摘要
A new chaotic genetic hybrid algorithm(CGHA) based on float point coding was put forward in this paper.Firstly,it used chaos optimization to search coarsely and produced a better initial population.Then,a power function carrier was adopted to improve the ergodicity and the sufficiency of the chaos optimization.Secondly,the genetic algorithm(GA) was used to search finely and guaranteed the population's evolution.To avoid the search being trapped in local minimum,a chaos degenerate mutation operator was designed to make the search converge to a global optimum quickly.Finally,CGHA was used to solve a typical mechanical optimization problem of shear stress checking for a cylinder helix spring.Compared with traditional penalty function method,chaos-Powell hybrid algorithm and standard GA,CGHA shows better performance in solution precision and convergence speed than those of the algorithms.Therefore,CGHA is a new effective way to solve the problems in mechanical optimization design.
A new chaotic genetic hybrid algorithm （CGHA） based on float point coding was put forward in this paper. Firstly, it used chaos optimization to search coarsely and produced a better initial population. Then, a power function carrier was adopted to improve the ergodicity and the sufficiency of the chaos optimization. Secondly, the genetic algorithm （GA） was used to search finely and guaranteed the population＇s evolution. To avoid the search being trapped in local minimum, a chaos degenerate mutation operator was designed to make the search converge to a global optimum quickly. Finally, CGHA was used to solve a typical mechanical optimization problem of shear stress checking for a cylinder helix spring. Compared with traditional penalty function method, chaos-Powell hybrid algorithm and standard GA, CGHA shows better performance in solution precision and convergence speed than those of the algorithms. Therefore, CGHA is a new effective way to solve the problems in mechanical optimization design.
基金
Sponsored by the National Natural Science Foundation of China (50875186)
the Research and Development Projects of Tianjin University of Technology and Education (KJ2008028,KJ2008043)
