Basic Algorithm Research Articles

Prediction of compressive strength of concrete based on improved artificial bee colony-multilayer perceptron algorithm

There are many factors that affect the compressive strength of concrete. The relationship between compressive strength and these factors is a complex nonlinear problem. Empirical formulas commonly used to predict the compressive strength of concrete are based on summarizing experimental data of several different mix proportions and curing periods, and their generality is poor. This article proposes an improved artificial bee colony algorithm (IABC) and a multilayer perceptron (MLP) coupled model for predicting the compressive strength of concrete. To address the shortcomings of the basic artificial bee colony algorithm, such as easily falling into local optima and slow convergence speed, this article introduces a Gaussian mutation operator into the basic artificial bee colony algorithm to optimize the initial honey source position and designs an MLP neural network model based on the improved artificial bee colony algorithm (IABC-MLP). Compared with traditional strength prediction models, the ABC-MLP model can better capture the nonlinear relationship of the compressive strength of concrete and achieve higher prediction accuracy when considering the compound effect of multiple factors. The IABC-MLP model built in this study is compared with the ABC-MLP and particle swarm optimization (PSO) coupling algorithms. The research shows that IABC can significantly improve the training and prediction accuracy of MLP. Compared with the ABC-MLP and PSO-MLP coupling models, the training accuracy of the IABC-MLP model is increased by 1.6% and 4.5%, respectively. This model is also compared with common individual learning algorithms such as MLP, decision tree (DT), support vector machine regression (SVR), and random forest algorithms (RF). Based on the comparison of prediction results, the proposed method shows excellent performance in all indicators and demonstrates the superiority of heuristic algorithms in predicting the compressive strength of concrete.

Kinematics Analysis and Trajectory Planning of 6-DOF Hydraulic Robotic Arm in Driving Side Pile

Given the difficulty in manually adjusting the position and posture of the pile body during the pile driving process, the improved Denavit-Hartenberg (D-H) parameter method is used to establish the kinematics equation of the mechanical arm, based on the motion characteristics of each mechanism of the mechanical arm of the pile driver, and forward and inverse kinematics analysis is carried out to solve the equation. The mechanical arm of the pile driver is modeled and simulated using the Robotics Toolbox of MATLAB to verify the proposed kinematics model of the mechanical arm of the pile driver. The Monte Carlo method is used to investigate the working space of the mechanical arm of the pile driver, revealing that the arm can extend from the nearest point by 900 mm to the furthest extension of 1800 mm. The actuator’s lowest point allows for a descent of 1000 mm and an ascent of up to 1500 mm. A novel multi-strategy grey wolf optimizer (GWO) algorithm is proposed for robotic arm three-dimensional (3D) path planning, successfully outperforming the basic GWO, ant colony algorithm (ACA), genetic algorithm (GA), and artificial fish swarm algorithm (AFSA) in simulation experiments. Comparative results show that the proposed algorithm efficiently searches for optimal paths, avoiding obstacles with shorter lengths. In robotic arm simulations, the multi-strategy GWO reduces path length by 16.575% and running time by 9.452% compared to the basic GWO algorithm.

Improved genetic algorithm for solving the total weight tardiness job shop scheduling problem

An improved genetic algorithm is proposed for the Job Shop Scheduling Problem with Minimum Total Weight Tardiness (JSSP/TWT). In the proposed improved genetic algorithm, a decoding method based on the Minimum Local Tardiness (MLT) rule of the job is proposed by using the commonly used chromosome coding method of job numbering, and a chromosome recombination operator based on the decoding of the MLT rule is added to the basic genetic algorithm flow. As a way to enhance the quality of the initialized population, a non-delay scheduling combined with heuristic rules for population initialization. and a PiMX (Precedence in Machine crossover) crossover operator based on the priority of processing on the machine is designed. Comparison experiments of simulation scheduling under different algorithm configurations are conducted for randomly generated larger scale JSSP/TWT. Statistical analysis of the experimental evidence indicates that the genetic algorithm based on the above three improvements exhibits significantly superior performance for JSSP/TWT solving: faster convergence and better scheduling solutions can be obtained.

