The results of several simulations for different high dimension benchmark functions illustrate that the proposed algorithm has both better ability of global exploration and local exploitation, and the optimal precision is effectively improved under the condition of guaranteeing the convergence of PSO algorithm.

The result of research shows that the DABP algorithm can enhance the speed of convergence,the calculation precision,the ability of global search and the forecast ability of the network.

The results show that compared with the traditional ACA,the improved ACA has more global search capability and robu.

On the one hand,the hybrid algorithm provides global search(reliability) by means of the promotion of high levels of population diversity.

An optimization combination genetic algorithm based on the dual operator is developed in order to remedy the influence on the variety of population by combining other operator with better global searching ability.

A modified particle swarm optimization (PSO) algorithm was adopted to optimize functions, which overcame the shortcoming of converging to local optimum for PSO algorithm, increased the converging rate and achieve the global searching.

In order to improve the searching precision and the global searching ability of evolutionary strategy(ES),this paper proposed an adaptive evolutionary strategy based on the feedback and sharing mechanism(ESBFSM),in which the information of every generation\'s current optimal searching result and sharing degree was fed into the mutating formula.