Hybrid GA Multiobjective Optimization for the Design of Compliant Micro-Actuators

This paper demonstrates the automatic design of a compliant grip-and-move manipulator by topology and shape optimization using a hybrid genetic algorithm. It presents the novel idea of integrating both grip and move behaviors within one simple compliant actuator mechanism. The manipulator employs two identical path generating compliant mechanisms with two degrees-of-freedom each so that it can grip a workpiece and move it to anywhere within its working area. They are difficult to design because their motions have to be analyzed by finite element methods and the relationship between their geometry, and their elastic behavior is highly complex and non-linear. The synthesis of such a mechanism in this work is achieved by a structural optimization approach based on a hybrid genetic algorithm and a morphological representation scheme for defining the structural geometry upon a finite element grid. The proposed hybrid algorithm integrates a simple local search strategy with a constrained multiobjective evolutionary algorithm. A novel constrained tournament selection is used as a single objective function in the local search strategy. The selection is utilized to determine whether a new solution generated in local search process will survive. Hooke and Jeeves method is applied to decide search path. Good initial solutions, the solutions to be mutated, are chosen for local search.