Some Notes on Linear Programming Problems

The paper considers such problems of linear programming as the problem of using raw materials, the problem of composing a diet. The basic definitions, theorems, algorithm and solution of the linear programming problem by the graphical method are given.

Systematic attribute reductions based on double granulation structures and three-view uncertainty measures in interval-set decision systems

Attribute reductions eliminate redundant information to become valuable in data reasoning. In the data context of interval-set decision systems (ISDSs), attribute reductions rely on granulation structures and uncertainty measures; however, the current structures and measures exhibit the singleness limitations, so their enrichments imply corresponding improvements of attribute reductions. Aiming at ISDSs, a fuzzy-equivalent granulation structure is proposed to improve the existing similar granulation structure, dependency degrees are proposed to enrich the existing condition entropy by using algebra-information fusion, so 3×2 attribute reductions are systematically formulated to contain both a basic reduction algorithm (called CAR) and five advanced reduction algorithms. At the granulation level, the similar granulation structure is improved to the fuzzy-equivalent granulation structure by removing the granular repeatability, and two knowledge structures emerge. At the measurement level, dependency degrees are proposed from the algebra perspective to supplement the condition entropy from the information perspective, and mixed measures are generated by fusing dependency degrees and condition entropies from the algebra-information viewpoint, so three-view and three-way uncertainty measures emerge to acquire granulation monotonicity/non-monotonicity. At the reduction level, the two granulation structures and three-view uncertainty measures two-dimensionally produce 3×2 heuristic reduction algorithms based on attribute significances, and thus five new algorithms emerge to improve an old algorithm (i.e., CAR). As finally shown by data experiments, 3×2-systematic construction measures and attribute reductions exhibit the effectiveness and development, comparative results validate the three-level improvements of granulation structures, uncertainty measures, and reduction algorithms on ISDSs. This study resorts to tri-level thinking to enrich the theory and application of three-way decision.

Inversion of heavy metal content in soil using hyperspectral characteristic bands-based machine learning method

The global concern regarding the adverse effects of heavy metal pollution in soil has grown significantly. Accurate prediction of heavy metal content in soil is crucial for environmental protection. This study proposes an inversion analysis method for heavy metals (As, Cd, Cr, Cu, Ni, Pb) in soil based on hyperspectral and machine learning algorithms for 21 soil reference materials from multiple provinces in China. On this basis, an integrated learning model called Stacked RF (the base model is XGBoost, LightGBM, CatBoost, and the meta-model is RF) was established to perform soil heavy metal inversion. Specifically, three popular algorithms were initially employed to preprocess the spectral data, then Random Forest (RF) was used to select the best feature bands to reduce the impact of noise, finally Stacking and four basic machine learning algorithms were used to establish comparisons and analysis of inversion model. Compared with traditional machine learning methods, the stacking model showcases enhanced stability and superior accuracy. Research results indicate that machine learning algorithms, especially ensemble learning models, have better inversion effects on heavy metals in soil. Overall, the MF-RF-Stacking model performed best in the inversion of the six heavy metals. The research results will provide a new perspective on the ensemble learning model method for soil heavy metal content inversion using data of hyperspectral characteristic bands collected from soil reference materials.

Algorithmic Approach to Determination of Epidemic Thresholds in Infectious Disease Surveillance Systems

