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Swarm intelligence optimization and its application in geophysical data inversion 认领 引用 被引量：34
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作者 Yuan Sanyi Wang Shangxu Tian Nan 《Applied Geophysics》 SCIE 2009年第2期166-174,共9页
The inversions of complex geophysical data always solve multi-parameter,nonlinear,and multimodal optimization problems.Searching for the optimal inversion solutions is similar to the social behavior observed in swarms... The inversions of complex geophysical data always solve multi-parameter,nonlinear,and multimodal optimization problems.Searching for the optimal inversion solutions is similar to the social behavior observed in swarms such as birds and ants when searching for food.In this article,first the particle swarm optimization algorithm was described in detail,and ant colony algorithm improved.Then the methods were applied to three different kinds of geophysical inversion problems:(1)a linear problem which is sensitive to noise,(2)a synchronous inversion of linear and nonlinear problems,and(3)a nonlinear problem.The results validate their feasibility and efficiency.Compared with the conventional genetic algorithm and simulated annealing,they have the advantages of higher convergence speed and accuracy.Compared with the quasi-Newton method and Levenberg-Marquardt method,they work better with the ability to overcome the locally optimal solutions. 展开更多
关键词 Swarm intelligence optimization geophysical inversion multimodal particle swarm optimization algorithm
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Analytical reentry guidance framework based on swarm intelligence optimization and altitude-energy profile 认领 引用 被引量：8
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作者 Hui XU Guangbin CAI +1 位作者 Chaoxu MU Xin LI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2023年第12期336-348,共13页
Aimed at improving the real-time performance of guidance instruction generation,an analytical hypersonic reentry guidance framework is presented.The key steps of the novel guidance framework are the parameterization o... Aimed at improving the real-time performance of guidance instruction generation,an analytical hypersonic reentry guidance framework is presented.The key steps of the novel guidance framework are the parameterization of reentry guidance problems and the optimization of parameters.First,a quintic polynomial function of energy was designed to describe the altitude profile.Then,according to the altitude-energy profile,the altitude,velocity,flight path angle,and bank angle were obtained analytically,which naturally met the terminal constraints.In addition,the angle of the attack profile was determined using the velocity parameter.The swarm intelligent optimization algorithms were used to optimize the parameters.The path constraints were enforced by the penalty function method.Finally,extensive simulations were carried out in both nominal and dispersed cases,and the simulation results showed that the proposed guidance framework was effective,high-precision,and robust in different scenarios. 展开更多
关键词 Analytical solution Hypersonic glide vehicle Improved swarm intelligent optimization Monte-Carlo simulation Reentry guidance method
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Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用 被引量：2
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作者 WU Jin GAO Yaqiong +2 位作者 SU Zhengdong CHONG Gege XIONG Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期828-842,I0002,共15页
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for... A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems. 展开更多
关键词 sea otter optimization algorithm(SOOA) swarm intelligence optimization wireless sensor network coverage optimization
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Improved Artificial Rabbit Optimization Algorithm Fused with Particle Swarm Optimization for Wireless Sensor Network Coverage Optimization 认领 引用 被引量：1
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作者 WU Jin SU Zhengdong 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期375-389,共15页
Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for ... Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for network coverage optimization.ARO-PSO successfully combines the stochastic characteristics of ARO and the global characteristics of PSO.Firstly,to optimize the quality of the initial population,Sine chaos mapping is introduced to initialize the population;secondly,to better balance the exploration and exploitation,adaptive settings are made;finally,combined with the characteristics of the ARO energy factor,a population decreasing strategy is introduced to further accelerate the convergence speed of the algorithm.Experimental and analytical comparisons are made with ARO and PSO and 6 other excellent optimizers on 13 benchmark functions.The results show that ARO-PSO largely outperforms the original algorithm.Finally,ARO-PSO is applied to WSN coverage optimization experiments in 2D and 3D environments,and the proposed algorithm exhibits higher network coverage and improves the monitoring quality of the network compared to standard ARO and PSO and other state-of-the-art algorithms.The experimental results fully demonstrate the superiority of the ARO-PSO-based WSN node deployment optimization method. 展开更多
