Hybrid genetic algorithm for optimizing structural design problems

Genetic algorithms (GAs) are suited for solving structural design problems, since they handle the design variables efficiently. This ability of GAs considers them as a good choice for optimization problems. Nevertheless, there are many situations where the conventional genetic algorithms do not perform particularly well, and so various methods of hybridization have been proposed. Thus, the paper develops a hybrid genetic algorithm (HGA) to incorporate a local convergence method and precision search method around optimum in the genetic algorithms. In a case study, it is shown that HGA is able consistently to provide efficient, fine quality solutions and provide a significant capability for solving structural design problems.