Strong Global Search Ability Research Articles

A Hindmarsh–Rose neuron model with electromagnetic radiation control for the mechanical optimization design

This paper proposes a Hindmarsh–Rose(HR) neuron model controlled by electromagnetic radiation to solve the mechanical optimization design problem. A novel memristor for simulating electromagnetic radiation is established to control the dynamical behavior of HR neuron models. By analyzing the HR model and using the numerical analysis methods of phase portrait, bifurcation diagram, Lyapunov exponent and spectral entropy (SE) complexity, the HR model is shown to have a hidden attractor and rich chaotic dynamics. Then, a chaotic optimization algorithm based on HR neuron model is designed to solve the mechanical optimization design of tension/compression coil springs and pressure vessels. The chaotic sequence of this algorithm which has high-quality pseudo-random number, has stronger global search ability. Simulation of two mechanical optimization design problems in comparison with some well-known optimization algorithms demonstrates the superiority of the HR in the solution quality.

Intelligent optimization prediction of screw conveyor speed for earth pressure balance shield machine based on complete ensemble empirical mode decomposition of adaptive noise and deep learning

The excavation stratum of the earth pressure balance (EPB) shield machine is complex and changeable. For ensuring the excavated interface stabilization, the rotational speed of screw conveyor must be precisely controlled to maintain the balance of earth pressure in sealed cabin and to avoid accidents such as ground collapse or upliftment. A data-driven intelligent optimal model is presented by the paper, which organically integrates the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN), sparrow search algorithm (SSA) and long short-term memory (LSTM) network to achieve accurate prediction of screw conveyor rotation speed. Firstly, Pearson correlation analysis method is used to analyze the historical data of shield tunneling, and the tunneling parameters strongly related to the change of screw conveyor rotation speed are obtained as model input. Secondly, the CEEMDAN is used to decompose the obtained parameter data set into several modal components, and then the denoised data is reorganized to capture the changing characteristics of the original data and reduce the complexity of the data. Then, the parameters such as the learning rate of the LSTM network are optimized by using the strong global search ability of SSA to enhance the prediction precision further in the model. Finally, based on decomposed and reorganized data, SSA-LSTM is used to establish a rotation speed intelligent prediction model realize precise controlling of screw conveyor rotation speed. The simulation experiment is carried out by using the construction data of a section of Beijing Metro Line 10, and the results of three evaluation indexes of the model are given: Mean Absolute Error (MAE) is 0.02878, Mean Absolute Percentage Error (MAPE) is 0.00882 and R2 is 0.99909, which shows that the model has good prediction performance. The control effect of the method on the earth pressure balance of the sealed cabin is tested, and the maximum error value is 0.25%, which verifies the engineering applicability of the method.

Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm.

In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, which is based on the problems of slow optimization speed and being easily susceptible to falling into local extremum in the optimization ability of the dandelion optimization algorithm. This hybrid algorithm makes the whole algorithm more diverse by introducing the strong global search ability of particle swarm optimization and the unique individual update rules of the dandelion algorithm (i.e., rising, falling and landing). The ascending and descending stages of dandelion also help to introduce more changes and explorations into the search space, thus better balancing the global and local search. The experimental results show that compared with other algorithms, the proposed PSODO algorithm greatly improves the global optimal value search ability, convergence speed and optimization speed. The effectiveness and feasibility of the PSODO algorithm are verified by solving 22 benchmark functions and three engineering design problems with different complexities in CEC 2005 and comparing it with other optimization algorithms.

Hybrid Artificial Bee Colony Algorithm with Variable Neighborhood Search for Capacitated Vehicle Routing Problem

Aiming at the capacitated vehicle routing problem, a hybrid integer programming model with goal of lowest path cost is constructed, and a hybrid artificial bee colony algorithm with variable neighborhood search based on the model and the characteristics of the CVRP problem is proposed to solve the problem. The hybrid algorithm integrates the artificial bee colony algorithm and the variable neighborhood search algorithm, embeds a multi-variable neighborhood operator in the local search link of the artificial bee colony to carry out iteration. And the operator contains targeted transformation operations on path nodes, strings, and sub paths to ensure the diversity of the bee population. In addition, a variable neighborhood perturbation strategy is used to strengthen the algorithm's ability to escape from local optima. The comparative analysis of the literature study set and its algorithm solution shows that the designed hybrid variable neighborhood artificial bee colony algorithm has strong global search ability and high solution accuracy, especially in solution stability. HABC-VNS can obtain 47 optimal solutions in 74 examples. The average minimum deviation of the optimal solution is 0.34%, and the average deviation of the average CVRP set is 0.57%. The overall performance is better than the algorithm in the comparative literature.

