An Improved Differential Evolution Algorithm for Maritime Collision Avoidance Route Planning

High accuracy navigation and surveillance systems are pivotal to ensure efficient ship route planning and marine safety. Based on existing ship navigation and maritime collision prevention rules, an improved approach for collision avoidance route planning using a differential evolution algorithm was developed. Simulation results show that the algorithm is capable of significantly enhancing the optimized route over current methods. It has the potential to be used as a tool to generate optimal vessel routing in the presence of conflicts

Design of a System for Multiple Route Selection in the Presence of Flooding

Safe navigation during flooding is integral in minimizing loss of life. Navigation has throughout literature been treated as a search problem with the aim of optimizing certain impedance. The earliest study of path finding started in the late 1800s forming the basis of depth-first search techniques. This was followed by the introduction of a lot of popular algorithms including Dijkstra’s, A*and Bellman-Ford. Recently, the study of path planning – for road networks based on heuristics for dynamic or partially known environments has gained a lot of attention. In this thesis, we present a unique approach to finding multiple competitive paths between two locations on a street network that also considers road flooding. The key idea is to find a cost optimal solution for two locations using Dijkstra’s algorithm. We then penalize the found solution by increasing the traversal cost of one segment or the whole path, forcing the search algorithm to find alternative solutions. This framework is developed for the street network the City of Houston leveraging the capabilities of ArcGIS Desktop and Python scripting. The proposed algorithm is evaluated for quality and safety of resultant routes. This is done by comparing route lengths, elevations, widths, percentage of duplicate road segments, maximum speed limits of the obtained paths. We also conducted an experimental evaluation that shows an elevated sensitivity towards these factors as compared to the standard shortest path algorithms.Computer Science, Department o

Infiltration Route Analysis Using Thermal Observation Devices (TOD) and Optimization Techniques in a GIS Environment

Infiltration-route analysis is a military application of geospatial information system (GIS) technology. In order to find susceptible routes, optimal-path-searching algorithms are applied to minimize the cost function, which is the summed result of detection probability. The cost function was determined according to the thermal observation device (TOD) detection probability, the viewshed analysis results, and two feature layers extracted from the vector product interim terrain data. The detection probability is computed and recorded for an individual cell (50 m × 50 m), and the optimal infiltration routes are determined with A* algorithm by minimizing the summed costs on the routes from a start point to an end point. In the present study, in order to simulate the dynamic nature of a real-world problem, one thousand cost surfaces in the GIS environment were generated with randomly located TODs and randomly selected infiltration start points. Accordingly, one thousand sets of vulnerable routes for infiltration purposes could be found, which could be accumulated and presented as an infiltration vulnerability map. This application can be further utilized for both optimal infiltration routing and surveillance network design. Indeed, dynamic simulation in the GIS environment is considered to be a powerful and practical solution for optimization problems. A similar approach can be applied to the dynamic optimal routing for civil infrastructure, which requires consideration of terrain-related constraints and cost functions

Improved GWO Algorithm for UAV Path Planning on Crop Pest Monitoring

Agricultural information monitoring is the monitoring of the agricultural production process, and its task is to monitor the growth process of major crops systematically. When assessing the pest situation of crops in this process, the traditional satellite monitoring method has the defects of poor real-time and high operating cost, whereas the pest monitoring through Unmanned Aerial Vehicles (UAVs) effectively solves the above problems, so this method is widely used. An important key issue involved in monitoring technology is path planning. In this paper, we proposed an Improved Grey Wolf Optimization algorithm, IGWO, to realize the flight path planning of UAV in crop pest monitoring. A map environment model is simulated, and information traversal is performed, then the search of feasible paths for UAV flight is carried out by the Grey Wolf Optimization algorithm (GWO). However, the algorithm search process has the defect of falling into local optimum which leading to path planning failure. To avoid such a situation, we introduced the probabilistic leap mechanism of the Simulated Annealing algorithm (SA). Besides, the convergence factor is modified with an exponential decay mode for improving the convergence rate of the algorithm. Compared with the GWO algorithm, IGWO has the 8.3%, 16.7%, 28.6% and 39.6% lower total cost of path distance on map models with precision of 15, 20, 25 and 30 respectively, and also has better path planning results in contrast to other swarm intelligence algorithms

