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.

Study on local extremum of object function in mutual information-based image registration;

The difficulty of optimization caused by local extremum is the key problem of the algorithm.

It can not only overcome the disadvantage of easily getting into the local extremum in the later evolution period,but also keep the rapidity of the previous period.

Overcome of local maximum in mutual information-based image registration;

The cause of local maximum of object functions image registration was analyzed based on mutual information, and an optimization strategy by using simulated annealing-simplex method proposed.

That means local extrema of histograms are sensitive to ±1 embedding method.