Bionic Optimization Algorithm Research Articles

Deformation extent prediction of roadway roof during non-support period using support vector regression combined with swarm intelligent bionic optimization algorithms

Settlement deformation of the roof during non-support period in deep roadway excavation poses the most immediate hazard to coal mine roadway engineering. Thus, accurately predicting the roof’s displacement during this period is crucial in minimizing potential risks. Considering the intricate nature of factors that influence deep roadway engineering, this research employs Support Vector Regression (SVR) as the primary tool, coupled with four swarm intelligent bionic optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO) and Grasshopper Optimization Algorithm (GOA) to enhance the prediction performance and optimize hyper-parameters. The prediction models involve seven influential factors that are selected from the underground test roadway. These factors form a comprehensive evaluation system which is based on artificial intelligence techniques. To evaluate the performance of different prediction models, three error correlation coefficient indices are used, include root mean square error (RMSE), variance accounted for (VAF) and coefficient of determination (R2). Of all the models, the GWO-e-SVR model exhibits the best overall performance in predicting deformation extent of the roof during non-support period. With a swarm size of 100, the RMSE, VAF and R2 of training datasets are 0.3126, 15.05% and 0.8767, respectively. The RMSE, VAF and R2 of testing datasets are 0.3245, 16.43% and 0.8655, respectively. The model demonstrated outstanding predictive accuracy in small-scale experiments, signifying its strong generalization and robustness. Subsequently, a sensitivity analysis was conducted to expound on the impact of conclusive input parameters on the end target output, specifically the duration of non-support in the space–time effect concerning roof’s settlement during the non-supported state. These findings hold significant implications for the upcoming support construction scheme design and reinforcement support operation.

Estimating maize evapotranspiration based on hybrid back-propagation neural network models and meteorological, soil, and crop data.

Crop evapotranspiration is a key parameter influencing water-saving irrigation and water resources management of agriculture. However, current models for estimating maize evapotranspiration primarily rely on meteorological data and empirical coefficients, and the estimated evapotranspiration contains uncertainties. In this study, the evapotranspiration data of summer maize were collected from typical stations in Northern China (Yucheng Station), and a back-propagation neural network (BP) model for predicting maize evapotranspiration was constructed based on meteorological data, soil data, and crop data. To further improve its accuracy, the maize evapotranspiration model was optimized using three bionic optimization algorithms, namely the sand cat swarm optimization (SCSO) algorithms, hunter-prey optimizer (HPO) algorithm, and golden jackal optimization (GJO) algorithm. The results showed that the fusion of meteorological, soil moisture, and crop data can effectively improve the accuracy of the maize evapotranspiration model. The model showed higher accuracy with the hybrid optimization model SCSO-BP compared to the stand-alone BP neural network model, with improvements of 2.7-4.8%, 17.2-25.5%, 13.9-26.8%, and 3.3-5.6% in terms of R2, RMSE, MAE, and NSE, respectively. Comprehensively compared with existing maize evapotranspiration models, the SCSO-BP model presented the highest accuracy, with R2 = 0.842, RMSE = 0.433 mm/day, MAE = 0.316 mm/day, NSE = 0.840, and overall global evaluation index (GPI) ranking the first. The results have reference value for the calculation of daily evapotranspiration of maize in similar areas of northern China.

Coronavirus Mask Protection Algorithm: A New Bio-inspired Optimization Algorithm and Its Applications.

Nowadays, meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems. In this paper, a COVID-19 prevention-inspired bionic optimization algorithm, named Coronavirus Mask Protection Algorithm (CMPA), is proposed based on the virus transmission of COVID-19. The main inspiration for the CMPA originated from human self-protection behavior against COVID-19. In CMPA, the process of infection and immunity consists of three phases, including the infection stage, diffusion stage, and immune stage. Notably, wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves, which are similar to the exploration and exploitation in optimization algorithms. This study simulates the self-protection behavior mathematically and offers an optimization algorithm. The performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions, CEC2020 suite problems, and three truss design problems. The statistical results demonstrate that the CMPA is more competitive among these state-of-the-art algorithms. Further, the CMPA is performed to identify the parameters of the main girder of a gantry crane. Results show that the mass and deflection of the main girder can be improved by 16.44% and 7.49%, respectively.