关键词 wireless sensor network(WSN) swarm intelligence optimization artificial rabbits optimization(ARO) particle swarm optimization(PSO) coverage optimization
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Interpretable CEEMDAN-SMA-LSSVM hybrid model for predicting shield tunnel-induced settlement 认领 引用
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作者 Shaoqiang Meng Zhenming Shi Marte Gutierrez 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第10期6179-6194,共16页
Accurate and interpretable prediction of shield tunnel-induced settlement poses a significant challenge due to the complex interplay of various influencing factors.This paper proposes a novel interpretable hybrid mode... Accurate and interpretable prediction of shield tunnel-induced settlement poses a significant challenge due to the complex interplay of various influencing factors.This paper proposes a novel interpretable hybrid model that combines complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),slime mold algorithm(SMA),and least squares support vector machine(LSSVM)to enhance prediction accuracy and model transparency.The CEEMDAN method,optimized by SMA,decomposes settlement data into intrinsic mode functions(IMFs)and residuals,thereby reducing data noise.The LSSVM,also optimized by SMA,is then applied to predict each IMF and residual.The final settlement prediction is derived from the aggregation of these results.The model was rigorously validated using the Changsha(China)and Singapore Metro projects,demonstrating superior performance to traditional machine learning models.The evaluation metrics,including root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2),underscore the model's effectiveness.The model achieved the lowest error rates and highest accuracy across these metrics.Notably,Shapley additive explanations(SHAP)provided insights into the model's decision-making process,identifying shield stoppage and moisture content as the most influential factors in settlement prediction.This study contributes to the advancement of the methodological framework for predicting tunnel settlement.It addresses the discrepancy between prediction accuracy and interpretability,providing a robust tool for practical engineering applications. 展开更多
关键词 Machine learning Geotechnical engineering Swarm intelligence optimization algorithm Shapley additive explanations(SHAP)
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An Adaptive Fruit Fly Optimization Algorithm for Optimization Problems 认领 引用
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作者 L. Q. Zhang J. Xiong J. K. Liu 《Journal of Applied Mathematics and Physics》 2023年第11期3641-3650,共10页
In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local ... In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance. 展开更多
关键词 Swarm Intelligent Optimization Algorithm Fruit Fly Optimization Algorithm Adaptive Step Local Optimum Convergence Speed
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Product quality prediction based on RBF optimized by firefly algorithm 认领 引用 被引量：4
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作者 HAN Huihui WANG Jian +1 位作者 CHEN Sen YAN Manting 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第1期105-117,共13页
With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred... With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality. 展开更多
关键词 product quality prediction data pre-processing radial basis function swarm intelligence optimization algorithm
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Thermal Properties Reconstruction and Temperature Fields in Asphalt Pavements: Inverse Problem and Optimisation Algorithms 认领 引用
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作者 Zhonghai Jiang Qian Wang +1 位作者 Liangbing Zhou Chun Xiao 《Fluid Dynamics & Materials Processing》 EI 2023年第6期1693-1708,共16页
A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt pavement.The calculation of each layer only needs four iterations to achieve convergence.... A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt pavement.The calculation of each layer only needs four iterations to achieve convergence.Furthermore,in order to improve the calculation accuracy a swarm intelligence optimization algorithm is also exploited to inversely analyze the laws by which the thermal physical parameters of the asphalt pavement materials change with temperature.Using the basic cuckoo and the gray wolf algorithms,an adaptive hybrid optimization algorithm is obtained and used to determine the relationship between the thermal diffusivity of two types of asphalt pavement materials and the temperature.As shown by the results,the prediction accuracy achievable with this approach is higher than that of the linear model. 展开更多
关键词 Asphalt pavement temperature field swarm intelligence optimization algorithm prediction
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An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials 认领 引用
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作者 Abidhan Bardhan Raushan Kumar Singh +1 位作者 Mohammed Alatiyyah Sulaiman Abdullah Alateyah 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1521-1555,共35页
This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf o... This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness. 展开更多
关键词 Metakaolin-contained cemented materials compressive strength extreme learning machine grey wolf optimizer swarm intelligence uncertainty analysis artificial intelligence
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