Time-Frequency Fusion Features-Based GSWOA-KELM Model for Gear Fault Diagnosis

To improve the accuracy of gear fault diagnosis and overcome the low diagnostic accuracy of the model caused by manual parameter selection, a combined diagnostic model based on time-frequency fusion features is combined with the improved global search whale optimization algorithm (GSWOA) to optimize the fault diagnosis capability of the kernel extreme learning machine (KELM). First, the time-domain and frequency-domain features of the gear fault state are extracted separately, and feature vectors are constructed through feature fusion, which overcomes the limitations of single features. Second, the GSWOA based on three strategies is used to optimize the regularization coefficient C and kernel function parameter γ of KELM, and a GSWOA-KELM fault diagnosis model is built to avoid the problem of low fault diagnosis accuracy caused by the manual selection of KELM parameters. Finally, the public dataset from Southeast University is taken to verify the performance of the proposed model by comparing it with KELM, SSA-KELM, and WOA-KELM models. The experimental results demonstrate that the improved time-frequency fusion features-based GSWOA-KELM model shows faster convergence speed and stronger global search ability. Compared with KELM, SSA-KELM, and WOA-KELM models, the performance of the proposed model has been improved by 11.33%, 8.67%, and 1.33%, respectively.

Reactor lightweight shielding optimization method based on parallel embedded genetic particle-swarm hybrid algorithm

Reactor lightweight shielding design is a multi-objective optimization problem, which must balance multi-dimensional design parameters such as dose, weight and volume. Combining the strong global search ability of the genetic algorithm (GA) and the fast convergence speed of the particle swarm optimization (PSO) algorithm, we proposed a Parallel Embedded Genetic Particle-swarm Hybrid Algorithm (PEGPHA) for reactor lightweight shielding optimization. After parallelizing GA and PSO generation by generation, reorganizing the population for each generation, and updating the key parameters in the iterative equations, the respective advantages of GA and PSO are balanced. Applied and tested on a small helium-xenon cooled reactor, PEGPHA has a higher convergence rate than GA and has a higher optimization efficiency than PSO. The number of ideal solutions obtained by PEGPHA is 1.23 times higher than GA, 2.22 times higher than PSO, and the average fitness of the ideal solutions is reduced by 2.9% and 2.8% compared with GA and PSO, respectively. The average optimization depth of PEGPHA is also shown to be 1.40 times and 2.10 times higher than that of GA and PSO. PEGPHA provides a larger ideal space for choosing high-quality solutions that are lighter in weight and smaller in volume while meeting dose limits, showing great application potential.

An Effective Obstacle Avoidance and Motion Planning Design for Underwater Telescopic Arm Robots Based on a Tent Chaotic Dung Beetle Algorithm

As the underwater environment is complex, the existence of obstacles will produce a certain collision interference to underwater robot operations, which causes the overall path planning and time costs to increase. In this paper, we propose a Tent chaotic mapping and dung beetle hybrid algorithm (MDBO) application for trajectory optimal planning and effective obstacle avoidance for an underwater telescopic arm robot. The method invokes the unique obstacle avoidance habit and foraging optimization idea of the dung beetle algorithm. Introducing it into the chaotic Tent mapping idea prevents the dung beetle algorithm (DBO) from falling into local optimality and increases the coverage of a global search. Simulation results show that the MDBO algorithm exhibits strong optimization ability and stability when multiple algorithms are verified using eight test functions. The MATLAB test reflects the performance indexes of the six joints of the underwater telescopic arm, and compared with various algorithms, the MDBO algorithm has an obvious convergence trend and strong global search ability. The algorithm is applied to real underwater experiments to verify that the improved dung beetle algorithm has better obstacle avoidance ability and reduces trajectory planning time by 30%, which helps the underwater robot to complete motion planning.