Analysis of Efficient Optimization Algorithm for Information Nodes in Wireless Network Communication Chaos

Due to the poor node optimization effect of traditional mathematical model methods, an efficient node optimization algorithm based on ant colony genetic algorithm is proposed. The ant colony algorithm is a kind of bionic optimization algorithm, and its dynamics and self-similarity are very similar to the optimization principles of messy information nodes. "Chaos node efficient optimization algorithm" is dedicated to effectively aggregating various resources such as computing, storage, knowledge, communication, information, distributed around the world, serving the public, and realizing resource sharing and collaborative work. Among them, chaos nodes efficiently search the excellent problem is particularly prominent. If the number of parent nodes and the order of the nodes are known, the ant colony genetic algorithm is used to find the largest supporting tree, so as to obtain the best node to obtain the largest number of iterations, thereby effectively optimizing the information node.Wireless Network Communication, Chaotic Nodes, Nodes, Efficient Optimization

Applicability of hybrid bionic optimization models with kernel-based extreme learning machine algorithm for predicting daily reference evapotranspiration: a case study in arid and semiarid regions, China.

The accurate prediction of daily reference crop evapotranspiration (ETO) enables effective management decision-making for agricultural water resources; this is crucial for developing water-efficient agriculture. To improve the accuracy of ETO forecasts in data-deficient areas, this study uses a decision tree algorithm (classification and regression tree [CART]) to obtain the effects of various factors on ETO at typical stations in arid and semiarid regions of China. A combination of factors with considerable influence on the model was selected as the input for constructing a kernel-extreme-learning-machine (KELM) daily reference evapotranspiration prediction model, and three bionic optimization algorithms (i.e., sparrow search optimization algorithm, Harris Hawks optimization algorithm, and lion swarm optimization algorithm) were integrated to optimize KELM prediction model parameters and improve the accuracy of daily reference evapotranspiration prediction. The results indicate that temperature (maximum or minimum temperature) is the primary factor influencing ETO, and the range of importance is 0.399-0.554. RH and Ra are also key factors influencing ETO; the hybrid model optimized using the bionic optimization algorithm provides advantages over the independent KELM model, and the SSA-KELM model has the highest accuracy among hybrid models, with a root-mean-square error of 0.408-1.964, R2 of 0.545-0.982, mean absolute error of 0.273-1.086, and Nash-Sutcliffe efficiency coefficient of 0.658-0.967. The top five factors extracted using the CART algorithm are recommended as inputs for constructing the SSA-KELM model for ETO estimation in arid and semiarid regions of China, and this model can also serve as a reference for ETO forecasting in similar regions.

Improved artificial electric field algorithm based on multi-strategy and its application

Artificial electric field algorithm is a new swarm bionic optimization algorithm, which uses the interaction force of charged particles to create a mathematical model to solve the problem. In order to improve the global exploration ability and local development ability of artificial electric field algorithm, an artificial electric field algorithm based on opposition learning is proposed. The chaos strategy is used to strengthen the quality of the initial population, and the opposition learning strategy is used to increase the diversity of the population and the development ability of the algorithm. The excellent performance of the algorithm is proved by simulation experiments. The improved artificial electric field algorithm was combined with SVM to construct the sand liquefaction identification model by selecting seven measured indexes, including intensity, underground water level, overlying effective pressure, standard penetration hit number, average particle size, non-uniformity coefficient and shear stress ratio. Compared with traditional methods such as standard method and seed simplification method, the results show that IAEFA-SVM model has high prediction accuracy and provides an effective method for sand liquefaction identification

Weed Density Extraction Based on Few-Shot Learning Through UAV Remote Sensing RGB and Multispectral Images in Ecological Irrigation Area.

