Adaptive multimodal continuous ant colony optimization

Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima

OPTIMIZATION OF STATION LOCATIONS AND TRACK ALIGNMENTS FOR RAIL TRANSIT LINES

Designing urban rail transit systems is a complex problem, which involves the determination of station locations, track geometry, right-of-way type, and various other system characteristics. The existing studies overlook the complex interactions between railway alignments and station locations in a practical design process. This study proposes a comprehensive methodology that helps transit planners to concurrently optimize station locations and track alignments for an urban rail transit line. The modeling framework resolves the essential trade-off between an economically efficient system with low initial and operation cost and an effective system that provides convenient service for the public. The proposed method accounts for various geometric requirements and real-world design constraints for track alignment and stations plans. This method integrates a genetic algorithm (GA) for optimization with comprehensive evaluation of various important measures of effectiveness based on processing Geographical Information System (GIS) data. The base model designs the track alignment through a sequence of preset stations. Detailed assumptions and formulations are presented for geometric requirements, design constraints, and evaluation criteria. Three extensions of the base model are proposed. The first extension explicitly incorporates vehicle dynamics in the design of track alignments, with the objective of better balancing the initial construction cost with the operation and user costs recurring throughout the system's life cycle. In the second extension, an integrated optimization model of rail transit station locations and track alignment is formulated for situations in which the locations of major stations are not preset. The concurrent optimization model searches through additional decision variables for station locations and station types, estimate rail transit demand, and incorporates demand and station cost in the evaluation framework. The third extension considers the existing road network when selecting sections of the alignment. Special algorithms are developed to allow the optimized alignment to take advantage of links in an existing network for construction cost reduction, and to account for disturbances of roadway traffic at highway/rail crossings. Numerical results show that these extensions have significantly enhanced the applicability of the proposed optimization methodology in concurrently selecting rail transit station locations and generating track alignment

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Smart Energy and Intelligent Transportation Systems

With the Internet of Things and various information and communication technologies, a city can manage its assets in a smarter way, constituting the urban development vision of a smart city. This facilitates a more efficient use of physical infrastructure and encourages citizen participation. Smart energy and smart mobility are among the key aspects of the smart city, in which the electric vehicle (EV) is believed to take a key role. EVs are powered by various energy sources or the electricity grid. With proper scheduling, a large fleet of EVs can be charged from charging stations and parking infrastructures. Although the battery capacity of a single EV is small, an aggregation of EVs can perform as a significant power source or load, constituting a vehicle-to-grid (V2G) system. Besides acquiring energy from the grid, in V2G, EVs can also support the grid by providing various demand response and auxiliary services. Thanks to this, we can reduce our reliance on fossil fuels and utilize the renewable energy more effectively. This Special Issue “Smart Energy and Intelligent Transportation Systems” addresses existing knowledge gaps and advances smart energy and mobility. It consists of five peer-reviewed papers that cover a range of subjects and applications related to smart energy and transportation

Symmetric and Asymmetric Data in Solution Models

This book is a Printed Edition of the Special Issue that covers research on symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multicriteria decision-making (MCDM) problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the book

Habitat mapping and multiple criteria analysis for ecotourism planning in Lantau Island with GIS.

