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
We developed a habitat suitability model for predicting nest locations of breeding Northern Goshawks (Accipiter gentilis) in the high-elevation mixed forest and shrub-steppe habitat of south-central Idaho, USA. We use...We developed a habitat suitability model for predicting nest locations of breeding Northern Goshawks (Accipiter gentilis) in the high-elevation mixed forest and shrub-steppe habitat of south-central Idaho, USA. We used elevation, slope, aspect, ruggedness, distance-to-water, canopy cover, and individual bands of Landsat imagery as predictors for known nest locations with logistic regression. We found goshawks prefer to nest in gently-sloping, east-facing, non-rugged areas of dense aspen and lodgepole pine forests with low reflectance in green (0.53 - 0.61 μm) wavelengths during the breeding season. We used the model results to classify our 43,169 hectare study area into nesting suitability categories: well suited (8.8%), marginally suited (5.1%), and poorly suited (86.1%). We evaluated our model’s performance by comparing the modeled results to a set of GPS locations of known nests (n = 15) that were not used to develop the model. Observed nest locations matched model results 93.3% of the time for well suited habitat and fell within poorly suited areas only 6.7% of the time. Our method improves on goshawk nesting models developed previously by others and may be applicable for surveying goshawks in adjacent mountain ranges across the northern Great Basin.展开更多
建立了北方苍鹰算法优化长短期记忆神经网络(northern goshawk optimization-long short term memory,NGO-LSTM)的预测模型。以深圳市共享单车为例,首先对共享单车数据进行预处理,以Geohash算法为基础将骑行的时变数据作为特征输入;然...建立了北方苍鹰算法优化长短期记忆神经网络(northern goshawk optimization-long short term memory,NGO-LSTM)的预测模型。以深圳市共享单车为例,首先对共享单车数据进行预处理,以Geohash算法为基础将骑行的时变数据作为特征输入;然后采用Canopy算法结合K-means聚类算法将深圳市地铁站进行聚类分析,以此发掘不同类型站点骑行规律;最后在此基础上建立了NGO-LSTM预测模型对站点的需求量进行预测分析,并与其他模型进行对比。实验结果表明,NGO-LSTM模型的决定系数达到0.90。展开更多
针对Leach(low energy adaptive clustering hierarchy)协议在大规模网络中存在着数据传输效率不高和网络生命周期短的问题,提出了一种LEACH-CM-NGO优化算法。该方法通过在簇头选取阶段优化簇头数在所有节点中占比,引进能量密度因子和...针对Leach(low energy adaptive clustering hierarchy)协议在大规模网络中存在着数据传输效率不高和网络生命周期短的问题,提出了一种LEACH-CM-NGO优化算法。该方法通过在簇头选取阶段优化簇头数在所有节点中占比,引进能量密度因子和能耗因子改进阈值公式优化簇头分布,并在数据传输阶段,由原本的单跳传输改为多跳方式传输数据,引入基于立方映射方法,自适应权重策略和柯西变异的北方苍鹰优化算法改进簇头间数据传输路径,以提高网络的能效和数据传输效率。仿真结果表明,所提出的方法在减少能耗的同时,显著延长了网络的生命周期并提高了数据传输的成功率。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.
文摘We developed a habitat suitability model for predicting nest locations of breeding Northern Goshawks (Accipiter gentilis) in the high-elevation mixed forest and shrub-steppe habitat of south-central Idaho, USA. We used elevation, slope, aspect, ruggedness, distance-to-water, canopy cover, and individual bands of Landsat imagery as predictors for known nest locations with logistic regression. We found goshawks prefer to nest in gently-sloping, east-facing, non-rugged areas of dense aspen and lodgepole pine forests with low reflectance in green (0.53 - 0.61 μm) wavelengths during the breeding season. We used the model results to classify our 43,169 hectare study area into nesting suitability categories: well suited (8.8%), marginally suited (5.1%), and poorly suited (86.1%). We evaluated our model’s performance by comparing the modeled results to a set of GPS locations of known nests (n = 15) that were not used to develop the model. Observed nest locations matched model results 93.3% of the time for well suited habitat and fell within poorly suited areas only 6.7% of the time. Our method improves on goshawk nesting models developed previously by others and may be applicable for surveying goshawks in adjacent mountain ranges across the northern Great Basin.
文摘针对传统的神经网络模型因超参数众多,在实验中比对最优参数组合效率低下导致误差较大和反应速度慢的问题。本文提出一种基于北方苍鹰优化(Northern Goshawk Optimization,NGO)算法和双向门控循环单元神经网络(Bidirectional Gated Recurrent Unit, Bi-GRU)的船舶轨迹预测模型NGO-Bi-GRU(Northern Goshawk Optimization Bidirectional Gated Recurrent Unit)。利用NGO对Bi-GRU模型的学习率、隐藏节点和正则化系数进行寻优,然后将寻优得到的网络超参数代入Bi-GRU进行船舶轨迹预测。将该模型与长短时记忆神经网络(Long Short Term Memory, LSTM)和门控循环单元神经网络模型(Gated Recurrent Unit, GRU)以及使用该算法优化的长短期神经网络模型进行实验对比,将均方误差、均方根误差、平均绝对误差作为评价标准。结果表明,NGO-Bi-GRU模型在经度和纬度预测上误差较小、精确度较高且数值波动更加稳定。
文摘建立了北方苍鹰算法优化长短期记忆神经网络(northern goshawk optimization-long short term memory,NGO-LSTM)的预测模型。以深圳市共享单车为例,首先对共享单车数据进行预处理,以Geohash算法为基础将骑行的时变数据作为特征输入;然后采用Canopy算法结合K-means聚类算法将深圳市地铁站进行聚类分析,以此发掘不同类型站点骑行规律;最后在此基础上建立了NGO-LSTM预测模型对站点的需求量进行预测分析,并与其他模型进行对比。实验结果表明,NGO-LSTM模型的决定系数达到0.90。
文摘针对Leach(low energy adaptive clustering hierarchy)协议在大规模网络中存在着数据传输效率不高和网络生命周期短的问题,提出了一种LEACH-CM-NGO优化算法。该方法通过在簇头选取阶段优化簇头数在所有节点中占比,引进能量密度因子和能耗因子改进阈值公式优化簇头分布,并在数据传输阶段,由原本的单跳传输改为多跳方式传输数据,引入基于立方映射方法,自适应权重策略和柯西变异的北方苍鹰优化算法改进簇头间数据传输路径,以提高网络的能效和数据传输效率。仿真结果表明,所提出的方法在减少能耗的同时,显著延长了网络的生命周期并提高了数据传输的成功率。
