基于权重矩阵的经济可持续发展模糊评价方法

研究一个城市的经济可持续发展性有利于调整该城市的产业结构,促进其可持续发展。该文基于模糊模式识别法,利用评价指标体系,采用定量评价方法,研究了典型发展中城市-河南省＂A市＂的经济可持续发展性,对该市2002年和2009年两年的经济可持续发展能力进行了对比分析。结果表明：该市2009年经济的可持续发展性好于2002年,这说明A市的经济可持续发展能力正在不断提高。该识别法语义明确,评价结果符合实际,对国内类似城市的经济可持续发展的评价具有一定的参考价值。     

采用模糊模式识别的经济可持续发展评价方法

研究一个城市的经济可持续发展性有利于调整该城市的产业结构,促进其可持续发展。本文基于模糊模式识别法,利用评价指标体系,采用定量评价方法,研究典型发展中城市-河南省＂A市＂的经济可持续发展性,对该市2005年和2011年两年的经济可持续发展能力进行对比分析。结果表明：该市2011年经济的可持续发展性好于2005年,这说明A市的经济可持续发展能力正在不断提高。该识别法语义明确,评价结果符合实际,对国内类似城市的经济可持续发展的评价具有一定的参考价值。     

关于区域可持续发展评价过程数据处理方法研究

实现可持续发展是21世纪全球面临的一个重要研究课题,可持续发展评价则是实现可持续发展的基础。区域可持续发展评价是一个多因素、非线性的问题,不仅评价过程复杂,而且数据处理和评价方法也很多。     

基于过程神经网络和量子免疫算法的油气评价

为评价我国目前油气资源的可持续发展能力,提出了一种将过程神经元网络与量子免疫算法相结合的评价方法。首先,在构建油气资源可持续发展能力评价指标体系的基础上,采用过程神经元网络建立评价指标体系与评价结果之间的映射关系;然后采用量子免疫算法完成网络的训练;最后,以2004年～2006年的油气资源经济可持续发展评价为例,验证了方法的有效性和可行性。     

旅行商问题的近优解评价方法--浅析启发式算法的有效性

1引言 旅行商问题(Traveling Salesman Problem,TSP)的描述如下:给定N个城市,已知它们之间的距离矩阵,寻求一条经过所有城市一次且仅一次的最短Hamilton回路.TSP已被证明属于NP完全问题,由于启发式算法具有计算复杂性与算法所得近优解的"质量"折中性好的特点,它在TSP算法的研究中占有重要的地位[1],启发式算法的有效性表现在两方面:一是计算复杂性低,二是近优解"质量"高.启发式算法的上界收敛性,即准收敛性的研究是评价启发式算法的一个重要问题.本文以评价近优解为中心,通过总结、归纳已有方法,整理各种典型算法的评价结果,对现有方法的适用范围和评价结果的意义作了分析讨论,其结论对于改善现有算法以及评价新算法有一定的指导作用.启发式算法的准收敛性仍旧是一个正在研究中的问题.     

基于遗传算法的城市公交服务质量评价知识规则挖掘方法

城市公共交通服务质量评价知识规则是城市公共交通企业进行服务质量评价的重要依据,优质、合理的评价知识规则将使服务质量评价更加公正、更加客观。本文在分析城市公共交通服务质量评价指标体系的基础上,将一种改进的遗传算法用于城市公共交通服务质量评价价的知识规则挖掘,提出一种基于遗传算法的城市公共交通服务质量评价知识规则挖掘方法,阐述算法的实现途径。实例表明,该方法在进行知识规则挖掘时是完全可行的、有效的。     

面向SDGs的城市可持续发展评价指标体系进展研究

城市可持续性已成为全球最重要的城市发展议题。评价指标体系是客观诊断可持续发展现状和问题的有效工具,现有城市可持续性评价指标体系大都基于传统统计数据,评价内容各有侧重,且由于普适性及数据的可获得性等问题,很难用于不同城市对比评价研究。随着联合国可持续发展目标SDG11对可持续城市内涵的解读,以及遥感与网络大数据等多源数据的利用,为在统一标准下实现更精准的城市可持续性评价提供了可能。基于此,分析了城市可持续发展内函的演变,明确了可持续城市建设的关键领域。在梳理国际城市可持续性评价指标研究进展基础上,以SDG11为导向,结合传统统计数据,充分利用遥感数据与网络大数据等多源数据,建立了开放的城市可持续性评价指标体系框架,以期为我国城市在联合国框架下开展可持续性评价,推进城市可持续转型发展提供借鉴。     

