It is evident that complex optimization problems are becoming increasingly prominent,metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional,nonlinear problems.However,the traditional ...It is evident that complex optimization problems are becoming increasingly prominent,metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional,nonlinear problems.However,the traditional Sparrow Search Algorithm(SSA)suffers from limited global search capability,insufficient population diversity,and slow convergence,which often leads to premature stagnation in local optima.Despite the proposal of various enhanced versions,the effective balancing of exploration and exploitation remains an unsolved challenge.To address the previously mentioned problems,this study proposes a multi-strategy collaborative improved SSA,which systematically integrates four complementary strategies:(1)the Northern Goshawk Optimization(NGO)mechanism enhances global exploration through guided prey-attacking dynamics;(2)an adaptive t-distribution mutation strategy balances the transition between exploration and exploitation via dynamic adjustment of the degrees of freedom;(3)a dual chaotic initialization method(Bernoulli and Sinusoidal maps)increases population diversity and distribution uniformity;and(4)an elite retention strategy maintains solution quality and prevents degradation during iterations.These strategies cooperate synergistically,forming a tightly coupled optimization framework that significantly improves search efficiency and robustness.Therefore,this paper names it NTSSA:A Novel Multi-Strategy Enhanced Sparrow Search Algorithm with Northern Goshawk Optimization and Adaptive t-Distribution for Global Optimization.Extensive experiments on the CEC2005 benchmark set demonstrate that NTSSA achieves theoretical optimal accuracy on unimodal functions and significantly enhances global optimum discovery for multimodal functions by 2–5 orders of magnitude.Compared with SSA,GWO,ISSA,and CSSOA,NTSSA improves solution accuracy by up to 14.3%(F8)and 99.8%(F12),while accelerating convergence by approximately 1.5–2×.The Wilcoxon rank-sum test(p<0.05)indicates that NTSSA demonstrates a statistically substantial performance advantage.Theoretical analysis demonstrates that the collaborative synergy among adaptive mutation,chaos-based diversification,and elite preservation ensures both high convergence accuracy and global stability.This work bridges a key research gap in SSA by realizing a coordinated optimization mechanism between exploration and exploitation,offering a robust and efficient solution framework for complex high-dimensional problems in intelligent computation and engineering design.展开更多
Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting incr...Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting increased interest in swarm intelligence algorithms.Among these,the Cuckoo Search(CS)algorithm stands out for its promising global search capabilities.However,it often suffers from premature convergence when tackling complex problems.To address this limitation,this paper proposes a Grouped Dynamic Adaptive CS(GDACS)algorithm.Theenhancements incorporated intoGDACS can be summarized into two key aspects.Firstly,a chaotic map is employed to generate initial solutions,leveraging the inherent randomness of chaotic sequences to ensure a more uniform distribution across the search space and enhance population diversity from the outset.Secondly,Cauchy and Levy strategies replace the standard CS population update.This strategy involves evaluating the fitness of candidate solutions to dynamically group the population based on performance.Different step-size adaptation strategies are then applied to distinct groups,enabling an adaptive search mechanism that balances exploration and exploitation.Experiments were conducted on six benchmark functions and four constrained engineering design problems,and the results indicate that the proposed GDACS achieves good search efficiency and produces more accurate optimization results compared with other state-of-the-art algorithms.展开更多
A search strategy based on the maximal information gain principle is presented for the cued search of phased array radars. First, the method for the determination of the cued search region, arrangement of beam positio...A search strategy based on the maximal information gain principle is presented for the cued search of phased array radars. First, the method for the determination of the cued search region, arrangement of beam positions, and the calculation of the prior probability distribution of each beam position is discussed. And then, two search algorithms based on information gain are proposed using Shannon entropy and Kullback-Leibler entropy, respectively. With the proposed strategy, the information gain of each beam position is predicted before the radar detection, and the observation is made in the beam position with the maximal information gain. Compared with the conventional method of sequential search and confirm search, simulation results show that the proposed search strategy can distinctly improve the search performance and save radar time resources with the same given detection probability.展开更多
