Feature Selection(FS)is an important data management technique that aims to minimize redundant information in a dataset.This work proposes DENGO,an improved version of the Northern Goshawk Optimization(NGO),to address...Feature Selection(FS)is an important data management technique that aims to minimize redundant information in a dataset.This work proposes DENGO,an improved version of the Northern Goshawk Optimization(NGO),to address the FS problem.The NGO is an efficient swarm-based algorithm that takes its inspiration from the predatory actions of the northern goshawk.In order to overcome the disadvantages that NGO is prone to local optimum trap,slow convergence speed and low convergence accuracy,two strategies are introduced in the original NGO to boost the effectiveness of NGO.Firstly,a learning strategy is proposed where search members learn by learning from the information gaps of other members of the population to enhance the algorithm's global search ability while improving the population diversity.Secondly,a hybrid differential strategy is proposed to improve the capability of the algorithm to escape from the trap of the local optimum by perturbing the individuals to improve convergence accuracy and speed.To prove the effectiveness of the suggested DENGO,it is measured against eleven advanced algorithms on the CEC2015 and CEC2017 benchmark functions,and the obtained results demonstrate that the DENGO has a stronger global exploration capability with higher convergence performance and stability.Subsequently,the proposed DENGO is used for FS,and the 29 benchmark datasets from the UCL database prove that the DENGO-based FS method equipped with higher classification accuracy and stability compared with eight other popular FS methods,and therefore,DENGO is considered to be one of the most prospective FS techniques.DENGO's code can be obtained at https://www.mathworks.com/matlabcentral/fileexchange/158811-project1.展开更多
Optimization algorithms play a pivotal role in enhancing the performance and efficiency of systems across various scientific and engineering disciplines.To enhance the performance and alleviate the limitations of the ...Optimization algorithms play a pivotal role in enhancing the performance and efficiency of systems across various scientific and engineering disciplines.To enhance the performance and alleviate the limitations of the Northern Goshawk Optimization(NGO)algorithm,particularly its tendency towards premature convergence and entrapment in local optima during function optimization processes,this study introduces an advanced Improved Northern Goshawk Optimization(INGO)algorithm.This algorithm incorporates a multifaceted enhancement strategy to boost operational efficiency.Initially,a tent chaotic map is employed in the initialization phase to generate a diverse initial population,providing high-quality feasible solutions.Subsequently,after the first phase of the NGO’s iterative process,a whale fall strategy is introduced to prevent premature convergence into local optima.This is followed by the integration of T-distributionmutation strategies and the State Transition Algorithm(STA)after the second phase of the NGO,achieving a balanced synergy between the algorithm’s exploitation and exploration.This research evaluates the performance of INGO using 23 benchmark functions alongside the IEEE CEC 2017 benchmark functions,accompanied by a statistical analysis of the results.The experimental outcomes demonstrate INGO’s superior achievements in function optimization tasks.Furthermore,its applicability in solving engineering design problems was verified through simulations on Unmanned Aerial Vehicle(UAV)trajectory planning issues,establishing INGO’s capability in addressing complex optimization challenges.展开更多
In order to more accurately classify the stability of roadway surrounding rock and identify dangerous areas in a timely manner to prevent roadway collapse and other disasters,this study proposes an Improved Northern G...In order to more accurately classify the stability of roadway surrounding rock and identify dangerous areas in a timely manner to prevent roadway collapse and other disasters,this study proposes an Improved Northern Gok algorithm(INGO)and Random Forest(RF)roadway surrounding rock stability prediction model.This model combines the improved INGO-RF based on the analysis of influencing factors of roadway surrounding rock stability.First,three strategies were employed to enhance the Northern Gob algorithm(NGO):logistic chaotic mapping,refraction reverse learning,and improved sine and cosine.Subsequently,INGO was utilized to optimize the number of decision trees and the minimum number of leaf nodes for RF species in order to improve the prediction accuracy of RF.Secondly,a data set consisting of 34 groups of roadway surrounding rock data is selected.The input indexes of the model include the roof strength,two-wall strength,floor strength,burial depth,roadway pillar width,ratio of direct roof thickness to mining height,and surrounding rock integrity.Meanwhile,surrounding rock stability is considered as the output index.Particle swarm optimization backpropagation neural network(PSO-BPNN),genetic algorithm optimization support vector machine(GA-SVM),Sparrow Search Algorithm optimization RF(SSA-RF)models were introduced to compare the predictive results with the INGO-RF model,and the results showed that:INGO-RF model has the best performance in the comparison of various performance indicators;compared with other models,the accuracy rate(Ac)in the test set has increased by 0.12–0.40,the accuracy rate(Pr)has increased by 0.07–0.65,and the recall rate(Re)has increased by 0.08–0.37;the harmonic mean(F1-Score)of the recall rate increased by 0.08–0.52,the mean absolute error(MAE)decreased by 0.1428–0.4285,the mean absolute percentage error(MAPE)decreased by 7.15%–28.57%,and the root mean square error(RMSE)decreased by 0.1565–0.3779;and finally,the data on surrounding rock conditions of roadways in multiple mining areas in Shanxi Province were collected to test the INGO-RF model.The results indicate that the predicted outcomes closely align with the actual results,demonstrating a certain level of reliability and stability,which can better meet the practical needs of engineering and avoid the occurrence of mine disasters.展开更多
基金supported in part by the National Natural Science Foundation of China's top-level program under grant No.52275480in part by Reserve projects for centralized guidance of local science and technology development funds under grant No.QKHZYD[2023]002.
