GAS IN DECOMPOSITION BASED DESIGN-SUBSYSTEM INTERACTIONS THROUGH IMMUNE NETWORK SIMULATION

The paper describes an adaptation of genetic algorithms (GA's) in deco mposition-based design of multidisciplinary systems. The coupled multi disciplinary 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 variab les allow for shorter string lengths to be used in the GA-based optimi zation in each subproblem, reducing the number of design alternatives to be explored, and hence also reducing the required number of functio n 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 desig n changes of all other subproblems. The design representation scheme, therefore, is common to both the design optimization step and the proc edure required to account for interaction among the subproblems. The d ecomposition based solution of a dual structural-control de-sign probl em is used as a test problem for the proposed approach. The convergenc e characteristics of the proposed approach are compared against those available from a nondecomposition-based method.