Epidemiological Studies Distributed Simulatiom Model Based On The Classificatiom Optimizatiom Space Constraints

Research questions about constrained optimization problem is conducting scientific research and engineering calculations often encountered mathematical planning, in recent years, how to solve constrained optimization problems related to gradually develop into a computational model and evolutionary algorithms related. In this paper, you need to use research-based constrained optimization, Use classification optimization method to build analytical models, spatial distribution of human epidemiological models.Five years from the start of the twentieth century, The method of calculating nonlinear constrained optimization variety, The method of calculating nonlinear constrained optimization variety, With the emergence and development of computer, For the nonlinear constrained optimization method has played a significant role in promoting, However, the calculation of this type of questions need to follow a principle, Is to be converted to an unconstrained nonlinear constrained optimization algorithm to solve, After calculating a single or multiple problems to solve nonlinear constrained optimization problem. We use adaptive particle swarm optimization algorithm, by this method to solve unconstrained optimization, And by expanding the use of particle swarm method and then to solve the nonlinear constrained optimization problem.The main work of this paper are as follows:(1) Through development and research, the particle swarm optimization algorithm to solve the nonlinear constrained optimization, the first step in establishing a similar function, Then particle swarm optimization and cross-referencing, Thus leads to sub-problem of the original problem, And research Lagrange Multiplier function equation factor correction and punishment after. By using the value of the sub-function method and the filter method, Exploratory study whether the process can be received, at the same steps can be accepted to the temptation to increase the chance. Through the use of adaptive PSO this method to solve nonlinear constrained optimization problem, at the same time able to judge the convergence of the algorithm.(2) Establish generalized nonlinear constrained optimization model:Mainly the use of particle swarm optimization, the method includes calculating nonlinear equations of the nonlinear problem simplistic. Specifically by introducing slack variables, by using the method set to change in a positive inequality constrained problem becomes constrained optimization equation, and use the same approach to constrained optimization problem into an unconstrained optimization problem to solve.