Intelligent Identification on Hydraulic Parameters of Ship Lock Based Generalized Genetic Algorithms

In hydroscience investigations, there are many hydraulic parameters to need identifying by use of optimization methods. According to 'Natural Selection' from Darwinism, Genetic Algorithms (GA) has developed rapidly as effective and much robust optimization technique in recent ten years. But it isn't easily applied to practice for Simple Genetic Algorithms (SGA) has the disadvantages of slow convergence rate, premature convergence and stagnation, etc. Enlightened from Accelerating Genetic Algorithms (AGA), the author presented Generalized Genetic Algorithms (GGA) to settle the problem. GGA inherits ancestors genes and imitates trend behavior in nature. It can preserve excellent individuals' diversity and uses excellent individual room of ancestors as propagating room of next generation. GGA generalizes SGA and AGA. When GGA's parameters are changed, more kinds of GAs may be designed Then in this paper, GGA was applied to identify hydraulic parameters of ship lock, that is, inertia head of valve opening with chamber filling and discharge coefficient of filling and emptying system, and the results indicate that GGA is fit for identifying hydraulic parameters because of its rapid convergence rate and high convergence precision. Thus, GGA will possibly provide a new idea to model hydraulic process of ship lock accurately.