With the development of ecological irrigation area, a higher level of detection and control categories for weeds are currently required. In this article, an improved transfer neural network based on bionic optimization to detect weed density and crop growth is proposed, which used the pre-trained AlexNet network for transfer learning. Because the learning rate of the new addition layer is difficult to tune to the best, the weight and bias learning rate of the newly added fully connected layer is set with particle swarm optimization (PSO) and bat algorithm (BA) to find the optimal combination on the small data set. Data are transported to the convolutional neural network (CNN) by collecting red-green-blue (RGB) and 5-band multispectral images of 3 kinds of weeds and 3 kinds of crops as data sets, through cutting, rotating, and other operations. Finally, 6 kinds of classifications are implemented. At the same time, a self-constructed CNN based on model-agnostic meta-learning (MAML) is proposed in order to realize the learning of neural networks with small sample and high efficiency, and its accuracy is verified in the test set. The neural networks optimized by two bionic optimization algorithms are compared with the self-constructed CNN based on MAML and histogram of oriented gradient + support vector machine (HOG + SVM). The experimental results show that the combination of learning rate through BA is the best, and its accuracy can reach 99.39% for RGB images, 99.53% for multispectral images, and 96.02% for a 6-shot small sample. The purpose of the classification proposed in this article is to calculate the growth of various plants (including weeds and crops) in the farmland. And various plant densities can be accurately calculated through the plant density calculation formula and algorithm proposed in this article, which provides a basis for the application of variable herbicides by experimenting in different farmlands. Finally, an excellent cycle of ecological irrigation district can be promoted.

Data Transmission Reliability Analysis of Wireless Sensor Networks for Social Network Optimization

With the rapid development of the Internet in recent years, people are using the Internet less and less frequently. People publish and obtain information through various channels on the Internet, and online social networks have become one of the most important channels. Many nodes in social networks and frequent interactions between nodes create great difficulties for privacy protection, and some of the existing studies also have problems such as cumbersome computational steps and low efficiency. In this paper, we take the complex environment of social networks as the research background and focus on the key issues of mobile wireless sensor network reliability from the mobile wireless sensor networks that apply to large-scale, simpler information, and delay tolerance. By introducing intelligent learning methods and swarm intelligence bionic optimization algorithms, we address reliability issues such as mobile wireless sensor network fault prediction methods and topology reliability assessment methods in industrial application environments, the impact of mobile path optimization of mobile wireless sensor networks on data collection efficiency and network reliability, reliable data transmission based on data fusion methods, and intelligent fault tolerance strategies for multipath routing to ensure mobile wireless sensor networks operate energy-efficiently and reliably in complex industrial application environments.

Exploration of Financial Market Credit Scoring and Risk Management and Prediction Using Deep Learning and Bionic Algorithm

The purpose is to effectively manage the financial market, comprehensive assess personal credit, reduce the risk of financial enterprises. Given the systemic risk problem caused by the lack of credit scoring in the existing financial market, a credit scoring model is put forward based on the deep learning network. The proposed model uses RNN (Recurrent Neural Network) and BRNN (Bidirectional Recurrent Neural Network) to avoid the limitations of shallow models. Afterward, to optimize path analysis, bionic optimization algorithms are introduced, and an integrated deep learning model is proposed. Finally, a financial credit risk management system using the integrated deep learning model is proposed. The probability of default or overdue customers is predicted through verification on three real credit data sets, thus realizing the credit risk management for credit customers.

Economic Structure Analysis Based on Neural Network and Bionic Algorithm

In this article, an in-depth study and analysis of economic structure are carried out using a neural network fusion release algorithm. The method system defines the weight space and structure space of neural networks from the perspective of optimization theory, proposes a bionic optimization algorithm under the weight space and structure space, and establishes a neuroevolutionary method with shallow neural network and deep neural network as the research objects. In the shallow neuroevolutionary, the improved genetic algorithm (IGA) based on elite heuristic operation and migration strategy and the improved coyote optimization algorithm (ICOA) based on adaptive influence weights are proposed, and the shallow neuroevolutionary method based on IGA and the shallow neuroevolutionary method based on ICOA are applied to the weight space of backpropagation (BP) neural networks. In deep neuroevolutionary method, the structure space of convolutional neural network is proposed to solve the search space design of neural structure search (NAS), and the GA-based deep neuroevolutionary method under the structure space of convolutional neural network is proposed to solve the problem that numerous hyperparameters and network structure parameters can produce explosive combinations when designing deep learning models. The neural network fusion bionic algorithm used has the application value of exploring the spatial structure and dynamics of the socioeconomic system, improving the perception of the socioeconomic situation, and understanding the development law of society, etc. The idea is also verifiable through the present computer technology.