Feature selection has been widely used in data mining and machine learning.Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly.In...Feature selection has been widely used in data mining and machine learning.Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly.In this article,a feature selection algorithm with local search strategy based on the forest optimization algorithm,namely FSLSFOA,is proposed.The novel local search strategy in local seeding process guarantees the quality of the feature subset in the forest.Next,the fitness function is improved,which not only considers the classification accuracy,but also considers the size of the feature subset.To avoid falling into local optimum,a novel global seeding method is attempted,which selects trees on the bottom of candidate set and gives the algorithm more diversities.Finally,FSLSFOA is compared with four feature selection methods to verify its effectiveness.Most of the results are superior to these comparative methods.展开更多
This paper proposes an enhanced arithmetic optimization algorithm(AOA)called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA.Furthermore,an adju...This paper proposes an enhanced arithmetic optimization algorithm(AOA)called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA.Furthermore,an adjustable parameter is also developed to balance the exploration and exploitation operations.In addition,a jump mechanism is included in the PSAOAto assist individuals in jumping out of local optima.Using 29 classical benchmark functions,the proposed PSAOA is extensively tested.Compared to the AOA and other well-known methods,the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.展开更多
Traditional source search algorithms are prone to local optimization,and source search methods combining crowdsourcing and human-AI collaboration suffer from low cost-efficiency due to human intervention.In this study...Traditional source search algorithms are prone to local optimization,and source search methods combining crowdsourcing and human-AI collaboration suffer from low cost-efficiency due to human intervention.In this study,we proposed a lightweight human-AI collaboration framework that utilized multi-modal large language models(MLLMs)to achieve visual-language conversion,combined chain-of-thought(CoT)reasoning to optimize decision-making,and constructed a heuristic strategy that incorporated probability distribution filtering and a balance between exploitation and exploration.The effectiveness of the framework was verified by experiments.The human-AI alignment heuristic strategy with large language model adaptation design provides a new idea to reduce manual dependency for source search task in complex scenes.展开更多
To provide an energy-efficient and slab-demand-compliant rolling delay strategy,the simulation software is utilized to calculate the rolling delay process of the reheating furnace.Based on energy consumption evaluatio...To provide an energy-efficient and slab-demand-compliant rolling delay strategy,the simulation software is utilized to calculate the rolling delay process of the reheating furnace.Based on energy consumption evaluation,two optimization methods were employed.The bisection approach uses the needs of the slab to estimate the rolling delay temperature,and the golden section search method uses the energy consumption analysis of the slab to determine the high-temperature insulation duration.Generally,the slab closest to the discharge position in the control zone is selected as the optimization target.The optimized slab does not show a significant temperature rise after the end of the rolling delay process.When comparing the optimized rolling delay strategies with the traditional ones,the optimized rolling delay strategies not only meet the output requirements for slabs but also offer significant advantages in terms of energy efficiency,and this advantage increases with rolling delay time.展开更多
Influenza,an acute respiratory infectious disease caused by the influenza virus,exhibits distinct seasonal patterns in China,with peak activity occurring in winter and spring in northern regions,and in winter and summ...Influenza,an acute respiratory infectious disease caused by the influenza virus,exhibits distinct seasonal patterns in China,with peak activity occurring in winter and spring in northern regions,and in winter and summer in southern areas[1].The World Health Organization(WHO)emphasizes that early warning and epidemic intensity assessments are critical public health strategies for influenza prevention and control.Internet-based flu surveillance,with real-time data and low costs,effectively complements traditional methods.The Baidu Search Index,which reflects flu-related queries,strongly correlates with influenza trends,aiding in regional activity assessment and outbreak tracking[2].展开更多
针对传统地磁匹配中动态时间规整算法(Dynamic Time Warping,DTW)应用于地磁定位时存在误差较大、匹配速度较慢的问题,提出一种基于改进的DTW算法和边界搜索策略的地磁匹配定位方法。首先将传统地磁数据序列变换为一阶导数序列,其更能...针对传统地磁匹配中动态时间规整算法(Dynamic Time Warping,DTW)应用于地磁定位时存在误差较大、匹配速度较慢的问题,提出一种基于改进的DTW算法和边界搜索策略的地磁匹配定位方法。首先将传统地磁数据序列变换为一阶导数序列,其更能反应地磁场的变化趋势,以提高匹配的准确性。引入有限窗口对DTW算法计算单元数进行限制,以加快匹配速度。同时,采用边界搜索策略进行局部筛选,约束相邻两次定位点间的距离,避免出现不合理匹配点的现象。实验结果表明,所提算法相比传统DTW算法在室内环境下定位精度最高可提升87%,处理速度提升15%;室外环境下定位精度可提升17%,处理速度提升52%。研究成果满足地磁定位的实时性、高精度的需求,具有很强的实用性和广阔的发展空间。展开更多