城市交通噪声环境承载力分析模型及算法

以城市噪声环境容量为约束条件计算城市区域路网最大交通承载力。分析模型是一个双层优化问题,其中上层是噪声环境容量约束下的最大路网交通流量模型;下层是道路网上的用户均衡分配模型。应用遗传算法进行求解,仿真示例表明该模型和算法是可行的、有效的,可以为城市交通可持续发展的规划和需求管理提供依据。     

基于模糊综合评判城市社区评价模型仿真研究

研究城市社区认同性优化问题。针对目前城市社区认同性受到了多方面因素的综合影响,传统的认同评价算法很难全面的综合评价城市之间的差异,提出了一种模糊综合评判城市社区综合评价优化模型。主要技术在于采用了模糊综合评判算法首次应用在城市社区评价体系中。算法以城市社区优化设计为研究对象,在全面分析城市社区的相关需求的基础上,采用了改进的层次分析法多模型进行数学优化,并以城市社区居民住宅满意度为核心,建立了关于模糊综合评判的城市社区认同性多指标优化模型。最后案例实验结果表明,提出的新的模糊综合评判算法可以较好的评价出城市社区的满意度,具有一定的实际应用价值。     

面向可持续发展的煤炭城市动态评价体系研究

煤炭城市主要是以煤炭资源为生存和发展的主要资源之一,但是随着煤炭资源的枯竭以及煤炭对环境的造成危害,对煤炭城市的生存和发展造成威胁,因此加强对煤炭城市的可持续发展的研究是目前最为重要的问题,本文主要进行研究煤炭城市可持续发展的评价体系。     

A Quantum-inspired Ant Colony Optimization for solving a sustainable four-dimensional traveling salesman problem under type-2 fuzzy variable

In this paper, a Quantum-inspired Ant Colony Optimization (Qi-ACO) is proposed to solve a sustainable four-dimensional traveling salesman problem (4DTSP). In 4DTSP, various paths with a different number of conveyances are available to travel between any two cities. In this model, we have considered a sustainable 4DTSP in terms of emission as a constraint. Since travel costs and emissions are uncertain/imprecise in nature, so here we consider type-2 variables. Sustainable development in the traveling salesman problem (TSP) sector can be divided into two major sections: economy and environmental. Sustainable TSP development requires balancing to achieve the maximum benefits for these two sectors. For increasing development in sustainable transportation, we need to use some strategies for increasing sustainability. These strategies include improving route and vehicle selection, routing plan, vehicle speed, etc. The novelties of the proposed Qi-ACO algorithm are (i) Qubit generated based on the amount of emission of the vehicle as well as travel cost between two cities, (ii) pheromone initialized and updated depends on the qubit, (iii) quantum-inspired technique makes fast computation. The proposed sustainable 4DTSP is illustrated with some numerical data. The defuzzification of type-2 fuzzy variable based on the Critical value (CV) method is used in this model. The supremacy of the proposed method is established through some statistical tests. The proposed algorithm and its modified form can be easily adapted in ship routing, supply chain problems, and other fields.     

一种基于蚁群算法的K-means算法 ——在公路运输枢纽宏观布局规划中的应用

公路运输的发展有效促进了我国经济持续、快速的发展,但公路建设和运输枢纽建设呈现出不平衡性。因此需采用聚类分析对公路主枢纽城市进行聚类,划分层次来进行功能分析。K-means算法是聚类分析中使用最为广泛的算法之一,但算法具有初始中心点和聚类个数不确定等方面的缺点。针对其缺点,提出将基于蚁群算法的K-means算法应用于在公路运输枢纽布局规划中。实验结果表明,与单独使用两种算法相比,该算法更能有效地解决公路主枢纽城市的聚类问题。     

Knowledge-city index construction: An intellectual capital perspective

Knowledge cities are the most capable of producing sustainable economic growth. Cities, where relationships between people are more extensive, provide the most natural environment in which to look for evidence of the knowledge spillovers so emphasized by the endogenous growth theory. The intellectual capital approach considers the ability to transform knowledge and intangible resources into sustainable long-term wealth. The approach does not only contemplate sustainability and social wellbeing, but also intangible factors such as human development, economic structure, trade, image and innovation. This paper proposes a novel methodology to measure intangible capital as the growth capacity of knowledge cities based on intellectual capital using a composite index of seven intangible factors, disaggregated into 19 dimensions with 73 different indicators. Using this methodology, we rank 158 European cities for which information is available. The results show clear differences between northern and southern European cities. The top positions in the ranking are occupied by cities with a favorable balance in human capital; the most important growth factors in the most developed cities being research, development and innovation and image components, whilst these together with the human components are the most important growth factors in the poorest cities.     