Human memory optimization algorithm: A memory-inspired optimizer for global optimization problems

With the progress of science and technology, optimization problems have become complex. Meta-heuristic algorithms have the advantages of high efficiency and strong global search ability in solving optimization problems, so more and more meta-heuristic algorithms are proposed and investigated deeply, and how to improve the universality of an algorithm is an important issue. In this paper, propose an optimization algorithm that simulates human memory behaviour, fully known as human memory optimization Algorithm, abbreviated as HMO, which simulates the way humans behave in production, stores human preferences for success and failure, simulates the way humans behave in their memory, gradually moving towards better directions and outcomes to find a reasonable optimal solution. The results were compared with other meta-heuristic algorithms in the CEC 2013 test set and showed that HMO has better optimization capabilities, and the feasibility of the algorithm was verified from convergence analysis and parametric analysis experiments. In three engineering optimization problems, HMO was able to find optimal solutions within a reasonable range of parameters, verifying the practicality of HMO.

Personalizing Students' Learning Needs by a Teaching Decision Optimization Method

With the rapid development of educational technology and the deepening of educational system reform, personalized education has gradually become an important topic in education. However, existing classroom teaching decision-making methods often fail to meet students’ personalized learning needs, resulting in some students being unable to reach their full potential in the classroom. To solve this problem, this study proposed a multi-conditional factor classroom teaching decision optimization method based on the improved particle swarm optimization (IPSO) algorithm, and predicted students’ personalized learning needs by combining with the improved ant colony optimization-support vector regression (IACO-SVR) model. First, the IACO-SVR model was used to collect students’ learning data, such as grades, interests, hobbies and learning progress, to accurately predict their needs in different teaching contexts. Second, the IPSO algorithm was used to optimize the multi-conditional factor classroom teaching decisions, thus meeting the personalized needs of students. The IPSO algorithm had strong global search ability, which effectively found the optimal solution to achieve personalized teaching strategies. It is expected that the teaching quality can be improved by predicting the personalized learning needs of students and optimizing classroom teaching decisions in this study, thus providing better support for their comprehensive development. In addition, the results of this study can provide theoretical basis and reference for administrative departments of education and schools to formulate personalized education policies.

Hybrid Slime Mold and Arithmetic Optimization Algorithm with Random Center Learning and Restart Mutation.

The slime mold algorithm (SMA) and the arithmetic optimization algorithm (AOA) are two novel meta-heuristic optimization algorithms. Among them, the slime mold algorithm has a strong global search ability. Still, the oscillation effect in the later iteration stage is weak, making it difficult to find the optimal position in complex functions. The arithmetic optimization algorithm utilizes multiplication and division operators for position updates, which have strong randomness and good convergence ability. For the above, this paper integrates the two algorithms and adds a random central solution strategy, a mutation strategy, and a restart strategy. A hybrid slime mold and arithmetic optimization algorithm with random center learning and restart mutation (RCLSMAOA) is proposed. The improved algorithm retains the position update formula of the slime mold algorithm in the global exploration section. It replaces the convergence stage of the slime mold algorithm with the multiplication and division algorithm in the local exploitation stage. At the same time, the stochastic center learning strategy is adopted to improve the global search efficiency and the diversity of the algorithm population. In addition, the restart strategy and mutation strategy are also used to improve the convergence accuracy of the algorithm and enhance the later optimization ability. In comparison experiments, different kinds of test functions are used to test the specific performance of the improvement algorithm. We determine the final performance of the algorithm by analyzing experimental data and convergence images, using the Wilcoxon rank sum test and Friedman test. The experimental results show that the improvement algorithm, which combines the slime mold algorithm and arithmetic optimization algorithm, is effective. Finally, the specific performance of the improvement algorithm on practical engineering problems was evaluated.

CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures.

