ارزیابی الگوریتم های تکاملی برای تجزیه و تحلیل موضعی دقت و عدم اطمینان از نقشه
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|78793||2014||15 صفحه PDF||سفارش دهید||8943 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 41, Issue 14, 15 October 2014, Pages 6346–6360
Pre-geodetic maps are an important part of our cultural heritage and a potential source of information for historical studies. Historical cartography should be evaluated in terms of precision and uncertainty prior to their use in any application. In the last decade, the majority of papers that address multi-objective optimization employed the concept of Pareto optimality. The goal of Pareto-based multi-objective strategies is to generate a front (set) of nondominated solutions as an approximation to the true Pareto-optimal front. This article proposes a solution for the problems of multi-objective accuracy and uncertainty analysis of pre-geodetic maps using four Pareto-based multi-objective evolutionary algorithms: HVSEA, NSGAII, SPEAII and msPESA. “The Geographic Atlas of Spain (AGE)” by Tomas Lopez in 1804 provides the cartography for this study. The results obtained from the data collected from the kingdoms of Extremadura and Aragon, sheets of maps (54-55-56-57) and (70-71-72-73), respectively, demonstrate the advantages of these multi-objective approaches compared with classical methods. The results show that the removal of 8% of the towns it is possible to obtain improvements of approximately 30% for HVSEA, msPESA and NSGAII. The comparison of these algorithms indicates that the majority of nondominated solutions obtained by NSGAII dominate the solutions obtained by msPESA and HVSEA; however, msPESA and HVSEA obtain acceptable extreme solutions in some instances. The Pareto fronts based on multi-objective evolutionary algorithms (MOEAs) are a better alternative when the uncertainty of map analyzed is high or unknown. Consequently, Pareto-based multi-objective evolutionary algorithms establish new perspectives for analyzing the positional accuracy and uncertainty of maps.