Noisy source recognition in multi noise plants by differential evolution

Since last few decades differential evolution algorithm (DE) has been successfully applied for solving many real life optimization problems. In this paper DE is applied to identifying the location of noisy sources in a multi noise plants. A trail noise technique is used to obtain the variation between trial sound pressure level (SPL) and exact SPL at monitoring points and then DE is employed in conjunction with the method of minimized variation square in seeking for the best locations and sound power level (SWLs). The results reveal that the significant locations and SWLs of noises can be precisely identified by DE.