Progresson Sustainable City Assessment Index System for SDGs

Urban sustainability has become the world’s most important urban issue.The evaluation index system is an effective tool to objectively diagnosethe current status and problems of sustainable development.The existing urban sustainability evaluation index systems are mostly based on traditional statistical data and they have different emphases.Due to the suitability of index and availability of data,etc,These index systems are hardly used for comparative evaluation amongdifferent cities.With the interpretation of the sustainable city connotation by the Sustainable Development Goals11,and the use of multi-source data such as remote sensing and network big data,it is possible to achieve higher resolution for urban sustainability assessment under a unified standard.Based on this,this work analyzes the evolution of the concept of urban sustainable development and identifies the key areas of the connotation of sustainable city construction.This work also summarizes the research progress of sustainability evaluation indicators and analyzes typical index system.Based on SDG11,this study establish an open city sustainability index system,with combining traditional statistical data and multi-source data,such as remote sensing data and network big data.The framework of the evaluation index system aims to provide reference for the sustainability evaluation of cities in China under the framework of the United Nations.     

A novel Bluetooth low energy based system for spatial exploration in smart cities

Smart Cities are employing information and communication technologies in the quest for sustainable economic development and the fostering of new forms of collective life. They facilitate connections between citizens and organizations that are of paramount importance for their long-term sustainability. As cities become more complex and their communities more dispersed, questions such as ‘where can I find …’ are increasingly pertinent. In this paper, we introduce NomaBlue, a new vision of spatial recognition in smart cities, the proposed system is based on an intelligent nomadic data collection and users' collaboration using smart Bluetooth technology. We demonstrate using two case-studies that our approach is capable of proposing an efficient spatial recognition service while supporting a range of users’ constraints, our system is disconnected from the internet, it can operate in any indoor/outdoor area, it doesn't require pre-defined geographic databases and uses a new concept of nomadic data collection and sharing to speed-up the circulating information in smart cities.     

基于Creator/Vega的居民小区仿真系统的实现

虚拟现实技术(VR)近几年来已成为信息技术领域研究开发的热点,其长足的进步,为城市的仿真提供了有效手段,对于城市规划和城市的可持续发展有着非常重要的意义.文中以城市居民小区为原型,以建模工具Creator、开发工具Vega为平台,采用层次型数据结构,完成了城市居民小区仿真模型的构建以及优化,并在此基础上利用VegaAPI,在Visual c 环境下实现了居民小区的漫游,满足了系统仿真的实时性要求,从而为城市居民小区的规划和可持续发展提供了可靠的保证.     

基于蚁群算法的建筑业可持续发展分析

本文以建筑业可持续发展体系分类为基础,应用计算机智能算法--蚁群算法对建筑业可持续发展各分类指数公式和综合指数公式进行优化,构建建筑业可持续发展评价模型.并将此模型应用于江苏省建筑业可持续发展水平的评价,评价结果与现状分析基本一致.结果表明该模型简单实用、有良好的通用性.     

A novel constructive-optimizer neural network for the traveling salesman problem.

In this paper, a novel constructive-optimizer neural network (CONN) is proposed for the traveling salesman problem (TSP). CONN uses a feedback structure similar to Hopfield-type neural networks and a competitive training algorithm similar to the Kohonen-type self-organizing maps (K-SOMs). Consequently, CONN is composed of a constructive part, which grows the tour and an optimizer part to optimize it. In the training algorithm, an initial tour is created first and introduced to CONN. Then, it is trained in the constructive phase for adding a number of cities to the tour. Next, the training algorithm switches to the optimizer phase for optimizing the current tour by displacing the tour cities. After convergence in this phase, the training algorithm switches to the constructive phase anew and is continued until all cities are added to the tour. Furthermore, we investigate a relationship between the number of TSP cities and the number of cities to be added in each constructive phase. CONN was tested on nine sets of benchmark TSPs from TSPLIB to demonstrate its performance and efficiency. It performed better than several typical Neural networks (NNs), including KNIES_TSP_Local, KNIES_TSP_Global, Budinich's SOM, Co-Adaptive Net, and multivalued Hopfield network as wall as computationally comparable variants of the simulated annealing algorithm, in terms of both CPU time and accuracy. Furthermore, CONN converged considerably faster than expanding SOM and evolved integrated SOM and generated shorter tours compared to KNIES_DECOMPOSE. Although CONN is not yet comparable in terms of accuracy with some sophisticated computationally intensive algorithms, it converges significantly faster than they do. Generally speaking, CONN provides the best compromise between CPU time and accuracy among currently reported NNs for TSP.