Evolutionary optimization techniques like PSO, ACO and Neural Networks offer different performance guarantees for difficult problems. These techniques are inherited from biological behavior of organic species. A detailed study and comparison on the techniques reveals differences in their performances at various levels. It is essential to understand performance variance to apply the optimization techniques to applications as appropriate. Accuracy and precision are significant parameters which determine the quality of the optimization techniques. This paper primarily compares PSO/ACO with Neural Network technique for classification of vehicle dataset and further validates the accuracy parameter with various conventional methods. The results are compared and inference to demonstrate the performance rationale between the methods with the use of KEEL tool.
Volume 11 | 06-Special Issue
Pages: 1450-1458