After finding out a discrete local minimal solution by using a genetic algorithm as a initial point,the algorithm tries to improve a discrete local minimal solution by minimizing a filled function.

With the definition of discrete local minimal solution of an integer programming problem, this paper presents a neighbourhood search algorithm for finding a discrete local minimal solution of the problem.

With the difinition of discrete local minimal solution of an integer programming problem,this paper presents a neighbourhood search algorithm for finding a discrete local minimal solution of the problem.

With the purpose of solving the local minima of standard Back-Propagation(BP) algorithm,a modified error function was proposed to escape from the local minima and converge to the global minima.

Some particles trapped in local minimums are relocated again by the designed selective behavior based on random mutation and help them break away from local minimums to find the global optimal solution rapidly.

The problem of trapping into the local minimum is solved, which is inherent with the learning algorithms-based on the BP principle by weight strategy.

On the other hand, the method in this paper can avoid the local minimum efficiently by adding the number of the particles.

Analysis of the traditional artificial potential field failed in path planning due to the local minimum and the possible local minimum area of the external obstacles on the geometry.