Semi-automated modeling approaches to route selection in GIS

As an alternative to traditional graphical intuitive approaches (GIA), a semi-automated modeling approach (SMA) can more efficiently identify linear routes by using powerful iterative and automated methods. In this research, two case studies were investigated to examine critical issues relating to the accuracy and effectiveness of raster-defined algorithmic approaches to linear route location. The results illustrate that different shortest-path algorithms do not necessarily result in markedly different linear routes. However, differing results can occur when using different neighboring-cell links in the cell-based route network construction. Cell-based algorithmic approaches in both Arc/Info and IDRISI software generate very similar results which are comparable to linear modeling with greater than eight neighboring-cell links. Given a specific shortest-path algorithm and its route searching technique, the use of a finer spatial resolution only results in a narrower and smoother route corridor. Importantly, cost surface models can be generated to represent differing cumulative environmental \u27costs\u27 or impacts in which different perceptions of environmental cost can be simulated and evaluated.;Three different simulation techniques comprising Ordered Weighted Combination models (OWC), Dynamic Decision Space (DDS), and Gateway-based approaches, were used to address problems associated with concurrent and dynamic changes in multi-objective decision space. These approaches provide efficient and flexible simulation capability within a dynamic and changing decision space. When aggregation data models were used within a Gateway approach the match of resulting routes between GIA and SMA analyses is close. The effectiveness of SMA is greatly limited when confronted by extensive linear and impermeable barriers or where data is sparse. Overall, achieving consensus on environmental cost surface generation and criteria selection is a prerequisite for a successful SMA outcome. It is concluded that SMA has several positive advantages that certainly complement a GIA in linear route siting and spatial decision-making

Move Table: An Intelligent Software Tool for OptimalPath Finding and Halt Schedule Generation

This study aims to help army officials in taking decisions before war to decide the optimalpath for army troops moving between two points in a real world digital terrain, consideringfactors like traveled distance, terrain type, terrain slope, and road network. There can optionallybe one or more enemies (obstacles) located on the terrain which should be avoided. A tile-basedA* search strategy with diagonal distance and tie-breaker heuristics is proposed for finding theoptimal path between source and destination nodes across a real-world  3-D  terrain. A performancecomparison (time analysis, search space analysis, and accuracy) has been made between themultiresolution A* search and the proposed tile-based A* search for large-scale digital terrainmaps. Different heuristics, which are used by the algorithms to guide these to the goal node,are presented and compared to overcome some of the computational constraints associated withpath finding on large digital terrains. Finally, a halt schedule is generated using the optimal path,weather condition, moving time, priority and type of a column, so that the senior military plannerscan strategically decide in advance the time and locations where the troops have to halt orovertake other troops depending on their priority and also the time of reaching the destination

Graph models of habitat mosaics

Graph theory is a body of mathematics dealing with problems of connectivity, flow, and routing in networks ranging from social groups to computer networks. Recently, network applications have erupted in many fields, and graph models are now being applied in landscape ecology and conservation biology, particularly for applications couched in metapopulation theory. In these applications, graph nodes represent habitat patches or local populations and links indicate functional connections among populations (i.e. via dispersal). Graphs are models of more complicated real systems, and so it is appropriate to review these applications from the perspective of modelling in general. Here we review recent applications of network theory to habitat patches in landscape mosaics. We consider (1) the conceptual model underlying these applications; (2) formalization and implementation of the graph model; (3) model parameterization; (4) model testing, insights, and predictions available through graph analyses; and (5) potential implications for conservation biology and related applications. In general, and for a variety of ecological systems, we find the graph model a remarkably robust framework for applications concerned with habitat connectivity. We close with suggestions for further work on the parameterization and validation of graph models, and point to some promising analytic insights. © 2009 Blackwell Publishing Ltd/CNRS

Optimization of resource storage location for managing flood emergencies.

Strategic infrastructure plays a key role in the functioning of urban areas, especially when dealing with emergency response to natural disasters. Urban areas and their infrastructure are threatened by natural hazards, which is likely to be exacerbated by climate change and intense urbanization in the near future. The UK National Flood Resilience Review (2016) committed £2.3 billion to be invested to reduce flood risk, of which £12.5 million specifically for temporary defenses. At present, the state of the art does not provide a proven efficient methodology specifically designed to optimally invest these resources; in light of this, a consolidated urban planning spatial optimization methodology is originally used for allocating resource storing space and ultimately optimize flood emergency management. This study developed and applied a RAOGA (Resource Allocation Optimization Genetic Algorithm) to balance the particular trade-off between simultaneous minimization of response time and costs. The presented optimization framework balances several competing tensions that include: (1) the identification of, and the cost of using, possible sites (warehouses) to store flood temporary defenses; (2) the identification of strategic infrastructure location; (3) transport optimization for moving emergency response resources into place. The methodology is applied to a regional case study (Yorkshire, UK) as proof of concept. Such a framework has the potential to lead a new generation of mathematically-based emergency response planning, targeted to policy makers dealing with urban planning and emergency management