Machine learning methods, such as support vector regression (SVR) and gradient boosting, have been introduced into the modeling of power amplifiers and achieved good results. Among various machine learning algorithms, XGBoost has been proven to obtain high-precision models faster with specific parameters. Hyperparameters have a significant impact on the model performance. A traditional grid search for hyperparameters is time-consuming and labor-intensive and may not find the optimal parameters. To solve the problem of parameter searching, improve modeling accuracy, and accelerate modeling speed, this paper proposes a PA modeling method based on CS-GA-XGBoost. The cuckoo search (CS)-genetic algorithm (GA) integrates GA's crossover operator into CS, making full use of the strong global search ability of CS and the fast rate of convergence of GA so that the improved CS-GA can expand the size of the bird nest population and reduce the scope of the search, with a better optimization ability and faster rate of convergence. This paper validates the effectiveness of the proposed modeling method by using measured input and output data of 2.5-GHz-GaN class-E PA under different temperatures (-40 °C, 25 °C, and 125 °C) as examples. The experimental results show that compared to XGBoost, GA-XGBoost, and CS-XGBoost, the proposed CS-GA-XGBoost can improve the modeling accuracy by one order of magnitude or more and shorten the modeling time by one order of magnitude or more. In addition, compared with classic machine learning algorithms, including gradient boosting, random forest, and SVR, the proposed CS-GA-XGBoost can improve modeling accuracy by three orders of magnitude or more and shorten modeling time by two orders of magnitude, demonstrating the superiority of the algorithm in terms of modeling accuracy and speed. The CS-GA-XGBoost modeling method is expected to be introduced into the modeling of other devices/circuits in the radio-frequency/microwave field and achieve good results.

Lithium battery model parameter identification based on the GA-LM algorithm

ABSTRACT The accuracy of lithium battery model parameters is the key to lithium battery state estimation. The offline parameter identification method for lithium batteries requires the nonlinear fitting of the voltage rebound curve of the hybrid pulse discharge experiment. The genetic algorithm has a strong global search ability, but it is easy to fall into local solutions. The Levenberg-Marquardt algorithm has a strong local optimization ability, but the algorithm cannot converge when the prior value is unknown. Given the above problems, this paper proposes a parameter identification method based on the Genetic-Levenberg-Marquardt (GA-LM) algorithm, which takes the sum of the squared model voltage errors as the objective function, and predicts the initial value of the parameter vector through the GA, providing the LM algorithm with prior value. In the case of unknown prior values, the GA-LM algorithm can achieve high-precision nonlinear optimization. Finally, the simulation test under the conditions of constant current discharge and hybrid pulse power discharge. The mean absolute error, mean relative error, and root mean square error of the model voltage in the two working conditions are 7.23 mV, 0.20%, 9.61 mV, and 13.37 mV, 0.37%, 15.44 mV, which shows that the algorithm has high accuracy.

Prediction using multi-objective slime mould algorithm optimized support vector regression model

Different from the traditional neural network models based on empirical risk minimization principle, support vector regression (SVR) minimizes the upper limit of generalization error by the structural risk minimization principle. SVR is suitable for solving nonlinear, small-sample, high-dimensional modelling, and pattern recognition problems. However, the selection of hyperparameters of SVR can significantly affect the prediction accuracy and computational time. This paper proposes a method to optimize the SVR hyperparameters through an intelligent optimization algorithm, specifically multi-objective slime mould algorithm (MOSMA). MOSMA is a new multi-objective optimization algorithm with strong global search ability and fast convergence. The principle and calculation process of MOSMA are first described in detail. The analytical method for transforming the SVR hyperparameter optimization problem into a multi-objective problem is then presented. The process of optimizing two SVR hyperparameters (the penalty coefficient and radial basis function kernel parameter) based on MOSMA is described in steps. Meanwhile, the procedures of algorithm parameter setting, fitness function design, and population update are given. To investigate the characteristics and performance of MOSMA-SVR, two datasets are used to predict the vibration trend of the spindle of CNC milling machine in one and the maximum bending normal stress in the cutting process in the other. The performance of MOSMA-SVR is evaluated by multiple statistical indexes and compared with seven other prediction models (GA-SVR, PSO-SVR, ABC-SVR, GWO-SVR, SSA-SVR, SMA-SVR and MOWOA-SVR).

Bread staling prediction with a multiobjective particle swarm optimization-based bread constitutive modeling method.

