Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used ...Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems.However,with the increased dimensions,higher requirements are put forward for algorithm performance.The double population whale optimization algorithm with distributed collaboration and reverse learning ability(DCRWOA)is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems.In the DCRWOA algorithm,the novel double population search strategy is constructed.Meanwhile,the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area.Numerical experi-ments are carried out using standard test functions with different dimensions(10,50,100,200).The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algo-rithm.The results show that the DCRWOA algorithm has higher optimization accuracy and stability,and the convergence speed is significantly improved.Therefore,the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.展开更多
Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software ...Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software defect prediction can be effectively performed using traditional features,but there are some redundant or irrelevant features in them(the presence or absence of this feature has little effect on the prediction results).These problems can be solved using feature selection.However,existing feature selection methods have shortcomings such as insignificant dimensionality reduction effect and low classification accuracy of the selected optimal feature subset.In order to reduce the impact of these shortcomings,this paper proposes a new feature selection method Cubic TraverseMa Beluga whale optimization algorithm(CTMBWO)based on the improved Beluga whale optimization algorithm(BWO).The goal of this study is to determine how well the CTMBWO can extract the features that are most important for correctly predicting software defects,improve the accuracy of fault prediction,reduce the number of the selected feature and mitigate the risk of overfitting,thereby achieving more efficient resource utilization and better distribution of test workload.The CTMBWO comprises three main stages:preprocessing the dataset,selecting relevant features,and evaluating the classification performance of the model.The novel feature selection method can effectively improve the performance of SDP.This study performs experiments on two software defect datasets(PROMISE,NASA)and shows the method’s classification performance using four detailed evaluation metrics,Accuracy,F1-score,MCC,AUC and Recall.The results indicate that the approach presented in this paper achieves outstanding classification performance on both datasets and has significant improvement over the baseline models.展开更多
Chinese spelling check is a task to detect and correct spelling mistakes in Chinese texts.Existing research aims to enhance the text representation and exploit multi-source information to improve the detection and cor...Chinese spelling check is a task to detect and correct spelling mistakes in Chinese texts.Existing research aims to enhance the text representation and exploit multi-source information to improve the detection and correction capabilities of models,with little attention to improving the ability to distinguish confusable words.Contrastive learning,aiming to minimize the distance in the representation space between similar sample pairs,has recently become a dominant technique in natural language processing.Inspired by contrastive learning,we present a novel method for Chinese spelling checking,RCL-CSC,which consists of three modules:language representation,spelling check,and reverse contrastive learning.Specifically,we propose a reverse contrastive learning method,which explicitly forces the model to minimize the agreement between similar examples,namely,the phonetically and visually confusable characters.Experimental results show that our method is model-agnostic,and thus can be combined with existing Chinese spelling check models to achieve state-of-the-art performance.展开更多
Dear Editor,In dynamic environments,the memory system of the brain must be able to perceive and process conflicting experiences to reach an adaptive decision.In Drosophila,in contrast to consistent experiences,conflic...Dear Editor,In dynamic environments,the memory system of the brain must be able to perceive and process conflicting experiences to reach an adaptive decision.In Drosophila,in contrast to consistent experiences,conflicting experiences trigger significantly increased Rac1 activity which mediates active forgetting [1].The ability to cope with conflicting experiences but not simple learning experiences is impaired in mutants of multiple autism-risk genes [2].展开更多
Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data collection.Path planning of UAV advancing along river valleys in wild environments is one of the first and mos...Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data collection.Path planning of UAV advancing along river valleys in wild environments is one of the first and most difficult problems faced by unmanned surveys of debris flow valleys.This study proposes a new hybrid bat optimization algorithm,GRE-Bat(Good point set,Reverse learning,Elite Pool-Bat algorithm),for unmanned exploration path planning of debris flow sources in outdoor environments.In the GRE-Bat algorithm,the good point set strategy is adopted to evenly distribute the population,ensure sufficient coverage of the search space,and improve the stability of the convergence accuracy of the algorithm.Subsequently,a reverse learning strategy is introduced to increase the diversity of the population and improve the local stagnation problem of the algorithm.In addition,an Elite pool strategy is added to balance the replacement and learning behaviors of particles within the population based on elimination and local perturbation factors.To demonstrate the effectiveness of the GRE-Bat algorithm,we conducted multiple simulation experiments using benchmark test functions and digital terrain models.Compared to commonly used path planning algorithms such as the Bat Algorithm(BA)and the Improved Sparrow Search Algorithm(ISSA),the GRE-Bat algorithm can converge to the optimal value in different types of test functions and obtains a near-optimal solution after an average of 60 iterations.The GRE-Bat algorithm can obtain higher quality flight routes in the designated environment of unmanned investigation in the debris flow gully basin,demonstrating its potential for practical application.展开更多
A hybrid strategy is proposed to solve the problems of poor population diversity, insufficient convergence accuracy and susceptibility to local optimal values in the original Arctic Puffin Optimization (APO) algorithm...A hybrid strategy is proposed to solve the problems of poor population diversity, insufficient convergence accuracy and susceptibility to local optimal values in the original Arctic Puffin Optimization (APO) algorithm, Enhanced Tangent Flight Adaptive Arctic Puffin Optimization with Elite initialization and Adaptive t-distribution Mutation (ETAAPO). Elite initialization improves initial population quality and accelerates convergence. Tangent Flight of the Tangent search algorithm replaces Levy Flight to balance local search and global exploration. The adaptive t-distribution mutation strategy enhances the optimization ability. ETAAPO was tested on CEC2021 functions, Wilcoxon rank-sum tests, and engineering problems, demonstrating superior optimization performance and faster convergence.展开更多
Effectively tuning the parameters of proportional-integralderivative(PID)controllers has persistently posed a challenge in control engineering.This study proposes enhanced hippopotamus optimization(EHO)to address this...Effectively tuning the parameters of proportional-integralderivative(PID)controllers has persistently posed a challenge in control engineering.This study proposes enhanced hippopotamus optimization(EHO)to address this challenge.Latin hypercube sampling and adaptive lens reverse learning are used to initialize the population to improve population diversity and enhance global search.Additionally,an adaptive perturbation mechanism is introduced into the position update in the exploration phase.To validate the performance of EHO,it is benchmarked against hippopotamus optimization and four classical or state-of-the-art intelligent algorithms using the CEC2022 test suite.The effectiveness of EHO is further evaluated by applying it in tuning PID controllers for different types of systems.The performance of EHO is compared with five other algorithms and the classical Ziegler-Nichols method.Analysis of convergence curves,step responses,box plots,and radar charts indicates that EHO outperforms the compared methods in accuracy,convergence speed,and stability.Finally,EHO is used to tune the cascade PID controller for trajectory tracking in a quadrotor unmanned aerial vehicle to assess its applicability.The simulation results indicate that the integrals of the time absolute error for the position channels(c,y,z),when the system is optimized using EHO over an 80 s runtime,are 59.979,22.162,and 0.017,respectively.These values are notably lower than those obtained by the original hippopotamus optimization and manual parameter adjustment.展开更多
The grey wolf optimizer(GWO),a population-based meta-heuristic algorithm,mimics the predatory behavior of grey wolf packs.Continuously exploring and introducing improvement mechanisms is one of the keys to drive the d...The grey wolf optimizer(GWO),a population-based meta-heuristic algorithm,mimics the predatory behavior of grey wolf packs.Continuously exploring and introducing improvement mechanisms is one of the keys to drive the development and application of GWO algorithms.To overcome the premature and stagnation of GWO,the paper proposes a multiple strategy grey wolf optimization algorithm(MSGWO).Firstly,an variable weights strategy is proposed to improve convergence rate by adjusting the weights dynamically.Secondly,this paper proposes a reverse learning strategy,which randomly reverses some individuals to improve the global search ability.Thirdly,the chain predation strategy is designed to allow the search agent to be guided by both the best individual and the previous individual.Finally,this paper proposes a rotation predation strategy,which regards the position of the current best individual as the pivot and rotate other members for enhacing the exploitation ability.To verify the performance of the proposed technique,MSGWO is compared with seven state-of-the-art meta-heuristics and four variant GWO algorithms on CEC2022 benchmark functions and three engineering optimization problems.The results demonstrate that MSGWO has better performance on most of benchmark functions and shows competitive in solving engineering design problems.展开更多
基金supported by Anhui Polytechnic University Introduced Talents Research Fund(No.2021YQQ064)Anhui Polytechnic University ScientificResearch Project(No.Xjky2022168).
