Based on the analysis of inertia weight and global best fitness of the standard PSO,a PSO method is described with self-adaptive stochastic inertia weight by the change of the global best fitness(WPSO).

The solution of the best optimal objective value and the worst optimal objective value of the standard form was studied.

A chaotic search method for global optimization on tent map;

Back propagation algorithm of neural network with global optimization;

Numerical results show that the present algorithm is a promising approach for solving global optimization problems.

An improved hybrid genetic algorithm(IHGA)composed of the self-adaptive genetic algorithm(SAGA)based on neighborhood searching and the SGA(standard genetic algorithm)can bring into play their advantages of time saving,high efficiency and mature convergence,as well as the global optimum searching capability of GA(genetic algorithm).

Virtually all control and decision problems can be cast as optimization problems ,while most of them exist multiple minima ,so it is always a focus to achieve global optimum by escaping local minimum both in academic and engineering fields.

The hybrid method is an effective approach to solve nonconvex optimization problems, because it inherits the merits of fast searching ability to locate global optimum in the chaos optimization method and the advantage of high convergence speed of the BFGS method.