advanced methods were evaluated on the CEC2019 test set as well as six examples of real engineering cases. The results demonstrate that the EJS algorithm can skip the trap of local optimization, can enhance the solution’s quality, and can increase the calculation speed. In addition, the practical engineering applications of the EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and therefore, suggests future possible applications for solving such optimization problems.

Stacking sequence optimization of arbitrary quadrilateral laminated plates for maximum fundamental frequency by hybrid whale optimization algorithm

A Hybrid Whale Optimization Algorithm (HWOA) is proposed to optimize the stacking sequence of arbitrary quadrilateral composite plates. By introducing an adversarial learning strategy, a broader search is performed on the initial domain to generate a better initial population. The mutation operator and opposition-based learning (OBL) strategy of the differential evolution (DE) algorithm are combined to maintain the diversity of the evolutionary process. In addition, the nonlinear convergence factor is introduced to improve the global exploration ability of HWOA when dealing with discrete problems. The stacking sequences of quadrilateral composite plates with different boundary conditions and geometries are optimized to maximize the fundamental frequency. The optimized results of HWOA are compared with those of whale optimization algorithm (WOA), Aquila optimizer (AO), artificial bee colony (ABC) algorithm, DE algorithm and layerwise optimization approach (LOA) algorithm. The results show that HWOA with multiple strategy combinations has good global search capability and robustness, and thus HWOA is a good candidate optimization algorithm for composite plate optimization.

An effective optimization method for solving the relay coordination problem of the microgrids

In this work, an opposition-based learning-aggrandized class topper optimization (OBL-ACTO) algorithm is proposed to optimize the operating time of directional overcurrent relays (DOCRs) for an integrated microgrid power distribution network. The proposed OBL-ACTO algorithm is a hybridized method. In this method, an opposition-based learning (OBL) strategy is employed to tackle the local sticking problem of the traditional ACTO method and ameliorate its exploration and exploitation capability. The effectiveness of OBL-ACTO in solving the optimum coordination problem of (DOCRs) is tested on the IEC microgrid system and the 33kv distribution part of a standard IEEE 30-bus system. The proposed method along with the system is executed on the LabVIEW©2015 platform. The effectiveness of the developed method is shown based on the acquired optimal solution by comparing it with recently developed optimization methods for DOCRs problem. The proposed approach reduces the total operating time by 22.827 to 73.819 % to the compared methods. In addition, a non-parametric statistical hypothesis test is performed to show the proposed method’s significance based on the obtained simulation results.

A hybrid filter-wrapper feature selection using Fuzzy KNN based on Bonferroni mean for medical datasets classification: A COVID-19 case study

Several feature selection methods have been developed to extract the optimal features from a dataset in medical datasets classification. Creating an efficient technique has become a challenge because of the high dimensions, noise, and redundant information. In this paper, we propose a hybrid filter-wrapper approach for feature selection. An ensemble of filter methods, ReliefF and Fuzzy Entropy (RFE) is developed, and the union of top-n features from each method are considered. The Equilibrium Optimizer (EO) technique is combined with Opposition Based Learning (OBL), Cauchy Mutation operator and a novel search strategy to enhance its capabilities. The OBL strategy improves the diversity of the population in the search space. The Cauchy Mutation operator enhances its ability to evade the local optima during the search, and the novel search strategy improves the exploration capability of the algorithm. This enhanced form of EO is integrated with eight time-varying S and V-shaped transfer functions to convert the solutions into binary form, Binary Enhanced Equilibrium Optimizer (BEE). The features from the ensemble are given as input to the Binary Enhanced Equilibrium Optimizer to extract the essential features. Fuzzy KNN based on Bonferroni mean is used as the learning algorithm. Twenty-two benchmark datasets and four microarray datasets are used to test the algorithm’s efficiency. This method is also applied to a COVID-19 case study. The results demonstrate the superiority of the proposed approach, RFE-BEE, among other methods in terms of fitness values, accuracy, precision, sensitivity, and F-measure, among several other state-of-the-art algorithms. RFE-BEE can be applied to various biomedical, computer vision and engineering applications such as electromyography pattern recognition, COVID-19 diagnosis, face recognition and fault diagnosis.