文摘Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems.However,with the increased dimensions,higher requirements are put forward for algorithm performance.The double population whale optimization algorithm with distributed collaboration and reverse learning ability(DCRWOA)is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems.In the DCRWOA algorithm,the novel double population search strategy is constructed.Meanwhile,the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area.Numerical experi-ments are carried out using standard test functions with different dimensions(10,50,100,200).The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algo-rithm.The results show that the DCRWOA algorithm has higher optimization accuracy and stability,and the convergence speed is significantly improved.Therefore,the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.
文摘Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software defect prediction can be effectively performed using traditional features,but there are some redundant or irrelevant features in them(the presence or absence of this feature has little effect on the prediction results).These problems can be solved using feature selection.However,existing feature selection methods have shortcomings such as insignificant dimensionality reduction effect and low classification accuracy of the selected optimal feature subset.In order to reduce the impact of these shortcomings,this paper proposes a new feature selection method Cubic TraverseMa Beluga whale optimization algorithm(CTMBWO)based on the improved Beluga whale optimization algorithm(BWO).The goal of this study is to determine how well the CTMBWO can extract the features that are most important for correctly predicting software defects,improve the accuracy of fault prediction,reduce the number of the selected feature and mitigate the risk of overfitting,thereby achieving more efficient resource utilization and better distribution of test workload.The CTMBWO comprises three main stages:preprocessing the dataset,selecting relevant features,and evaluating the classification performance of the model.The novel feature selection method can effectively improve the performance of SDP.This study performs experiments on two software defect datasets(PROMISE,NASA)and shows the method’s classification performance using four detailed evaluation metrics,Accuracy,F1-score,MCC,AUC and Recall.The results indicate that the approach presented in this paper achieves outstanding classification performance on both datasets and has significant improvement over the baseline models.
基金supported by the Guangdong Social Science Fund of China under Grant No.GD20CWY10the National Social Science Fund of China under Grant No.22BTQ045the Science and Technology Program of Guangzhou under Grant No.202002030227.
文摘Chinese spelling check is a task to detect and correct spelling mistakes in Chinese texts.Existing research aims to enhance the text representation and exploit multi-source information to improve the detection and correction capabilities of models,with little attention to improving the ability to distinguish confusable words.Contrastive learning,aiming to minimize the distance in the representation space between similar sample pairs,has recently become a dominant technique in natural language processing.Inspired by contrastive learning,we present a novel method for Chinese spelling checking,RCL-CSC,which consists of three modules:language representation,spelling check,and reverse contrastive learning.Specifically,we propose a reverse contrastive learning method,which explicitly forces the model to minimize the agreement between similar examples,namely,the phonetically and visually confusable characters.Experimental results show that our method is model-agnostic,and thus can be combined with existing Chinese spelling check models to achieve state-of-the-art performance.
基金supported by grants from the National Natural Science Foundation of China (31970955 and 31700912)。
文摘Dear Editor,In dynamic environments,the memory system of the brain must be able to perceive and process conflicting experiences to reach an adaptive decision.In Drosophila,in contrast to consistent experiences,conflicting experiences trigger significantly increased Rac1 activity which mediates active forgetting [1].The ability to cope with conflicting experiences but not simple learning experiences is impaired in mutants of multiple autism-risk genes [2].
基金supported by National Natural Science Foundation of China(No.42302336)Project of the Department of Science and Technology of Sichuan Province(No.2024YFHZ0098,No.2023NSFSC0751)Open Project of Chengdu University of Information Technology(KYQN202317,760115027,KYTZ202278,KYTZ202280).
文摘Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data collection.Path planning of UAV advancing along river valleys in wild environments is one of the first and most difficult problems faced by unmanned surveys of debris flow valleys.This study proposes a new hybrid bat optimization algorithm,GRE-Bat(Good point set,Reverse learning,Elite Pool-Bat algorithm),for unmanned exploration path planning of debris flow sources in outdoor environments.In the GRE-Bat algorithm,the good point set strategy is adopted to evenly distribute the population,ensure sufficient coverage of the search space,and improve the stability of the convergence accuracy of the algorithm.Subsequently,a reverse learning strategy is introduced to increase the diversity of the population and improve the local stagnation problem of the algorithm.In addition,an Elite pool strategy is added to balance the replacement and learning behaviors of particles within the population based on elimination and local perturbation factors.To demonstrate the effectiveness of the GRE-Bat algorithm,we conducted multiple simulation experiments using benchmark test functions and digital terrain models.Compared to commonly used path planning algorithms such as the Bat Algorithm(BA)and the Improved Sparrow Search Algorithm(ISSA),the GRE-Bat algorithm can converge to the optimal value in different types of test functions and obtains a near-optimal solution after an average of 60 iterations.The GRE-Bat algorithm can obtain higher quality flight routes in the designated environment of unmanned investigation in the debris flow gully basin,demonstrating its potential for practical application.
