ABP法在高密度电阻率法反演中的应用   总被引：3，自引：1，他引：2       下载免费PDF全文

张凌云  刘鸿福 《地球物理学报》2011,54(1):227-233

非线性反演方法作为地球物理反演的一个重要分支,在地球物理反演中发挥着特有的作用.近年来学者对非线性联合反演研究较多,但目前仍未有实质性的研究进展;本文尝试利用BP(Back Propagation)神经网络优化方法与蚁群算法联合演算,实现高密度电阻率法的电阻率二维非线性反演.通过两组模型的结果比较,BP与ABP 法的反...  相似文献   

基于MIMIC算法和RPCA的混合蚁群优化算法     

官娟  刘国华  刘天祺  秦健  张淼森 《南京气象学院学报》2020,12(5):569-576

为提高连续域上蚁群算法的寻优性能,降低决策变量之间的相关性,设计一种基于MIMIC算法和RPCA的连续域上蚁群优化算法.本文首先介绍连续域上的蚁群算法;然后根据一些处理多变量相关性的方法,给出有效相关性的定义;接着提出一种基于MIMIC算法和RPCA的混合蚁群算法;最后,通过对标准测试函数进行优化求解实验,将所得结果与连续域上的蚁群优化算法相比较,可知该算法在寻优能力和收敛性方面都有明显的提高,是一种有效的优化算法.  相似文献   

华北平原城镇地名群的发展及其地理分布特征   总被引：3，自引：1，他引：2  

孙冬虎 《地理研究》1990,9(3):49-56

本文分析了华北平原城镇地名群形成和演变的历史过程,以及经济、政治、军事、文化等因素在这一过程中的作用,探讨了区域城镇地名群的地理分布特征及其形成的历史地理背景,并预测了该地名群的发展趋势。  相似文献   

基于蚁群算法的点状注记智能化配置   总被引：1，自引：0，他引：1  

彭珊鸰  宋鹰  吴凡 《测绘科学》2007,32(5):80-81

地图注记是为待定位注记求取注记位置的过程,它是自动地图制图中的一个重要的难题,其中点状注记问题可以看作是一个组合优化问题。目前,模拟退火法,遗传算法和神经网络优化算法已经被用来优化点状注记的配置问题。本文将引入蚁群算法在点状注记配置中的应用,经实验证明该方法有更高的搜索效率,得到的注记结果基本上解决了冲突问题。  相似文献   

蚁群算法在GIS中的应用探讨     

欧春江 《测绘与空间地理信息》2013,(8):187-188,193

蚁群算法是近几年来迅速发展起来的、并得到广泛应用的一种新型模拟进化优化算法。最早成功应用于解决TSP问题。研究表明该算法具有并行性、鲁棒性等优良性质。在GIS中,蚁群算法应用在路径寻优和正射影像镶嵌线自动选择两个问题效果较好。  相似文献   

氮磷营养变化对钦州湾球形棕囊藻藻体生长的影响     

莫钰  蓝彩碧  许铭本  赖俊翔  凌慧娇 《海洋科学》2022,46(5):30-41

球形棕囊藻(Phaeocystis globosa)是我国北部湾海域主要的有害藻华原因种, 囊体的生长是藻华发生和持续的关键。为了研究在富营养化日趋严重的钦州湾海域中营养盐输入对球形棕囊藻藻华形成的影响, 采集钦州湾含球形棕囊藻的表层海水进行了添加不同营养盐的室内培养实验。根据钦州湾历史调查数据, 进行了不同营养盐、不同氮磷比和不同添加方式的培养。结果表明, 同时添加氮和磷显著促进浮游植物的生长, 球形棕囊藻囊体继浮游植物细胞密度高峰期后大量形成。一次性添加磷对囊体生长的刺激作用较添加氮时强, 丰度最高可达4.8×10³ colonies·L^–1, 囊体平均直径为(115±84) μm, 且具有较高囊体细胞密度, 但囊体衰退较快。单独添加氮时, 囊体细胞分布稀疏, 囊体数量及直径皆较低。每天添加磷的方式相比一次性添加更有利于囊体丰度的维持。总体上, 磷营养的添加能刺激囊体数量、囊体细胞数和囊体直径的生长。在具有较高N/P比值的钦州湾, 应加强磷的排放管理, 避免突发性磷污染对球形棕囊藻囊体生长的刺激作用。  相似文献   

