An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from its...An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from itself and the best one but also from the mean value of some other particles.In addition,adaptive mutation was introduced to reduce the rate of premature convergence.The experimental results on the synthetic aperture radar（SAR） target recognition of moving and stationary target acquisition and recognition（MSTAR） dataset and character recognition of MNIST database show that the improved algorithm is feasible and effective for fuzzy multi-class SVM training.展开更多
A novel multi-chip module(MCM) interconnect test generation scheme based on ant algorithm(AA) with mutation operator was presented.By combing the characteristics of MCM interconnect test generation,the pheromone updat...A novel multi-chip module(MCM) interconnect test generation scheme based on ant algorithm(AA) with mutation operator was presented.By combing the characteristics of MCM interconnect test generation,the pheromone updating rule and state transition rule of AA is designed.Using mutation operator,this scheme overcomes ordinary AA’s defects of slow convergence speed,easy to get stagnate,and low ability of full search.The international standard MCM benchmark circuit provided by the MCNC group was used to verify the approach.The results of simulation experiments,which compare to the results of standard ant algorithm,genetic algorithm(GA) and other deterministic interconnecting algorithms,show that the proposed scheme can achieve high fault coverage,compact test set and short CPU time,that it is a newer optimized method deserving research.展开更多
土地生态安全是土地资源持续利用的核心,由人类活动造成的土地利用变化改变生态系统结构与功能,对区域生态安全系统产生严重影响。为探究近年来重庆市及2030年生态安全变化情况,以重庆市为研究对象,采用PLUS(patch-level land use simul...土地生态安全是土地资源持续利用的核心,由人类活动造成的土地利用变化改变生态系统结构与功能,对区域生态安全系统产生严重影响。为探究近年来重庆市及2030年生态安全变化情况,以重庆市为研究对象,采用PLUS(patch-level land use simulation)模型模拟2030年自然发展、生态优先、发展优先情景下土地利用变化。基于生态学角度构建生态安全评价指标体系,并结合突变模型定量评价土地生态安全水平。结果表明重庆市土地利用类型空间分布差异较大,耕地面积减少3 995.14 km^(2),建设用地面积增加1 147.36 km^(2),实现城市快速发展;2000—2020年重庆市生态安全处于一般安全等级以上面积占比呈增加-降低-增加-减少趋势,总体呈上升趋势。同时三种情景下处于在相对安全及以上占64.53%、67.31%、55.97%;重庆市生态安全空间格局与人口密度、GDP等空间格局相反,与植被覆盖、坡度等自然数据空间格局相符。通过对往年及不同情景下的土地利用变化情况进行生态评定,为生态及经济高质量协同发展提供依据。展开更多
多目标萤火虫算法采用整体维度更新策略,常因某几维变量上优化效果不佳,导致算法收敛速度慢和寻优精度低。针对上述问题,本文提出基于决策变量分组优化的多目标萤火虫算法(multi-objective firefly algorithm with group optimization o...多目标萤火虫算法采用整体维度更新策略,常因某几维变量上优化效果不佳,导致算法收敛速度慢和寻优精度低。针对上述问题,本文提出基于决策变量分组优化的多目标萤火虫算法(multi-objective firefly algorithm with group optimization of decision variables,MOFA-GD)。引入决策变量分组机制,根据各变量对算法性能的不同影响,将整体决策变量划分成收敛性变量组和多样性变量组;设计决策变量分组优化模型,利用学习行为优化收敛性变量组,加快种群收敛速度,非均匀变异算子优化多样性变量组,避免种群过早收敛,逐渐减小的变异幅度引导种群局部开发,提升算法寻优精度;采用档案截断策略维护外部档案,精准删除拥挤个体,从而保持外部档案的多样性。实验结果表明:MOFA-GD表现出优秀的收敛速度和寻优精度,获得了均匀分布的Pareto解集。本文所提算法为求解多目标优化问题提供了一种高效且可靠的解决方案。展开更多
基金supported by the National Natural Science Foundation of China (60873086)the Aeronautical Science Foundation of China(20085153013)the Fundamental Research Found of Northwestern Polytechnical Unirersity (JC200942)
文摘An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from itself and the best one but also from the mean value of some other particles.In addition,adaptive mutation was introduced to reduce the rate of premature convergence.The experimental results on the synthetic aperture radar（SAR） target recognition of moving and stationary target acquisition and recognition（MSTAR） dataset and character recognition of MNIST database show that the improved algorithm is feasible and effective for fuzzy multi-class SVM training.
文摘A novel multi-chip module(MCM) interconnect test generation scheme based on ant algorithm(AA) with mutation operator was presented.By combing the characteristics of MCM interconnect test generation,the pheromone updating rule and state transition rule of AA is designed.Using mutation operator,this scheme overcomes ordinary AA’s defects of slow convergence speed,easy to get stagnate,and low ability of full search.The international standard MCM benchmark circuit provided by the MCNC group was used to verify the approach.The results of simulation experiments,which compare to the results of standard ant algorithm,genetic algorithm(GA) and other deterministic interconnecting algorithms,show that the proposed scheme can achieve high fault coverage,compact test set and short CPU time,that it is a newer optimized method deserving research.
文摘土地生态安全是土地资源持续利用的核心,由人类活动造成的土地利用变化改变生态系统结构与功能,对区域生态安全系统产生严重影响。为探究近年来重庆市及2030年生态安全变化情况,以重庆市为研究对象,采用PLUS(patch-level land use simulation)模型模拟2030年自然发展、生态优先、发展优先情景下土地利用变化。基于生态学角度构建生态安全评价指标体系,并结合突变模型定量评价土地生态安全水平。结果表明重庆市土地利用类型空间分布差异较大,耕地面积减少3 995.14 km^(2),建设用地面积增加1 147.36 km^(2),实现城市快速发展;2000—2020年重庆市生态安全处于一般安全等级以上面积占比呈增加-降低-增加-减少趋势,总体呈上升趋势。同时三种情景下处于在相对安全及以上占64.53%、67.31%、55.97%;重庆市生态安全空间格局与人口密度、GDP等空间格局相反,与植被覆盖、坡度等自然数据空间格局相符。通过对往年及不同情景下的土地利用变化情况进行生态评定,为生态及经济高质量协同发展提供依据。
文摘多目标萤火虫算法采用整体维度更新策略,常因某几维变量上优化效果不佳,导致算法收敛速度慢和寻优精度低。针对上述问题,本文提出基于决策变量分组优化的多目标萤火虫算法(multi-objective firefly algorithm with group optimization of decision variables,MOFA-GD)。引入决策变量分组机制,根据各变量对算法性能的不同影响,将整体决策变量划分成收敛性变量组和多样性变量组;设计决策变量分组优化模型,利用学习行为优化收敛性变量组,加快种群收敛速度,非均匀变异算子优化多样性变量组,避免种群过早收敛,逐渐减小的变异幅度引导种群局部开发,提升算法寻优精度;采用档案截断策略维护外部档案,精准删除拥挤个体,从而保持外部档案的多样性。实验结果表明:MOFA-GD表现出优秀的收敛速度和寻优精度,获得了均匀分布的Pareto解集。本文所提算法为求解多目标优化问题提供了一种高效且可靠的解决方案。