Wong Kwan Kit.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 276-315).Abstracts in English and Chinese.ACKNOWLEDGEMENTS --- p.IIABSTRACT --- p.IIITABLE OF CONTENTS --- p.VIIIAPPENDICES (IN CD) --- p.XIIILIST OF TABLES --- p.XVLIST OF FIGURES --- p.XIXChapter CHAPTER 1 --- lNTRODUCTION --- p.1Chapter 1.1 --- BACKGROUND TO THE STUDY --- p.1Chapter 1.1.1 --- Ecotourism Opportunity in Hong Kong and Ecotourism Planning --- p.1Chapter 1.1.2 --- Habitat Mapping and Conservation Areas Selection --- p.2Chapter 1.1.3 --- Lantau Island and the Concept Plan --- p.4Chapter 1.2 --- OBJECTIVES OF THE STUDY --- p.7Chapter 1.3 --- SIGNIFICANCE OF THE STUDY --- p.8Chapter 1.4 --- SCOPE OF THE STUDY --- p.10Chapter 1.5 --- ORGANIZATION OF THE THESIS --- p.11Chapter CHAPTER 2 --- LITERATURE REVIEW --- p.14Chapter 2.1 --- WILDLIFE HABITAT MAPPING --- p.14Chapter 2.1.1 --- Habitat Requirements and Factors Influencing Wildlife Distribution --- p.15Chapter 2.1.2 --- Habitat Mapping: Past and Present --- p.17Chapter 2.1.3 --- "Remote Sensing, GIS and Habitat Mapping" --- p.20Chapter 2.1.4 --- Multivariate Statistical Habitat Modeling Approaches --- p.21Chapter 2.2 --- BIODIVERSITY AND CONSERVATION --- p.30Chapter 2.2.1 --- "Biological Diversity, Species Richness and Conservation Planning" --- p.30Chapter 2.2.2 --- Gap Analysis Program (GAP) and Conservation Planning --- p.34Chapter 2.3 --- ECOTOURISM PLANNING AND MULTIPLE CRITERIA ANALYSIS (MCA) --- p.37Chapter 2.3.1 --- ECOTOURISM AND PLANNING MODEL --- p.37Chapter 2.3.2 --- GIS and Multiple Criteria Analysis as decision support tools --- p.42Chapter 2.4 --- SUMMARY --- p.49Chapter CHAPTER 3 --- METHODOLOGY --- p.51Chapter 3.1 --- lNTRODUCTION --- p.51Chapter 3.2 --- STUDY SITE DESCRIPTION --- p.53Chapter 3.3 --- METHODOLOGY OVERVIEW --- p.56Chapter 3.4 --- GEOGRAPHICAL INFORMATION SYSTEM (GIS) DATABASE --- p.58Chapter 3.4.1 --- Hong Kong Biodiversity Survey --- p.58Chapter 3.4.2 --- Land Cover Classification of Hong Kong --- p.65Chapter 3.4.2.1 --- Acquisition and Pre-processing of Remotely-Sensed Data --- p.65Chapter 3.4.2.2 --- Land Cover Classification and Post Classification --- p.67Chapter 3.4.3 --- GIS Database --- p.69Chapter 3.4.3.1 --- Acquisition of GIS Data --- p.69Chapter 3.4.3.2 --- GIS Operations --- p.69Chapter 3.4.3.3 --- Criteria for Multiple Criteria Analysis (MCA) --- p.80Chapter 3.5 --- WILDLIFE HABITAT MAPPING --- p.81Chapter 3.5.1 --- Ecological Niche Factor Analysis (ENFA) --- p.83Chapter 3.5.1.1 --- Generation of Pseudo-absence Data-point --- p.87Chapter 3.5.2 --- Binary Logistic Regression Model (BLRM) --- p.88Chapter 3.5.3 --- Generalized Additive Model (GAM) --- p.95Chapter 3.5.4 --- Model Comparison and Selection --- p.100Chapter 3.5.5 --- Identification of Biodiversity Hotspots --- p.101Chapter 3.5.6 --- Overlap Analysis of Taxonomic Groups --- p.102Chapter 3.5.7 --- Gap Analysis --- p.102Chapter 3.6 --- SITE SELECTION FOR COMPATIBLE TOURISM ACTIVITIES THROUGH MCA --- p.103Chapter 3.6.1 --- Establishment of Evaluation Criteria: Constraints and Factors --- p.103Chapter 3.6.2 --- Standardization of Factors --- p.104Chapter 3.6.3 --- Weights Assignment and Analytic Hierarchy Process (AHP) --- p.106Chapter 3.6.4 --- Decision Rule: The Simple Additive Weighting method (SAW) --- p.111Chapter 3.7 --- FORMULATION OF ZONING PLAN THROUGH MOLA --- p.112Chapter 3.8 --- EVALUATION OF THE CONCEPT PLAN FOR LANTAU --- p.119Chapter 3.9 --- SUMMARY --- p.121Chapter CHAPTER 4 --- RESULTS AND DISCUSSION (l) 一 MULTIVARIATE STATISTICAL WILDLIFE HABITAT MAPPING AND BIODIVERSITY HOTSPOTS IDENTIFICATION --- p.125Chapter 4.1 --- lNTRODUCTION --- p.125Chapter 4.2 --- DATA EXPLORATION --- p.126Chapter 4.3 --- IDENTIFICATION OF HABITAT FOR AMPHIBIAN SPECIES --- p.126Chapter 4.3.1 --- Ecological Niche Factor Analysis (ENFA) --- p.127Chapter 4.3.2 --- Binary Logistic Regression Model (BLRM) --- p.131Chapter 4.3.3 --- Generalized Additive Model (GAM) --- p.135Chapter 4.4 --- IDENTIFICATION OF HABITAT FOR BIRD SPECIES --- p.139Chapter 4.4.1 --- Ecological Niche Factor Analysis (ENFA) --- p.141Chapter 4.4.2 --- Binary Logistic Regression Model (BLRM) --- p.144Chapter 4.4.3 --- Generalized Additive Model (GAM) --- p.149Chapter 4.5 --- IDENTIFICATION OF HABITAT FOR BUTTERFLY SPECIES --- p.153Chapter 4.5.1 --- Ecological Niche Factor Analysis (ENFA) --- p.154Chapter 4.5.2 --- Binary Logistic Regression Model (BLRM) --- p.158Chapter 4.5.3 --- Generalized Additive Model (GAM) --- p.163Chapter 4.6 --- IDENTIFICATION OF HABITAT FOR DRAGONFLY SPECIES --- p.168Chapter 4.6.1 --- Ecological Niche Factor Analysis (ENFA) --- p.169Chapter 4.6.2 --- Binary Logistic Regression Model (BLRM) --- p.173Chapter 4.6.3 --- Generalized Additive Model (GAM) --- p.178Chapter 4.7 --- IDENTIFICATION OF HABITAT FOR MAMMAL SPECIES --- p.183Chapter 4.7.1 --- Ecological Niche Factor Analysis (ENFA) --- p.183Chapter 4.7.2 --- Binary Logistic Regression Model (BLRM) --- p.186Chapter 4.7.3 --- Generalized Additive Model (GAM) --- p.189Chapter 4.8 --- MODEL SELECTION --- p.192Chapter 4.9 --- IDENTIFICATION OF BIODIVERSITY HOTSPOTS --- p.194Chapter 4.10 --- CORRELATIONS BETWEEN TAXONOMIC GROUPS --- p.196Chapter 4.11 --- GAP ANALYSIS --- p.197Chapter 4.12 --- SUMMARY --- p.203Chapter CHAPTER 5 --- RESULTS AND DISCUSSION (II) 一 TOURISM PLANNING AND ZONE ALLOCATION --- p.205Chapter 5.1 --- INTRODUCTION --- p.205Chapter 5.2 --- SITE SELECTION FOR COMPATIBLE TOURISM ACTIVITIES lN LANTAU ISLAND --- p.206Chapter 5.2.1 --- Potential Campsite selection --- p.206Chapter 5.2.1.1 --- Evaluation factors --- p.207Chapter 5.2.1.2 --- Factor weights from the AHP --- p.208Chapter 5.2.1.3 --- Results --- p.209Chapter 5.2.2 --- Potential Hiking Route Selection --- p.213Chapter 5.2.2.1 --- Evaluation factors --- p.214Chapter 5.2.2.2 --- Factor weights from the AHP --- p.215Chapter 5.2.2.3 --- Results --- p.217Chapter 5.2.3 --- Potential Cycling and Picnic Site Selection --- p.225Chapter 5.2.3.1 --- Evaluation factors --- p.225Chapter 5.2.3.2 --- Factor weights from the AHP --- p.227Chapter 5.2.3.3 --- Results --- p.228Chapter 5.2.4 --- Potential Tourism Development Site Selection --- p.234Chapter 5.2.4.1 --- Evaluation factors --- p.234Chapter 5.2.4.2 --- Factor weights from the AHP --- p.235Chapter 5.2.4.3 --- Results --- p.236Chapter 5.3 --- ZONE ALLOCATION AND ZONING PLANS --- p.240Chapter 5.3.1 --- Potential Conflicting Sites --- p.240Chapter 5.3.2 --- Scenario 1: Conservation-oriented --- p.242Chapter 5.3.3 --- Scenario 2: Equal-preference --- p.246Chapter 5.3.4 --- Scenario 3: Recreation-and-tourism-development-oriented --- p.249Chapter 5.4 --- EVALUATION AND RECOMMENDATION FOR THE CONCEPT PLAN --- p.252Chapter 5.4.1 --- Exploring Additional Conservation Needs --- p.252Chapter 5.4.2 --- Maximizing Recreational Opportunities --- p.257Chapter 5.4.3 --- Tourism Development --- p.258Chapter 5.5 --- SUMMARY --- p.262Chapter CHAPTER 6 --- CONCLUSION --- p.264Chapter 6.1 --- SUMMARY OF THE STUDY --- p.264Chapter 6.2 --- LIMITATION OF THE STUDY --- p.267Chapter 6.3 --- RECOMMENDATIONS --- p.271REFERENCES --- p.27

