Optimization Design of Manipulator Based on the Improved Immune Genetic Algorithm

Abstract

In order to further improve the design effect of rolling transport mechanism of manipulator, an improved immune genetic algorithm (IIGA) is introduced to execute the optimization design in this paper. Aiming at the shortages of blind search and weak local optimization ability of genetic algorithm (GA), inspired by the antibody diversity in biological immnue system, the information entropy is used to construct the expected reproduction rate of antibody, and then the antibodies are selected reasonably. In addition, a memmory base is also used to improve the population diversity. Compared with the genetic algorithm, the simulation results of function optimization show that the IIGA is characterized by strong global optimization ability and quick convergence speed, and the local convergence is solved well. The optimization results of manipulator not only verify the validity of the IIGA, but also show its stronger optimization ability than GA.