Deployment for NOMA-UAV Base Stations Based on Hybrid Sparrow Search Algorithm

Auxiliary UAV base stations are expected to be the most promising schemes to fill the resource shortage and the blind spot of coverage of the ground base stations in the future. This paper proposes a hybrid sparrow search algorithm (SSA) to optimize the three-dimensional (3D) deployment of multi-UAV base stations with the objective of maximizing the sum log-rate utility. First, the air-to-ground channel model is established using the non-orthogonal multiple access (NOMA) technology to improve the spectrum utilization by considering the complex interference forms in multi-UAV scenarios, which is more compatible with the actual applications. Then, the multi-UAV deployment problem is described as an optimization problem to maximize the sum log-rate utility of all users. Finally, a hybrid SSA is proposed to implement the optimal 3D deployment of multi-UAV base stations. Specifically, in order to overcome the local optimization defects of the traditional SSA, the opposition-based learning (OBL) strategy is utilized to modify the initial position of the sparrows to promote particle diversity, and the sine-cosine algorithm is developed to optimize the update formula of the beggar sparrows by integrating the disturbance into the update process. Consequently, a hybrid SSA with higher search accuracy, stability and robustness is formed. The simulation results demonstrate that the proposed method outperforms the traditional methods in terms of the sum log-rate utility and throughput.

APSO: An A*-PSO Hybrid Algorithm for Mobile Robot Path Planning

Aiming at the problems of the A* algorithm in mobile robot path planning, such as multiple nodes, low path accuracy, long running time and difficult path initialization of particle swarm optimization, an APSO algorithm combining A* and PSO was proposed to calculate the optimal path. First, a redundant point removal strategy is adopted to preliminarily optimize the path planned by the A* algorithm and obtain the set of key nodes. Second, a stochastic inertia weight is proposed to improve the search ability of PSO. Third, a stochastic opposition-based learning strategy is proposed to further improve the search ability of PSO. Fourth, the global path is obtained by using the improved PSO to optimize the set of key nodes. Fifth, a motion time objective function that is more in line with the actual motion requirements of the mobile robot is used to evaluate the algorithm. The simulation results of path planning show that the path planned by APSO not only reduces the running time of the mobile robot by 17.35%, 14.84%, 15.31%, 15.21%, 18.97%, 15.70% compared with the A* algorithm in the six environment maps but also outperforms other path planning algorithms to varying degrees. Therefore, the proposed APSO is more in line with the actual movement of the mobile robot.

PCOBL: A Novel Opposition-Based Learning Strategy to Improve Metaheuristics Exploration and Exploitation for Solving Global Optimization Problems

PCOBL: A Novel Opposition-Based Learning Strategy to Improve Metaheuristics Exploration and Exploitation for Solving Global Optimization Problems

Secret Image Restoration With Convex Hull and Elite Opposition-Based Learning Strategy

Digital images are easily corrupted during transmission. Most image denoising methods cannot perform well on restoring the secret image extracted from a corrupted stego image. To deal with this issue, we propose a new secret image restoration method with convex hull and elite opposition-based learning strategy. Specifically, the pixel distortion values of the corrupted secret image are calculated and used to classify the pixels into trustable pixels or untrusted pixels. For an untrusted pixel, a convex hull is generated by its context trustable pixels due to the irregular distribution of trustable pixels. The untrusted pixel in the convex hull is restored by the trustable pixels within the convex hull. The other untrusted pixels are restored using elite opposition-based learning strategy. The experimental results show that the proposed method outperforms some state-of-the-art methods regarding recovered secret image quality.

An Improved Sparrow Search Algorithm and Its Application in HIFU Sound Field.

