Research Of Qga On The Mechanical Optimization Problems

Optimization design, as a new branch of science in recent years, is the result of optimization technique and computer technology applied in design.Optimization design provides an important scientific design method for engineering designer, which seeks the most perfect and optimum design proposal and greatly improves design efficiency and quality. Genetic Algorithm (GA) is an overall random searching method based on the Darwin's evolution and Mendelian. Genetic algorithm has better adaptability and wide accomodation. But it also has the disadvantages of poor local search ability, slow convergence and other defects. Quantum Genetic Algorithm (Quantum Genetic QGA) is produced by combining Quantum computing and Genetic Algorithm. And it is got enormous attention because of its small population size, higher ability to find the global optimal value, fast convergence rate and time-saving.Based on QGA ,a new Real Coded Quantum Genetic Algorithm(RQGA)come up in this paper. And the example system demonstrates the effectivity and effeciency of the proposed method compared with SGA and QGA. The main research contents are as follows:(1) Propose a new algorithm named Real Coded Quantum Genetic Algorithm(RQGA). After systematically introduce algorithm flow chart, key techniques and characteristics of SA and QGA, based on the characteristics of mechanical optimization problems, this paper proposes a new real number coded quantum genetic algorithm, and introduces coding method, operators, algorithm flow chart and etc. Further more, this paper also describes the algorithm implementation based on a Java language.(2) Take reducer that is widely used in mechanical for example, and study the application of RQGA in the mechanical structure optimization design.Establish the optimization model, adopt RQGA to solve it, and analyze by comparison to the solution of SA and QGA. And the comparision shows the validity and feasibility of the RQGA.(3) Take rod mechanism widely used in mechanical for example, and study the application of RQGA in the mechanism optimum design. Establish the optimization model, adopt RQGA to solve it, and analyze by comparison to the solution of SA and QGA. And the comparision shows the validity and feasibility of the RQGA. For connecting rod mechanism path optimization problem, there are large deviations between the optimal solutions and the targets. So the objective function is improved, but the problem is still not got reasonable solution. Therefore, for connecting rod mechanism path optimization problem, the QGA is still need to research and improve.