Introduction: This review is devoted to the algorithmic approach to establishing epidemic thresholds for a wide range of diseases, including influenza and acute respiratory infections. Objective: To compare Russian and foreign approaches to the determination of epidemic thresholds within public health surveillance systems. Materials and methods: To reveal the algorithmic approach to establishing epidemic thresholds in the epidemiological surveillance system, we summarized the results of 14 foreign scientific works and two domestic method guidelines published before December 31, 2023. The literature search was conducted in the eLibrary, CyberLeninka, PubMed, and Google Scholar databases using the keywords “epidemic threshold” and “epidemic”. We compared domestic and foreign algorithms for establishing epidemic thresholds by various characteristics, including the statistical method used, determination of a numerical value of the epidemic threshold, complexity of the algorithm, and the possibility of automating calculations. Results: Here we discuss the classification and comparative characteristics of the basic algorithms for determining epidemic thresholds used in various countries of the world when carrying out epidemiological surveillance (including the syndromic one). We describe the existing methods for establishing and presenting epidemic thresholds, as well as the sequence of steps for performing the Farrington algorithms, the Early Aberration Detection System C1–C3, the Method of Moving Epidemics, the Method of Moving Percentiles, Multi-level identification of increasing activity by indicators taking into account mixed effects, as well as algorithms provided in Russian Method Guidelines MR 3.1.2.0118–17 and MR 3.1.2.0303–22. We also dwell on the problems of development, accuracy assessment and prospects for the implementation of existing and developed algorithms. Conclusions: Current algorithms for establishing epidemic thresholds in epidemiological surveillance systems around the world are diverse; they rely on different statistical methods and vary in complexity. To date, there is no convincing evidence of higher efficiency of any algorithm.

Research on Parameter Inversion of Coal Mining Subsidence Prediction Model Based on Improved Whale Optimization Algorithm

Rapid coal mining results in a series of mining subsidence damages. Predicting surface movement and deformation accurately is essential to reducing mining damage. The accurate determination of parameters for a mining subsidence prediction model is crucial for accurately predicting mining subsidence. In this research, with the incorporation of the Sobol sequence and Lévy flight strategy, we propose an improved whale optimization algorithm (IWOA), thereby enhancing its global optimization capability and mitigating local optimization issues. Our simulation experiment results demonstrate that the IWOA achieved a root mean square error and relative error of less than 0.42 and 0.27%, respectively, indicating its superior accuracy compared to a basic algorithm. The IWOA inversion model also exhibits superior performance compared to a basic algorithm in mitigating gross error interference, Gaussian noise interference, and missing observation point interference. Additionally, it demonstrates enhanced global search capabilities. The IWOA was employed to perform parameter inversion for the working face 1414(1) in Guqiao Coal Mine. The root mean square error of the inversion results did not exceed 6.03, while the subsidence coefficient q, tangent of the main influence angle tanβ, horizontal movement coefficient b, and mining influence propagation angle θ were all below 0.32. The average value of the fitted root mean square error for the subsidence value’s fitted root mean square error and horizontal movement value’s fitted root mean square error of the IWOA was 91.51 mm, which satisfies the accuracy requirements for general engineering applications.

Voice-based age, gender, and language recognition based on ResNet deep model and transfer learning in spectro-temporal domain

In personal identity recognition systems, detecting a person's age, gender, and language using voice signal characteristics is a crucial issue, especially because of the importance of security considerations. Age, gender, and language classification problems are important in signal processing because they are used to analyze and understand human behavior, interactions, and preferences. This can be especially useful in the fields of human-computer interaction, psychology, and social science research. In this paper, a new system for detecting a speaker's age, gender, and language based on deep learning models is presented. Deep learning models have shown great efficacy in various fields of signal processing. For this paper, a range of deep models were tested, including convolutional neural networks (CNNs), recurrent neural network (RNN), and a fine-tuning ResNet34 architecture. Additionally, techniques such as transfer learning were applied to improve the efficiency of the proposed system. The input voice signals are preprocessed by applying the spectro-temporal transform to obtain additional features that can be fed to the ResNet34 model, which is designed specifically for the task of voice signal processing. The dataset used in this paper was sourced from the Mozilla common voice initiative, which is dedicated to advancing speech recognition and language identification technologies. The performance of the proposed algorithm was evaluated in the presence of Gaussian noise to determine its robustness. The experimental results demonstrated that the proposed algorithm significantly outperformed basic algorithms and other deep neural networks in terms of age and gender recognition from voice signals.