An improved intelligent bionic optimization algorithm based on the growth characteristics of tree branches

To improve the optimization efficiency of the intelligent bionic optimization algorithm, this paper proposes intelligent bionic optimization algorithm based on the growth characteristics of tree branches. Firstly, the growth organ of the tree is mapped into the coding of the tree growth algorithm (intelligent bionic optimization algorithm). Secondly, the entire tree, that is the growing tree, is formed by selecting the individual that grows fast to generate the next level of shoot population. Lastly, if the growing tree reaches a certain level, the individual coding of the shoots is added to enhance the searching ability of the individuals of current generation in the growth tree growth space, so that the algorithm approaches the optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function and showed that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm and the ant colony algorithm.

An Improved Whale Optimization Algorithm for the Traveling Salesman Problem

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.

Intelligent Bionic Optimization Algorithm Based on the Growth Characteristics of Tree Branches

To improve the performance of bionic algorithms, an intelligent bionic optimization algorithm is proposed based on the morphological characteristics of trees growing toward light. The growth organ of the tree is mapped into the coding of the tree growth algorithm, and the entire tree is formed by selecting the fastest growing individual to form the next level of the tree. When the tree growth reaches a certain level, the individual code of the shoot tip is added to enhance the search ability of the individual shoot tip in the growth space of the entire tree. This method achieves a near-optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function. The experimental results show that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm or the ant colony algorithm.

Design of cancer classification and visualization platform based on Internet big data

With the development of Internet technology, Internet technology is widely used in the medical industry. Based on the medical Internet big data, using visualization, data mining and other technologies, combined with distributed crawler, HTTP data transmission technology, vue2 front-end framework and the application of visualization technology, this paper completed the development and design of the analysis platform based on the internet medical big data. From the three aspects of presentation layer, business logic layer and data access layer, the architecture can show the number of hospitals, the proportion of doctors, the number of departments, the hospital’s praise, poor evaluation and other information. Then, the cancer big data classification model based on the new bionic optimization algorithm is established. The experimental results show that the method has high classification accuracy and speed. In the era of big data, the integration of early diagnosis and treatment of cancer resources and information construction is very necessary and has far-reaching significance.

False Lipschitz Penalty Sparse Low-Rank Matrix and Chaotic Bionic Optimization for Prognosis of Bearing Degradation

Accurately predicting the degradation trend of a pivotal component is essential for scheduling maintenance decisions, avoiding abrupt shutdowns of mechanical equipment, and improving overall systems reliability. This article proposes a hybrid data-driven approach for bearing degradation prognosis that utilizes sparse low-rank matrix (SLRM) and chaotic bionic optimization algorithms based upon bearings health indicator time series (HITS). First, by mean of SLRM method, the HITS of bearings can be adaptively decomposed into low frequency trend component (LFC) and high frequency oscillation component (HFC), respectively. A novel non-convex regularizer named false Lipschitz penalty is designed, and the strict convexity of the proposed cost function can be ensured. Meanwhile, an efficient iterative algorithm using the forward–backward splitting technique is presented. Second, a metaheuristic algorithm named chaotic cuckoo search (CCS) is utilized to optimize the key parameters of the support vector machine (SVM) model, and the decomposed LFC and HFC are, respectively, predicted via the CCS-SVM approach. The final predicted time series is obtained after a summation operation. The proposed hybrid approach is evaluated using accelerated degradation test data of rolling bearings. Experimental results demonstrate the effectiveness and superiority of the proposed approach in comparison with five benchmark methods.