Z-Numbers-Based Approach to Hotel Service Quality Assessment

In this study, we are analyzing the possibility of using Z-numbers for measuring the service quality and decision-making for quality improvement in the hotel industry. Techniques used for these purposes are based on consumer evalu- ations - expectations and perceptions. As a rule, these evaluations are expressed in crisp numbers (Likert scale) or fuzzy estimates. However, descriptions of the respondent opinions based on crisp or fuzzy numbers formalism not in all cases are relevant. The existing methods do not take into account the degree of con- fidence of respondents in their assessments. A fuzzy approach better describes the uncertainties associated with human perceptions and expectations. Linguis- tic values are more acceptable than crisp numbers. To consider the subjective natures of both service quality estimates and confidence degree in them, the two- component Z-numbers Z = (A, B) were used. Z-numbers express more adequately the opinion of consumers. The proposed and computationally efficient approach (Z-SERVQUAL, Z-IPA) allows to determine the quality of services and iden- tify the factors that required improvement and the areas for further development. The suggested method was applied to evaluate the service quality in small and medium-sized hotels in Turkey and Azerbaijan, illustrated by the example

Urban Street Networks and Sustainable Transportation

Urban street space is challenged with a variety of emerging usages and users, such as various vehicles with different speeds, passenger pick-up and drop-off by mobility services, increasing parking demand for a variety of private and shared vehicles, new powertrains (e.g., charging units), and new vehicles and services fueled by digitalization and vehicle automation. These new usages compete with established functions of streets such as providing space for mobility, social interactions, and cultural and recreational activities. The combination of these functions makes streets focal points of communities that do not only fulfill a functional role but also provide identity to cities. Streets are prominent parts of cities and are essential to sustainable transport plans. The main aim of the Street Networks and Sustainable Transportation collection is to focus on urban street networks and their effects on sustainable transportation. Accordingly, various street elements related to mobility, public transport, parking, design, and movement of people and goods at the street level can be included