An iterative reconstruction algorithm for unsupervised PET image

Objective. In recent years, convolutional neural networks (CNNs) have shown great potential in positron emission tomography (PET) image reconstruction. However, most of them rely on many low-quality and high-quality reference PET image pairs for training, which are not always feasible in clinical practice. On the other hand, many works improve the quality of PET image reconstruction by adding explicit regularization or optimizing the network structure, which may lead to complex optimization problems. Approach. In this paper, we develop a novel iterative reconstruction algorithm by integrating the deep image prior (DIP) framework, which only needs the prior information (e.g. MRI) and sinogram data of patients. To be specific, we construct the objective function as a constrained optimization problem and utilize the existing PET image reconstruction packages to streamline calculations. Moreover, to further improve both the reconstruction quality and speed, we introduce the Nesterov’s acceleration part and the restart mechanism in each iteration. Main results. 2D experiments on PET data sets based on computer simulations and real patients demonstrate that our proposed algorithm can outperform existing MLEM-GF, KEM and DIPRecon methods. Significance. Unlike traditional CNN methods, the proposed algorithm does not rely on large data sets, but only leverages inter-patient information. Furthermore, we enhance reconstruction performance by optimizing the iterative algorithm. Notably, the proposed method does not require much modification of the basic algorithm, allowing for easy integration into standard implementations.

An in silico scheme for optimizing the enzymatic acquisition of natural biologically active peptides based on machine learning and virtual digestion

BackgroundAs a potential natural active substance, natural biologically active peptides (NBAPs) are recently attracting increasing attention. The traditional proteolysis methods of obtaining effective NBAPs are considerably vexing, especially since multiple proteases can be used, which blocks the exploration of available NBAPs. Although the development of virtual digesting brings some degree of convenience, the activity of the obtained peptides remains unclear, which would still not allow efficient access to the NBAPs. It is necessary to develop an efficient and accurate strategy for acquiring NBAPs. ResultsA new in silico scheme named SSA-LSTM-VD, which combines a sparrow search algorithm-long short-term memory (SSA-LSTM) deep learning and virtually digested, was presented to optimize the proteolysis acquisition of NBAPs. Therein, SSA-LSTM reached the highest Efficiency value reached 98.00 % compared to traditional machine learning algorithms, and basic LSTM algorithm. SSA-LSTM was trained to predict the activity of peptides in the proteins virtually digested results, obtain the percentage of target active peptide, and select the appropriate protease for the actual experiment. As an application, SSA-LSTM was employed to predict the percentage of neuroprotective peptides in the virtual digested result of walnut protein, and trypsin was ultimately found to possess the highest value (85.29 %). The walnut protein was digested by trypsin (WPTrH) and the peptide sequence obtained was analyzed closely matches the theoretical neuroprotective peptide. More importantly, the neuroprotective effects of WPTrH had been demonstrated in nerve damage mouse models. SignificanceThe proposed SSA-LSTM-VD in this paper makes the acquisition of NBAPs efficient and accurate. The approach combines deep learning and virtually digested skillfully. Utilizing the SSA-LSTM-VD based strategy holds promise for discovering and developing peptides with neuroprotective properties or other desired biological activities.

Development Model of Basic ECG Algorithm Interpretation based on Android Application to Increase Ability of Nurses

Introduction: ECG interpretation ability by nurses is still not appropriate, thus affecting the accuracy and speed of the next action that will be given to patients to reduce mortality. This study aimed to develop the use of an Android system based ECG interpretation algorithm application to improve the ability of nurse. Methods: Research development design to develop the Android system based ECG interpretation algorithm application. The sampling method used purposive sampling as many as 90 nurses in Universitas Airlangga Hospital, Surabaya, Indonesia. The independent variables were interpretation skills (rhythm, frequency, pacemaker, axis, extrasistole, block, enlargement of heart muscle, coronary heart disease, conclusion). The instruments that are used are ECG recording, ECG interpretation sheet, and focus group disscusion. The analysis used an independent t-test with a significance level of α ≤ 0.05. Results: The results of developing an ECG interpretation algorithm application based on the Android system have received recommendations from experts and feasibility tests have been carried out. There was a relationship between basic ECG interpretation capabilities with the use of an android system based ECG interpretation algorithm application. Increasing the ability of nurses had p value of 0,000. Conclusion: The application of an ECG interpretation algorithm based on an android system can improve nurses' abilities in interpreting basic ECG. For future researchers, it is necessary to develop and refine the Android system-based ECG interpretation algorithm application and this can be applied on a case-by-case basis.