混沌蜂群优化的NSST域多光谱与全色图像融合     

吴一全  王志来 《遥感学报》2017,21(4):549-557

为有效融合多光谱图像的光谱信息和全色图像的空间细节信息,提出了一种基于混沌蜂群优化和改进脉冲耦合神经网络(PCNN)的非下采样Shearlet变换(NSST)域图像融合方法。首先对多光谱图像进行Intensity-HueSaturation(IHS)变换,全色图像的直方图按照多光谱图像亮度分量的直方图进行匹配;然后分别对多光谱图像的亮度分量和新全色图像进行NSST变换,对低频分量使用改进加权融合算法进行融合,以互信息作为适应度函数,利用混沌蜂群算法找到最优加权系数。对高频分量采用改进脉冲耦合神经网络(PCNN)方法进行融合,再经NSST逆变换和IHS逆变换得到融合图像。本文方法在主观视觉效果和信息熵、光谱扭曲度等客观定量评价指标上优于基于IHS变换、基于非下采样Contourlet变换(NSCT)和非负矩阵分解(NMF)、基于NSCT和PCNN等5种融合方法。本文方法在提升图像空间分辨率的同时,有效地保留了光谱信息。  相似文献   

Ant colony optimization of tuned mass dampers for earthquake oscillations of high-rise structures including soil–structure interaction     

《Soil Dynamics and Earthquake Engineering》2013

This paper investigates the optimized parameters for tuned mass dampers (TMDs) to decrease the earthquake vibrations of tall buildings; involving soil–structure interaction (SSI) effects. The time domain analysis based on Newmark method is employed in this study. To illustrate the results, Tabas and Kobe earthquakes data are applied to the model, and ant colony optimization (ACO) method is utilized to obtain the best parameters for TMD. The TMD mass, damping coefficient and spring stiffness are assumed as design variables, and the objective is to reduce both the maximum displacement and acceleration of stories. It is shown that how the ACO can be effectively applied to design the optimum TMD device. It is also indicated that the soil type greatly affects the TMD optimized parameters and the time response of structures. This study helps the researchers to better understanding of earthquake vibrations, and leads the designers to achieve the optimized TMD for high-rise buildings.  相似文献   

A multi-type ant colony optimization (MACO) method for optimal land use allocation in large areas     

Xiaoping Liu  Xia Li  Xun Shi  Kangning Huang  Yilun Liu 《International journal of geographical information science》2013,27(7):1325-1343

Optimizing land use allocation is a challenging task, as it involves multiple stakeholders with conflicting objectives. In addition, the solution space of the optimization grows exponentially as the size of the region and the resolution increase. This article presents a new ant colony optimization algorithm by incorporating multiple types of ants for solving complex multiple land use allocation problems. A spatial exchange mechanism is used to deal with competition between different types of land use allocation. This multi-type ant colony optimization optimal multiple land allocation (MACO-MLA) model was successfully applied to a case study in Panyu, Guangdong, China, a large region with an area of 1,454,285 cells. The proposed model took only about 25 minutes to find near-optimal solution in terms of overall suitability, compactness, and cost. Comparison indicates that MACO-MLA can yield better performances than the simulated annealing (SA) and the genetic algorithm (GA) methods. It is found that MACO-MLA has an improvement of the total utility value over SA and GA methods by 4.5% and 1.3%, respectively. The computation time of this proposed model amounts to only 2.6% and 12.3%, respectively, of that of the SA and GA methods. The experiments have demonstrated that the proposed model was an efficient and effective optimization technique for generating optimal land use patterns.  相似文献   

An improved artificial bee colony algorithm for optimal land-use allocation     

Lina Yang  Xu Sun  Jing Shao  Tianhe Chi 《International journal of geographical information science》2013,27(8):1470-1489

Land-use allocation is of great importance for rapid urban planning and natural resource management. This article presents an improved artificial bee colony (ABC) algorithm to solve the spatial optimization problem. The new approach consists of a heuristic information-based pseudorandom initialization (HIPI) method for initial solutions and pseudorandom search strategy based on a long-chain (LC) mechanism for neighborhood searches; together, these methods substantially improve the search efficiency and quality when handling spatial data in large areas. We evaluated the approach via a series of land-use allocation experiments and compared it with particle swarm optimization (PSO) and genetic algorithm (GA) methods. The experimental results show that the new approach outperforms the current methods in both computing efficiency and optimization quality.  相似文献