Aiming at the complex and cumbersome problems of current bread staling detection technology, a food constitutive modeling method based on the multiobjective particle swarm optimization (MOPSO) was proposed, which can quickly and efficiently identify the creep test parameters for bread, and predict the viscoelastic parameters of bread staling using the analyzed viscoelastic parameters, resulting in convenient and efficient detection of bread staling. Firstly, airflow-laser detection technology was used to carry out rapid, efficient, and non-destructive bread rheological tests to obtain bread creep test data. The MOPSO based on the Pareto set was then used to identify the generalized Kelvin model, and the discrimination accuracy was evaluated by using the inversion results established by the viscoelastic parameters, which resulted in efficient discrimination of creep test data of starch-based products represented by bread. Finally, using extreme learning machine regression (ELM), a prediction model between the analysis results and the moisture content of bread staling was established, and the prediction effect of the analysis results on bread staling was verified. The experimental results show that, when compared to finite element analysis (FEA) and non-linear regression (NLR) to identify creep parameters, the MOPSO overcomes the shortcomings of easy falling into the local optimal solution, is easy to implement, has strong global search ability, and is suitable for the analysis of high-dimensional viscoelastic models of complex foods. The correlation coefficient (R) of the prediction set established by the 12-membered viscoelastic parameters in the prediction model established by multi-element viscoelastic parameters and bread moisture content was 0.847, and the root mean square error (RMSE) was 0.021. This demonstrated that, when combined with MOPSO, airflow-laser detection technology could effectively identify the viscoelastic parameters of bread and establish a method suitable for monitoring bread staling in industrial production. The results of this study provide a reference for the identification of viscoelastic parameters of complex foods and to detect bread staling quickly and efficiently.

Post-stroke rehabilitation optimization & recommendation framework using tele-robotic ecosystem: Industry 4.0 readiness approach

Technological development in biomedical procedures has given an upper understanding of the ease of evaluating and handling critical scenarios and diseases. A sustainable model design is required for the post-medical procedures to maintain the consistency of medical treatment. In this article, a telerobotic-based stroke rehabilitation optimization and recommendation technique cum framework is proposed and evaluated. Selecting optimal features for training deep neural networks can help in optimizing the training time and also improve the performance of the model. To achieve this, we have used Whale Optimization Algorithm (WOA) due to its higher convergence accuracy, better stability, stronger global search ability, and faster convergence speed to streamline the dependency matrix of each attribute associated with post-stroke rehabilitation. Deep Neural Networking assures the selection of datasets from training and testing validation. The proposed framework is developed on providing decision support with a recommendation of activities and task flow, these recommendations are independent and have higher feasibility with the scenario of evaluation. The proposed model achieved a precision of 99.6%, recall of 99.5 %, F1-score of 99.7%, and accuracy of 99.9%, which outperform the other considered optimization algorithms such as antlion and gravitational search algorithms. The proposed technique has provided an efficient recommendation model compared to the trivial SVM-based models and techniques.

AIMD Rule-Based Duty Cycle Scheduling in Wireless Sensor Networks Using Quartile-Directed Adaptive Genetic Algorithm

Wireless sensor networks (WSNs) are attractive, in large part because they do not require wired infrastructure. However, this very characteristic renders them energy-constrained. Duty cycle scheduling (DCS) is thought to contribute to the energy efficiency of sensing. We develop a novel paradigm for modeling WSNs and a DCS strategy based on the additive increase/multiplicative decrease (AIMD) rule. The scheduling problem is framed as an optimization problem with the optimization objectives of lowering energy consumption and enhancing detection ability. The classic GA converges slowly in late evolution phases for using fixed crossover and mutation probability. An adaptable genetic algorithm (AGA) eliminates the above concern by allowing modifying crossover and mutation probability as per fitness values, but, unfortunately, it often fails to select the best individuals. We suggest a quartile-directed adaptive genetic algorithm (Q-AGA) in which individuals with fitness above the first quantile of the last generation were selected. The interquartile range (IQR) is utilized to weight crossover and mutation probability. If IQR is larger, the fitness distribution is more concentrated, and then, we can diminish the crossover and mutation probability, allowing favorable traits to be retained to the greatest extent feasible. Simulation results show that Q-AGA has a faster convergence speed and a stronger global search ability. Furthermore, we change the hypothesis of the system model by considering that the two objectives can be weighted, thereby broadening the application scenarios.

Groundwater contamination source identification based on Sobol sequences-based sparrow search algorithm with a BiLSTM surrogate model.