An Improved Hybrid Clustering Algorithm Based on Particle Swarm Optimization and K-means

The most common algorithm in data mining is cluster analysis. Cluster analysis algorithm has been widely used in many fields, especially in data analysis, market research and pattern recognition. K-means clustering algorithm(KMCA), as one of the common algorithms in clustering analysis, occupies an important position in clustering analysis and has relatively more applications. However, in recent years, with the deepening of the application of clustering analysis algorithm, it is found that there are a series of problems in KMCA, which directly affect the accuracy of data analysis results. On this basis, a bionic optimization algorithm, particle swarm optimization algorithm, is generated, which makes up for the shortcomings of KMCA. The aim of this paper is to establish the optimal algorithm for data analysis by means of the research on the improved hybrid clustering algorithm of particle swarm optimization and k-means. The experimental results show that the algorithm can effectively integrate the advantages of particle swarm optimization algorithm and KMCA, which not only improves the speed of the algorithm, but also guarantees the accuracy of the results.

The Research of Path Planning for Transmission Network Based on Improved Ant Colony Algorithms

With the continuous expansion of power grid scale, reasonable transmission network planning is the basis of reliable, safe and economic operation of power system. The path planning for transmission network is a high-dimensional, large-scale and non-linear combinatorial optimization problem, which is difficult to solve by traditional mathematical optimization methods. The ant colony algorithm is a very intelligent bionic optimization algorithm, ant colony algorithm is applied to solve the transmission network planning problem in this paper. An improved ant colony algorithm is proposed, which uses it to search the pathes and reduces the coupling between parameters. On the premise of guaranteeing the convergence accuracy, the calculation speed of ant colony algorithm in the planning for transmission network is improved effectively. The results show that the improved ant colony algorithm in the paper has many advantages in computing time and can search the optimal transmission line path more efficiently.

Application of Economic Load Distribution of Power System Based on BAS-PSO

Beetle Antennae Search (BAS) is a h euristic algorithm proposed in 2017. BAS is the bionic optimization algorithm. The principle comes from the fact that Tianniu finds food by sensing the direction of flight by sensing the strength of the smell emitted by the left and right. The BAS algorithm has better optimization speed and precision when solving low-dimensional problems. However, it is easy to fall into local optimum when solving high-dimensional problems. For the purpose of the better search ability of BAS, we h ave combined particle swarm optimization (PSO) algorithm and proposed a new hybrid BAS and PSO algorithm (BAS-PSO).In BASPSO, First use the standard PSO for particle position update and velocity update.Particle learning ontology information and group optimal individual information for evolution, then the particles are seen as individual beetles to use BAS. Each beetle individual performs a local search and continues to cycle until it finds the optimal value.In this paper, the design simulation experiment is applied to the problem of economic load dispatching (ELD) allocation.By comparing the optimization results of BAS-PSO with the three types of algorithms, the results suggest that the BAS-PSO algorithm is better in the ELD optimization problem.

A New Magnetotactic Bacteria Optimization Algorithm Based on Moment Migration.

Magnetotactic bacteria is a kind of polyphyletic group of prokaryotes with the characteristics of magnetotaxis that make them orient and swim along geomagnetic field lines. Its distinct biology characteristics are useful to design new optimization technology. In this paper, a new bionic optimization algorithm named Magnetotactic Bacteria Moment Migration Algorithm (MBMMA) is proposed. In the proposed algorithm, the moments of a chain of magnetosomes are considered as solutions. The moments of relative good solutions can migrate each other to enhance the diversity of the MBMMA. It is compared with variants of PSO on standard functions problems. The experiment results show that the MBMMA is effective in solving optimization problems. It shows better or competitive performance compared with the variants of PSO on most of the tested functions in this paper.

A PSO-SVM-based 24 Hours Power Load Forecasting Model

In order to improve the drawbacks of over-fitting and easily get stuck into local extremes of BACK propagation Neural Network, a new method of combination of wavelet transform and PSO-SVM (Particle Swarm Optimization- Support Vector Machine) power load forecasting model is proposed. By employing wave-let transform, the authors decompose the time sequences of power load into high-frequency and low-frequency parts, namely the low-frequency part forecast with this model and the high-frequency part forecast with weighted average method. With PSO, which is a heuristic bionic optimization algorithm, the authors figure out the prefer-able parameters of SVM, and the model proposed in this paper is tested to be more accurately to forecast the 24h power load than BP model.