文摘Feature Selection(FS)is an important data management technique that aims to minimize redundant information in a dataset.This work proposes DENGO,an improved version of the Northern Goshawk Optimization(NGO),to address the FS problem.The NGO is an efficient swarm-based algorithm that takes its inspiration from the predatory actions of the northern goshawk.In order to overcome the disadvantages that NGO is prone to local optimum trap,slow convergence speed and low convergence accuracy,two strategies are introduced in the original NGO to boost the effectiveness of NGO.Firstly,a learning strategy is proposed where search members learn by learning from the information gaps of other members of the population to enhance the algorithm's global search ability while improving the population diversity.Secondly,a hybrid differential strategy is proposed to improve the capability of the algorithm to escape from the trap of the local optimum by perturbing the individuals to improve convergence accuracy and speed.To prove the effectiveness of the suggested DENGO,it is measured against eleven advanced algorithms on the CEC2015 and CEC2017 benchmark functions,and the obtained results demonstrate that the DENGO has a stronger global exploration capability with higher convergence performance and stability.Subsequently,the proposed DENGO is used for FS,and the 29 benchmark datasets from the UCL database prove that the DENGO-based FS method equipped with higher classification accuracy and stability compared with eight other popular FS methods,and therefore,DENGO is considered to be one of the most prospective FS techniques.DENGO's code can be obtained at https://www.mathworks.com/matlabcentral/fileexchange/158811-project1.
基金supported by theKey Research and Development Project of Hubei Province(No.2023BAB094)the Key Project of Science and Technology Research Program of Hubei Educational Committee(No.D20211402)the Open Foundation of HubeiKey Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System(No.HBSEES202309).
文摘Optimization algorithms play a pivotal role in enhancing the performance and efficiency of systems across various scientific and engineering disciplines.To enhance the performance and alleviate the limitations of the Northern Goshawk Optimization(NGO)algorithm,particularly its tendency towards premature convergence and entrapment in local optima during function optimization processes,this study introduces an advanced Improved Northern Goshawk Optimization(INGO)algorithm.This algorithm incorporates a multifaceted enhancement strategy to boost operational efficiency.Initially,a tent chaotic map is employed in the initialization phase to generate a diverse initial population,providing high-quality feasible solutions.Subsequently,after the first phase of the NGO’s iterative process,a whale fall strategy is introduced to prevent premature convergence into local optima.This is followed by the integration of T-distributionmutation strategies and the State Transition Algorithm(STA)after the second phase of the NGO,achieving a balanced synergy between the algorithm’s exploitation and exploration.This research evaluates the performance of INGO using 23 benchmark functions alongside the IEEE CEC 2017 benchmark functions,accompanied by a statistical analysis of the results.The experimental outcomes demonstrate INGO’s superior achievements in function optimization tasks.Furthermore,its applicability in solving engineering design problems was verified through simulations on Unmanned Aerial Vehicle(UAV)trajectory planning issues,establishing INGO’s capability in addressing complex optimization challenges.
基金supported by the National Natural Science Foun-dation of China (No.52174188)the Shanxi Basic Research Program (Free Exploration)Project of China (No.202203021222300).
文摘In order to more accurately classify the stability of roadway surrounding rock and identify dangerous areas in a timely manner to prevent roadway collapse and other disasters,this study proposes an Improved Northern Gok algorithm(INGO)and Random Forest(RF)roadway surrounding rock stability prediction model.This model combines the improved INGO-RF based on the analysis of influencing factors of roadway surrounding rock stability.First,three strategies were employed to enhance the Northern Gob algorithm(NGO):logistic chaotic mapping,refraction reverse learning,and improved sine and cosine.Subsequently,INGO was utilized to optimize the number of decision trees and the minimum number of leaf nodes for RF species in order to improve the prediction accuracy of RF.Secondly,a data set consisting of 34 groups of roadway surrounding rock data is selected.The input indexes of the model include the roof strength,two-wall strength,floor strength,burial depth,roadway pillar width,ratio of direct roof thickness to mining height,and surrounding rock integrity.Meanwhile,surrounding rock stability is considered as the output index.Particle swarm optimization backpropagation neural network(PSO-BPNN),genetic algorithm optimization support vector machine(GA-SVM),Sparrow Search Algorithm optimization RF(SSA-RF)models were introduced to compare the predictive results with the INGO-RF model,and the results showed that:INGO-RF model has the best performance in the comparison of various performance indicators;compared with other models,the accuracy rate(Ac)in the test set has increased by 0.12–0.40,the accuracy rate(Pr)has increased by 0.07–0.65,and the recall rate(Re)has increased by 0.08–0.37;the harmonic mean(F1-Score)of the recall rate increased by 0.08–0.52,the mean absolute error(MAE)decreased by 0.1428–0.4285,the mean absolute percentage error(MAPE)decreased by 7.15%–28.57%,and the root mean square error(RMSE)decreased by 0.1565–0.3779;and finally,the data on surrounding rock conditions of roadways in multiple mining areas in Shanxi Province were collected to test the INGO-RF model.The results indicate that the predicted outcomes closely align with the actual results,demonstrating a certain level of reliability and stability,which can better meet the practical needs of engineering and avoid the occurrence of mine disasters.
