GA's in decomposition based design-subsystem interactions through immune network simulation

Abstract

The paper describes an adaptation of genetic algorithms (GA's) in decomposition-based design of multidisciplinary systems. The coupled multidisciplinary design problem is adaptively deomposed into a number of smaller subproblems, each with fewer design variables, and the design in each subproblem allowed to proceed in parallel. Fewer design variables allow for shorter string lengths to the used in the GA-based optimization in each subproblem, reducing the number of design alternatives to be explored, and hence also reducing the required number of function evaluations to convergence. A novel procedure is proposed to account for interactions between the decomposed subproblems, and is based on the modelling of the biological immune system. This approach also uses the genetic algorithm approach to update in each subproblem the design changes of all other subproblems. The design representation scheme, therefore, is common to both the design optimization step and the procedure required to account for interaction among the subproblems. The decomposition based solution of a dual structural-control design problem is used as a test problem for the proposed approach. The convergence characteristics of the proposed approach are compared against those available from a nondecomposition-based method.