期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B2
页码：253-256
出版社：Copernicus Publications
摘要：Within the last few decades, there has been exponential growth in the research, development, and utilization of Geospatial Information Systems (GIS). While GISs have been developed to challenge most types of spatial analysis problems, many of the more complex spatial problems are still beyond their current capabilities to solve. These types of problems often encounter large search spaces with large numbers of potential solutions. In such cases, standard analytical techniques typically cannot find optimal solutions to the problem within practical temporal and/or computational limits. One such problem within the field of spatial analysis is that of the routing problem. This paper focuses on the development of algorithmic solutions for the best path problem. Finding optimum path has many practical applications within the fields of operations research, logistics, distribution, supply chain management and transportation. In general, best path routing involves finding efficient routes for travellers along transportation networks, in order to minimize route length, service cost, travel time, number of vehicles, etc. This is a combinatorial optimization problem for which no simple solutions exist. As an alternative, solution techniques from the field of evolutionary computation is implemented and tested for solving instances of the best path. The field of evolutionary computation (EC) has developed to integrate several previously researched fields of related study into one. The sub-fields of EC include genetic algorithms, evolutionary programming, evolutionary strategies, and genetic programming. EC uses computational techniques that are analogous to the evolutionary mechanisms that work within natural biological systems, such as natural selection (i.e., survival of the fittest), crossover, mutation, etc. Within EC, these operators are used as a means of quickly evolving optimal or near-optimal solutions to a problem within a computational framework designed to represent a relevant search space.This paper scientifically reviews evolutionary algorithms in solving GIS problems. Based on their advantages and drawbacks of the methods a new algorithm called pseudo GA is developed. The algorithm is tested for various case studies. The results are presented and discussed. The result of performance analysis of the new algorithm is encouraging