The sparrow search algorithm (SSA) is a novel swarm intelligence optimization algorithm. It has a fast convergence speed and strong global search ability. However, SSA also has many shortcomings, such as the unstable quality of the initial population, easy to fall into the local optimal solution, and the diversity of the population decreases with the iterative process. In order to solve these problems, this paper proposes an improved sparrow search algorithm (ISSA). ISSA uses Chebyshev chaotic map and elite opposition-based learning strategy to initialize the population and improve the quality of the initial population. In the process of producer location update, dynamic weight factor and Levy flight strategy are introduced to avoid falling into a local optimal solution. The mutation strategy is applied to the scrounger location update process, and the mutation operation is performed on individuals to increase the diversity of the population. In order to verify the feasibility and effectiveness of ISSA, it is tested on 23 benchmark functions. The results show that compared with other seven algorithms, ISSA has higher convergence accuracy, faster convergence speed, and stronger stability. Finally, ISSA is used to optimize the sound field of high-intensity focused ultrasound (HIFU). The results show that ISSA can effectively improve the focusing performance and reduce the influence of sound field sidelobe, which is of great benefit for HIFU treatment.

Multi-Strategy Boosted Spider Monkey Optimization Algorithm for Feature Selection

To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm, this paper presents a new algorithm based on multi-strategy (ISMO). First, the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity. Second, this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency. Then, using the crisscross strategy, using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum. At last, we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals. Therefore, the application of ISMO is validated by ten benchmark functions and feature selection. It is proved that the proposed method can resolve the problem of feature selection.

An enhanced aquila optimization algorithm with velocity-aided global search mechanism and adaptive opposition-based learning.

The aquila optimization algorithm (AO) is an efficient swarm intelligence algorithm proposed recently. However, considering that AO has better performance and slower late convergence speed in the optimization process. For solving this effect of AO and improving its performance, this paper proposes an enhanced aquila optimization algorithm with a velocity-aided global search mechanism and adaptive opposition-based learning (VAIAO) which is based on AO and simplified Aquila optimization algorithm (IAO). In VAIAO, the velocity and acceleration terms are set and included in the update formula. Furthermore, an adaptive opposition-based learning strategy is introduced to improve local optima. To verify the performance of the proposed VAIAO, 27 classical benchmark functions, the Wilcoxon statistical sign-rank experiment, the Friedman test and five engineering optimization problems are tested. The results of the experiment show that the proposed VAIAO has better performance than AO, IAO and other comparison algorithms. This also means the introduction of these two strategies enhances the global exploration ability and convergence speed of the algorithm.

Dynamic Chaotic Opposition-Based Learning-Driven Hybrid Aquila Optimizer and Artificial Rabbits Optimization Algorithm: Framework and Applications

Aquila Optimizer (AO) and Artificial Rabbits Optimization (ARO) are two recently developed meta-heuristic optimization algorithms. Although AO has powerful exploration capability, it still suffers from poor solution accuracy and premature convergence when addressing some complex cases due to the insufficient exploitation phase. In contrast, ARO possesses very competitive exploitation potential, but its exploration ability needs to be more satisfactory. To ameliorate the above-mentioned limitations in a single algorithm and achieve better overall optimization performance, this paper proposes a novel chaotic opposition-based learning-driven hybrid AO and ARO algorithm called CHAOARO. Firstly, the global exploration phase of AO is combined with the local exploitation phase of ARO to maintain the respective valuable search capabilities. Then, an adaptive switching mechanism (ASM) is designed to better balance the exploration and exploitation procedures. Finally, we introduce the chaotic opposition-based learning (COBL) strategy to avoid the algorithm fall into the local optima. To comprehensively verify the effectiveness and superiority of the proposed work, CHAOARO is compared with the original AO, ARO, and several state-of-the-art algorithms on 23 classical benchmark functions and the IEEE CEC2019 test suite. Systematic comparisons demonstrate that CHAOARO can significantly outperform other competitor methods in terms of solution accuracy, convergence speed, and robustness. Furthermore, the promising prospect of CHAOARO in real-world applications is highlighted by resolving five industrial engineering design problems and photovoltaic (PV) model parameter identification problem.