文摘It is evident that complex optimization problems are becoming increasingly prominent,metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional,nonlinear problems.However,the traditional Sparrow Search Algorithm(SSA)suffers from limited global search capability,insufficient population diversity,and slow convergence,which often leads to premature stagnation in local optima.Despite the proposal of various enhanced versions,the effective balancing of exploration and exploitation remains an unsolved challenge.To address the previously mentioned problems,this study proposes a multi-strategy collaborative improved SSA,which systematically integrates four complementary strategies:(1)the Northern Goshawk Optimization(NGO)mechanism enhances global exploration through guided prey-attacking dynamics;(2)an adaptive t-distribution mutation strategy balances the transition between exploration and exploitation via dynamic adjustment of the degrees of freedom;(3)a dual chaotic initialization method(Bernoulli and Sinusoidal maps)increases population diversity and distribution uniformity;and(4)an elite retention strategy maintains solution quality and prevents degradation during iterations.These strategies cooperate synergistically,forming a tightly coupled optimization framework that significantly improves search efficiency and robustness.Therefore,this paper names it NTSSA:A Novel Multi-Strategy Enhanced Sparrow Search Algorithm with Northern Goshawk Optimization and Adaptive t-Distribution for Global Optimization.Extensive experiments on the CEC2005 benchmark set demonstrate that NTSSA achieves theoretical optimal accuracy on unimodal functions and significantly enhances global optimum discovery for multimodal functions by 2–5 orders of magnitude.Compared with SSA,GWO,ISSA,and CSSOA,NTSSA improves solution accuracy by up to 14.3%(F8)and 99.8%(F12),while accelerating convergence by approximately 1.5–2×.The Wilcoxon rank-sum test(p<0.05)indicates that NTSSA demonstrates a statistically substantial performance advantage.Theoretical analysis demonstrates that the collaborative synergy among adaptive mutation,chaos-based diversification,and elite preservation ensures both high convergence accuracy and global stability.This work bridges a key research gap in SSA by realizing a coordinated optimization mechanism between exploration and exploitation,offering a robust and efficient solution framework for complex high-dimensional problems in intelligent computation and engineering design.
基金supported in part by the Ministry of Higher Education Malaysia(MOHE)through Fundamental Research Grant Scheme(FRGS)Ref:FRGS/1/2024/ICT02/UTM/02/10,Vot.No:R.J130000.7828.5F748the Scientific Research Project of Education Department of Hunan Province(Nos.22B1046 and 24A0771).
文摘Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting increased interest in swarm intelligence algorithms.Among these,the Cuckoo Search(CS)algorithm stands out for its promising global search capabilities.However,it often suffers from premature convergence when tackling complex problems.To address this limitation,this paper proposes a Grouped Dynamic Adaptive CS(GDACS)algorithm.Theenhancements incorporated intoGDACS can be summarized into two key aspects.Firstly,a chaotic map is employed to generate initial solutions,leveraging the inherent randomness of chaotic sequences to ensure a more uniform distribution across the search space and enhance population diversity from the outset.Secondly,Cauchy and Levy strategies replace the standard CS population update.This strategy involves evaluating the fitness of candidate solutions to dynamically group the population based on performance.Different step-size adaptation strategies are then applied to distinct groups,enabling an adaptive search mechanism that balances exploration and exploitation.Experiments were conducted on six benchmark functions and four constrained engineering design problems,and the results indicate that the proposed GDACS achieves good search efficiency and produces more accurate optimization results compared with other state-of-the-art algorithms.
基金the High Technology Research and Development Programme of China (2003AA134030)
文摘A search strategy based on the maximal information gain principle is presented for the cued search of phased array radars. First, the method for the determination of the cued search region, arrangement of beam positions, and the calculation of the prior probability distribution of each beam position is discussed. And then, two search algorithms based on information gain are proposed using Shannon entropy and Kullback-Leibler entropy, respectively. With the proposed strategy, the information gain of each beam position is predicted before the radar detection, and the observation is made in the beam position with the maximal information gain. Compared with the conventional method of sequential search and confirm search, simulation results show that the proposed search strategy can distinctly improve the search performance and save radar time resources with the same given detection probability.
