Optimizing Engineering Designs Using a Combined Genetic Search

In the optimization of engineering designs, traditional search and optimization methods face at least two diiculties: (i) since each is specialized in solving a particular type of problem, one method does not work well on diierent types of problems (ii) most of them are designed to work on continuous search spaces. Since diierent optimal engineering design problems give rise to objective and constraint functions of varying degree of nonlinearity and since most engineering design problems involve mixed variables (zero-one, discrete, and continuous), designers often face diiculty in using the traditional methods. In this paper, a combined genetic search technique (GeneAS) is suggested to solve mixed-integer programming problems often encountered in engineering design activities. GeneAS uses a combination of binary-coded and real-coded GAs to handle diierent types of variables. In handling discrete variables, GeneAS restricts its search only to the permissible values of the variables. Thus, minimal search eeort is required in converging to the optimumsolution. The eeciency and ease of application of the proposed method are demonstrated by solving four diierent engineering design problems. Successful application of GeneAS in these problems suggests its use to other more complex engineering design problems.