文摘A hybrid strategy is proposed to solve the problems of poor population diversity, insufficient convergence accuracy and susceptibility to local optimal values in the original Arctic Puffin Optimization (APO) algorithm, Enhanced Tangent Flight Adaptive Arctic Puffin Optimization with Elite initialization and Adaptive t-distribution Mutation (ETAAPO). Elite initialization improves initial population quality and accelerates convergence. Tangent Flight of the Tangent search algorithm replaces Levy Flight to balance local search and global exploration. The adaptive t-distribution mutation strategy enhances the optimization ability. ETAAPO was tested on CEC2021 functions, Wilcoxon rank-sum tests, and engineering problems, demonstrating superior optimization performance and faster convergence.
基金Project supported by the National Natural Science Foundation of China(Nos.62341303,62203132,and 52265066)the Guizhou Provincial Science and Technology Projects(No.[ZK[2022]Yiban103])the Guizhou Science and Technology Support Plan(No.2024 General 136)。
文摘Effectively tuning the parameters of proportional-integralderivative(PID)controllers has persistently posed a challenge in control engineering.This study proposes enhanced hippopotamus optimization(EHO)to address this challenge.Latin hypercube sampling and adaptive lens reverse learning are used to initialize the population to improve population diversity and enhance global search.Additionally,an adaptive perturbation mechanism is introduced into the position update in the exploration phase.To validate the performance of EHO,it is benchmarked against hippopotamus optimization and four classical or state-of-the-art intelligent algorithms using the CEC2022 test suite.The effectiveness of EHO is further evaluated by applying it in tuning PID controllers for different types of systems.The performance of EHO is compared with five other algorithms and the classical Ziegler-Nichols method.Analysis of convergence curves,step responses,box plots,and radar charts indicates that EHO outperforms the compared methods in accuracy,convergence speed,and stability.Finally,EHO is used to tune the cascade PID controller for trajectory tracking in a quadrotor unmanned aerial vehicle to assess its applicability.The simulation results indicate that the integrals of the time absolute error for the position channels(c,y,z),when the system is optimized using EHO over an 80 s runtime,are 59.979,22.162,and 0.017,respectively.These values are notably lower than those obtained by the original hippopotamus optimization and manual parameter adjustment.
基金supported by the National Natural Science Foundation of China under Grants Nos.62006103 and 61872168in part by the Postgraduate research and practice innovation program of Jiangsu Province under Grand No.KYCX24_3057+3 种基金in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Normal University under Grand Nos.2024XKT2643 and 2024XKT2642in part by Xuzhou Basic Research Program under Grand No.KC23025in part by the Royal Society International Exchanges Scheme IECVNSFCV211404in part by China Scholarship Council under Grand No.202310090064.
文摘The grey wolf optimizer(GWO),a population-based meta-heuristic algorithm,mimics the predatory behavior of grey wolf packs.Continuously exploring and introducing improvement mechanisms is one of the keys to drive the development and application of GWO algorithms.To overcome the premature and stagnation of GWO,the paper proposes a multiple strategy grey wolf optimization algorithm(MSGWO).Firstly,an variable weights strategy is proposed to improve convergence rate by adjusting the weights dynamically.Secondly,this paper proposes a reverse learning strategy,which randomly reverses some individuals to improve the global search ability.Thirdly,the chain predation strategy is designed to allow the search agent to be guided by both the best individual and the previous individual.Finally,this paper proposes a rotation predation strategy,which regards the position of the current best individual as the pivot and rotate other members for enhacing the exploitation ability.To verify the performance of the proposed technique,MSGWO is compared with seven state-of-the-art meta-heuristics and four variant GWO algorithms on CEC2022 benchmark functions and three engineering optimization problems.The results demonstrate that MSGWO has better performance on most of benchmark functions and shows competitive in solving engineering design problems.