基金National Science Foundation of China(Nos.U1736105,61572259,41942017)The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group no.RGP-VPP-264.
文摘Feature selection has been widely used in data mining and machine learning.Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly.In this article,a feature selection algorithm with local search strategy based on the forest optimization algorithm,namely FSLSFOA,is proposed.The novel local search strategy in local seeding process guarantees the quality of the feature subset in the forest.Next,the fitness function is improved,which not only considers the classification accuracy,but also considers the size of the feature subset.To avoid falling into local optimum,a novel global seeding method is attempted,which selects trees on the bottom of candidate set and gives the algorithm more diversities.Finally,FSLSFOA is compared with four feature selection methods to verify its effectiveness.Most of the results are superior to these comparative methods.
基金partially supported by the Fundamental Research Funds for the Central Universities(WUT:2022IVA067)the National Natural Science Foundation of China(Grant No.:72172112)the Fundamental Research Funds for the Central Universities(HUST:2019kfyRCPY038).
文摘This paper proposes an enhanced arithmetic optimization algorithm(AOA)called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA.Furthermore,an adjustable parameter is also developed to balance the exploration and exploitation operations.In addition,a jump mechanism is included in the PSAOAto assist individuals in jumping out of local optima.Using 29 classical benchmark functions,the proposed PSAOA is extensively tested.Compared to the AOA and other well-known methods,the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.
基金National Natural Science Foundation of China (62202477)。
文摘Traditional source search algorithms are prone to local optimization,and source search methods combining crowdsourcing and human-AI collaboration suffer from low cost-efficiency due to human intervention.In this study,we proposed a lightweight human-AI collaboration framework that utilized multi-modal large language models(MLLMs)to achieve visual-language conversion,combined chain-of-thought(CoT)reasoning to optimize decision-making,and constructed a heuristic strategy that incorporated probability distribution filtering and a balance between exploitation and exploration.The effectiveness of the framework was verified by experiments.The human-AI alignment heuristic strategy with large language model adaptation design provides a new idea to reduce manual dependency for source search task in complex scenes.
文摘To provide an energy-efficient and slab-demand-compliant rolling delay strategy,the simulation software is utilized to calculate the rolling delay process of the reheating furnace.Based on energy consumption evaluation,two optimization methods were employed.The bisection approach uses the needs of the slab to estimate the rolling delay temperature,and the golden section search method uses the energy consumption analysis of the slab to determine the high-temperature insulation duration.Generally,the slab closest to the discharge position in the control zone is selected as the optimization target.The optimized slab does not show a significant temperature rise after the end of the rolling delay process.When comparing the optimized rolling delay strategies with the traditional ones,the optimized rolling delay strategies not only meet the output requirements for slabs but also offer significant advantages in terms of energy efficiency,and this advantage increases with rolling delay time.
基金supported by the National Key Research and Development Program of China(Project No.2023YFC2307500).
文摘Influenza,an acute respiratory infectious disease caused by the influenza virus,exhibits distinct seasonal patterns in China,with peak activity occurring in winter and spring in northern regions,and in winter and summer in southern areas[1].The World Health Organization(WHO)emphasizes that early warning and epidemic intensity assessments are critical public health strategies for influenza prevention and control.Internet-based flu surveillance,with real-time data and low costs,effectively complements traditional methods.The Baidu Search Index,which reflects flu-related queries,strongly correlates with influenza trends,aiding in regional activity assessment and outbreak tracking[2].
文摘针对传统地磁匹配中动态时间规整算法(Dynamic Time Warping,DTW)应用于地磁定位时存在误差较大、匹配速度较慢的问题,提出一种基于改进的DTW算法和边界搜索策略的地磁匹配定位方法。首先将传统地磁数据序列变换为一阶导数序列,其更能反应地磁场的变化趋势,以提高匹配的准确性。引入有限窗口对DTW算法计算单元数进行限制,以加快匹配速度。同时,采用边界搜索策略进行局部筛选,约束相邻两次定位点间的距离,避免出现不合理匹配点的现象。实验结果表明,所提算法相比传统DTW算法在室内环境下定位精度最高可提升87%,处理速度提升15%;室外环境下定位精度可提升17%,处理速度提升52%。研究成果满足地磁定位的实时性、高精度的需求,具有很强的实用性和广阔的发展空间。
