Improving Simplification Performance using FSA : Experimental Result

Simplification is generally eliminating unnecessary characteristics of the object without distortion the original shape of the object. Tolerance value is an important parameter needs to be considered produce a good result of simplification process. The current model of simplification has difficulty to execute simplification process in a short time and unable to determine the best tolerance value for simplification process. Fish Swarm Algorithm (FSA) one of the swarm intelligent algorithms with capability of estimating optimum solution shortly was executed in standard simplification design namely FSASimplification. Besides to improve performance of standard simplification, FSA-Simplification is proposed to determine the most optimum and best quality of tolerance value for simplification process. Therefore, this paper carries out the experimental for both simplification models. The roads in Miri, Sarawak with scale ratio 1:1000 is used for simplification process and be displayed in polyline data type. The result shows FSA-simplification has reduced the computing time by 9% till 12% that leads to a better simplification result compared to standard simplification. This performance indicates FSA-Simplification has given a better quality of simplification result. Besides that, 10 and 20 are the optimum value and best choice of tolerance value for simplification process. This performance indicates FSA-Simplification has given a better quality of simplification result.