In the traditional linked simulation-optimization method, solving the optimization model requires massive invoking of the groundwater numerical simulation model, which causes a huge computational load. In the present study, a surrogate model of the origin simulation model was developed using a bidirectional long and short-term memory neural network method (BiLSTM). Compared with the surrogate models built by shallow learning methods (BP neural network) and traditional LSTM methods, the surrogate model built by BiLSTM has higher accuracy and better generalization performance while reducing the computational load. The BiLSTM surrogate model had the highest R2 of the three with 0.9910 and the lowest RMSE with 3.7732g/d. The BiLSTM surrogate model was linked to the optimization model and solved using the sparrow search algorithm based on Sobol sequences (SSAS). SSAS enhances the diversity of the initial population of sparrows by introducing Sobol sequences and introduces nonlinear inertia weights to control the search range and search efficiency. Compared with SSA, SSAS has stronger global search ability and faster search efficiency. And SSAS identifies the contamination source location and release intensity stably and reliably. The average relative error of SSAS for the identification of source location is 9.4%, and the average relative error for the identification of source intensity is 1.83%, which are both lower than that of SSA at 11.12% and 3.03%. This study also applied the Cholesky decomposition method to establish a Gaussian field for hydraulic conductivity to evaluate the feasibility of the simulation-optimization method.

Improved BDS-2/3 Satellite Ultra-Fast Clock Bias Prediction Based with the SSA-ELM Model

Ultra-fast satellite clock bias (SCB) products play an important role in real-time precise point positioning. Considering the low accuracy of ultra-fast SCB, which is unable to meet the requirements of precise point position, in this paper, we propose a sparrow search algorithm to optimize the extreme learning machine (SSA-ELM) algorithm in order to improve the performance of SCB prediction in the Beidou satellite navigation system (BDS). By using the sparrow search algorithm's strong global search and fast convergence ability, we further improve the prediction accuracy of SCB of the extreme learning machine. This study uses ultra-fast SCB data from the international GNSS monitoring assessment system (iGMAS) to perform experiments. First, the second difference method is used to evaluate the accuracy and stability of the used data, demonstrating that the accuracy between observed data (ISUO) and predicted data (ISUP) of the ultra-fast clock (ISU) products is optimal. Moreover, the accuracy and stability of the new rubidium (Rb-II) clock and hydrogen (PHM) clock onboard BDS-3 are superior to those of BDS-2, and the choice of different reference clocks affects the accuracy of SCB. Then, SSA-ELM, quadratic polynomial (QP), and a grey model (GM) are used for SCB prediction, and the results are compared with ISUP data. The results show that when predicting 3 and 6 h based on 12 h of SCB data, the SSA-ELM model improves the prediction model by ~60.42%, 5.46%, and 57.59% and 72.27%, 44.65%, and 62.96% as compared with the ISUP, QP, and GM models, respectively. When predicting 6 h based on 12 h of SCB data, the SSA-ELM model improves the prediction model by ~53.16% and 52.09% and by 40.66% and 46.38% compared to the QP and GM models, respectively. Finally, multiday data are used for 6 h SCB prediction. The results show that the SSA-ELM model improves the prediction model by more than 25% compared to the ISUP, QP, and GM models. In addition, the prediction accuracy of the BDS-3 satellite is better than that of the BDS-2 satellite.

Nonlinear computer image scene and target information extraction based on big data technology

Abstract To explore the extraction of computer image scene and target information, a nonlinear method based on big data technology is proposed. The method can decompose the computer image into a plurality of components when the SAR computer image is processed such as target extraction and computer image compression, which represent different captured image features, respectively. Selecting the most suitable processing method according to the characteristics of different components can greatly improve the performance. Using nonlinear diffusion method, the computer image is decomposed into structural components representing large-scale structural information and texture components representing small-scale detailed information, and the automatic threshold estimation in the diffusion process is studied. The LAIDA criterion is introduced into the automatic threshold solution of nonlinear diffusion-based computer image decomposition to test and evaluate the diffusion process of various diffusion parameter forms. The results show that the experimental outcome of the diffusion decomposition based on automatic threshold estimation is very close on each index, which shows that using automatic threshold estimation, no matter what diffusion index is used, very close results can be obtained. Specifically, for each algorithm, the parameter estimation threshold l for outliers plays an obvious role. The third is the degree of initiative of the estimation process. The larger the L, the larger the outlier, which will lead to a greater extent of the diffusion process, resulting in a continuous decrease in the structural similarity index and compositional correlation. It is proved that the algorithm has strong global search ability, can effectively avoid premature convergence, has fast convergence speed, and good long stability. It can be widely used for optimization of various multimodal functions.

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.