USE OF PSO ALGORITHM IN DETERMINATION OF THE OPTIMUM OBSERVATION WEIGHTS IN THE DEFORMATION MONITORING NETWORKS

Geodetic networks are very important tools used to monitor earth and/or structural deformations. However, a geodetic network must be designed to meet sufficiently some network quality requirements such as precision, reliability, or sensitivity. This is the subject of geodetic network optimization. The determination of the observation weights problem in the deformation monitoring networks can be dealt with as an optimization procedure, this problem can performed by solving the second-order design (SOD) problem. Traditional methods have been used for geodetic optimization tasks. On the other hand, some heuristic techniques have been started to be used recently in geodetic science such as the Particle Swarm Optimization (PSO) algorithm. The general purpose optimization method known as Particle Swarm Optimization (PSO) has received much attention in past years, with many attempts to find the variant that performs best on a wide variety of optimization problems. In this paper, the PSO algorithm, a stochastic global optimization method, has been employed for the determination of the optimum observation weights to be measured in the field that will meet the postulated criterion matrix at a reasonable precision. The fundamentals of the method and a numeric example are given. تارٌتؤذت نذم ةرهاذالا ذذهل امل كلذو نٌسٌدوٌجلا هجاوت ًتلا ماهملا مهأ نم ةدحاو تآشنملا تاهوشتو هٌضرلأا تاهوشتلا ةبقارم ٌجلا رذطلا نأ تبت دقلو .ناسنلإا ةملاسو نمأ ًلع ةرشابم كةٌذسٌدوٌجلا تاجبذشلاك ةذقٌقد تلاٌذلحتو داذحرؤب ةبوحذحملا ةٌذسٌدو رذبتمٌو . دوذجلا رٌٌاذممل اذقبط ةٌذسٌدوٌجلا تاجبذشلا ممذحتو .تاهوذشتلا ذذه لذتم ةذمباتمو ةذساردل ةمدمتذسملا رذطلا لضفأ نم ذطلا لذضفأ رذبتمتو .لٌلاذجتلا لذقؤب اذهرجذ للاذسلا رٌٌاذمملا ذٌقحت نذجمأ اذك اذٌلاتم ةجبذشلا مٌمذحت ممٌذحتلا داذجٌلإ ةٌذضاٌرلا ر لوذلحلا نٌذٌمت ةذٌيٌج ًذلك مٌمذحتلا ةذٌلمع لذٌوحت نذجمأ اذهللام نذم ًتلاو ًلتملا لولحلا ةٌران ٌبطت ىلع دمتمت ًتلا ًه لتملأا ىذلتملا نااولأا نٌذٌمت ًف ثحبت ًهو ، ةٌسٌدوٌجلا تاجبشلا مٌمحت رط ىدحا نم ةٌناتلا مٌمحتلا ةلؤسم ربتمتو .لئاسم سممل ل .ةجبذشلا لجذشو ةذبولطملا ةذقدلا دٌدحت ضارتفاب داحرلأ داذجٌا ًذف ارمإذم ًعانطذحلإا ناجذذلا رذط مادمتذسا ًذف روذطتلا ذمو ثذحبلا اذذه ًذفو .ةذيلتمملا مٌمذحتلا لئاذسم لذح ًذف رذطلا ذذه لذتم مادمتذسك نذجمأ ةدذقمملاو ةطٌذسبلا لئاذسملل ىذلتملا لوذلحلا ج تامٌسجلا بارسأ نٌسحت ةٌماراوم تمدمتسا .ةٌناتلا مٌمحتلا ةلؤسمل لتملأا لحلا داجٌك ًف ًعانحلا ناجذلا رط يدحإ