Research on acoustical hoods used in industry has been widely discussed; however, the assessment of shape optimization on space-constrained close-fitting acoustic hoods by adjusting design parameters has been neglected. Moreover, the acoustical performance for a one-layer acoustic hood used in a high intensity environment seems to be insufficient. Therefore, an assessment of an optimally shaped acoustical hood with two layers will be proposed. In this paper, a numerical case for depressing the noise level of a piece of equipment by optimally designing a shaped two-layer close-fitting acoustic hood under a constrained space will be introduced. Furthermore, to optimally search for a better designed set for the multi-layer acoustical hood, an artificial immune method (AIM) has been adopted as well. Consequently, this paper provides a quick and effective method to reduce equipment noise by optimally designing a shaped multi-layer close-fitting acoustic hood via the AIM searching technique.
In the calculations presented in the article, an artificial immune system (AIS) was used to plan the routes of the fleet of delivery vehicles supplying food products to customers waiting for the delivery within a specified, short time, in such a manner so as to avoid delays and minimize the number of delivery vehicles. This type of task is classified as an open vehicle routing problem with time windows (OVRPWT). It comes down to the task of a traveling salesman, which belongs to NP-hard problems. The use of the AIS to solve this problem proved effective. The paper compares the results of AIS with two other varieties of artificial intelligence: genetic algorithms (GA) and simulated annealing (SA). The presented methods are controlled by sets of parameters, which were adjusted using the Taguchi method. Finally, the results were compared, which allowed for the evaluation of all these methods. The results obtained using AIS proved to be the best.