Explore flowchart and pseudocode concepts in algorithms and programming

Programming algorithms are a class of mathematical algorithms that are widely used in computer science and engineering. They are used to solve problems involving mathematical calculations, but in their implementation, algorithms do not always handle complex calculations, especially when genetic algorithms are often used. This article uses the method of literature study or literature review to evaluate several studies related to the topic discussed, especially in introducing the basic concepts of programming algorithms, especially for beginners, focusing on the basic understanding required. The research includes a review of algorithmic concepts, algorithm notation, characteristics, properties, and the basic structure of algorithm learning, along with the steps of implementing programming algorithms. The conclusion of this article is that learning basic programming algorithms is an essential necessity for anyone who wants to get started in the world of programming.

Group Intelligent City Mobile Communication Network's Control Strategy based on Cellular Internet of Things

Mobile communication network optimization heavily depends on power control technology, which impacts the effectiveness of the network. This paper aims to enhance control over nonlinear mobile communication networks and achieve superior performance by applying the particle swarm optimization (PSO) algorithm in the control domain. Addressing limitations in the basic PSO algorithm, improvements are made and applied to urban mobile communication networks. The methodology involves modifying the PSO algorithm to address identified issues and applying the enhanced algorithm to communication network scenarios. Simulation results indicate that with an initial particle count of 10 and 100 iterations, the optimized values for and are 0.691 and 0.486, respectively, resulting in an objective function value of 55.514. This achievement validates the successful implementation of the optimization process for mobile communication network control. The findings reveal that the proposed grad particle swarm optimization (Grad-PSO) algorithm exhibits mobile network optimization by robust search capability and rapid convergence.

Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital.

In recent years, artificial intelligence (AI) has shown promising applications in various scientific domains, including biochemical analysis research. However, the effectiveness of AI in modeling small-scale, imbalanced datasets remains an open question in such fields. This study explores the capabilities of eight basic AI algorithms, including ridge regression, logistic regression, random forest regression, and others, in modeling a small, imbalanced clinical dataset (total n = 387, class 0 = 27, class 1 = 360) related to the records of the biochemical blood tests from the patients with multiple wasp stings (MWS). Through rigorous evaluation using k-fold cross-validation and comprehensive scoring, we found that none of the models could effectively model the data. Even after fine-tuning the hyperparameters of the best-performing models, the results remained below acceptable thresholds. The study highlights the challenges of applying AI to small-scale datasets with imbalanced groups in biochemical or clinical research and emphasizes the need for novel algorithms tailored to small-scale data. The findings also call for further exploration into techniques such as transfer learning and data augmentation, and they underline the importance of understanding the minimum dataset scale required for effective AI modeling in biochemical contexts.

User profiling for Chinese super-new generation wine consumers based on improved density peak clustering algorithm

PurposeFor a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.Design/methodology/approachWe first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.FindingsIn this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.Practical implicationsYoung Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.Originality/valueIn this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.

Implementation of Bug1 and Bug2 Basic Path-Planning Algorithms for a TurtleBot 3 Robot in ROS Noetic

Implementation of Bug1 and Bug2 Basic Path-Planning Algorithms for a TurtleBot 3 Robot in ROS Noetic

Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM

The concentration of ammonia nitrogen is significant for intensive aquaculture, and if the concentration of ammonia nitrogen is too high, it will seriously affect the survival state of aquaculture. Therefore, prediction and control of the ammonia nitrogen concentration in advance is essential. This paper proposed a combined model based on X Adaptive Boosting (XAdaBoost) and the Long Short-Term Memory neural network (LSTM) to predict ammonia nitrogen concentration in mariculture. Firstly, the weight assignment strategy was improved, and the number of correction iterations was introduced to retard the shortcomings of data error accumulation caused by the AdaBoost basic algorithm. Then, the XAdaBoost algorithm generated and combined several LSTM su-models to predict the ammonia nitrogen concentration. Finally, there were two experiments conducted to verify the effectiveness of the proposed prediction model. In the ammonia nitrogen concentration prediction experiment, compared with the LSTM and other comparison models, the RMSE of the XAdaBoost–LSTM model was reduced by about 0.89–2.82%, the MAE was reduced by about 0.72–2.47%, and the MAPE was reduced by about 8.69–18.39%. In the model stability experiment, the RMSE, MAE, and MAPE of the XAdaBoost–LSTM model decreased by about 1–1.5%, 0.7–1.7%, and 7–14%. From these two experiments, the evaluation indexes of the XAdaBoost–LSTM model were superior to the comparison models, which proves that the model has good prediction accuracy and stability and lays a foundation for monitoring and regulating the change of ammonia nitrogen concentration in the future.

A Supervised Reinforcement Learning Algorithm for Controlling Drone Hovering

The application of drones carrying different devices for aerial hovering operations is becoming increasingly widespread, but currently there is very little research relying on reinforcement learning methods for hovering control, and it has not been implemented on physical machines. Drone’s behavior space regarding hover control is continuous and large-scale, making it difficult for basic algorithms and value-based reinforcement learning (RL) algorithms to have good results. In response to this issue, this article applies a watcher-actor-critic (WAC) algorithm to the drone’s hover control, which can quickly lock the exploration direction and achieve high robustness of the drone’s hover control while improving learning efficiency and reducing learning costs. This article first utilizes the actor-critic algorithm based on behavioral value Q (QAC) and the deep deterministic policy gradient algorithm (DDPG) for drone hover control learning. Subsequently, an actor-critic algorithm with an added watcher is proposed, in which the watcher uses a PID controller with parameters provided by a neural network as the dynamic monitor, transforming the learning process into supervised learning. Finally, this article uses a classic reinforcement learning environment library, Gym, and a current mainstream reinforcement learning framework, PARL, for simulation, and deploys the algorithm to a practical environment. A multi-sensor fusion strategy-based autonomous localization method for unmanned aerial vehicles is used for practical exercises. The simulation and experimental results show that the training episodes of WAC are reduced by 20% compared to the DDPG and 55% compared to the QAC, and the proposed algorithm has a higher learning efficiency, faster convergence speed, and smoother hovering effect compared to the QAC and DDPG.

Research on path planning of tea picking robot based on ant colony algorithm

Robot tea picking is an inevitable trend to solve the problem of tea picking, and the picking path planning is directly related to the robot picking efficiency. An Improved Ant Colony Algorithm (IACA) is proposed, which firstly introduces the adaptive adjustment mechanism into the pheromone volatilization factor of the ant colony algorithm, and then sets the pheromone volatilization factor with a high initial value to improve the searching speed, and then adjusts the size of its value within a certain range in real time according to the iterative results, and finally solves the problem that the searching of the ant colony algorithm is prone to fall into the local optimal solution. On the basis of visual recognition of tea leaves and obtaining coordinate information, the improved ant colony algorithm is used to enter the path planning simulation experiments, and the planning results of the other six algorithms are compared with the similar algorithms and dissimilar algorithms, and the experimental results indicate that the IACA method has improved the shortest path index by 5% compared to the basic ant colony algorithm, and by an average of 4% compared to similar improved ant colony algorithms. In comparison to different optimization algorithms, the enhancement has an average increase of 6%; Furthermore, the convergence speed has been improved by 60% compared to six other methods. The standard deviation of repeated experimental results is 50% lower than the other six methods. The gap between the results of multiple repeated experiments is small, the degree of fluctuation is low, and the calculation results are more stable, which verifies the superiority of IACA method. Therefore, the improvement of the ant colony algorithm makes the pheromone concentration value with adaptive adjustment ability, which reflects good effects in path optimization, convergence speed improvement, stability of results, etc., and has good application value for the path planning problems such as tea picking, which has complex paths and large computational volume.