Function optimization based on incremental resolution

Aiming at solving the problems of function optimization with interval constraints, a class of optimization techniques based on incremental resolution is proposed by combining with Monte Carlo uniform random sampling and chaos search respectively. The reasons of that these optimization techniques are more efficient than the corresponding common optimizers with invariant resolution are analyzed. Dealing with function optimization problems, they can achieve the compression of searching space and the location of optimal region rapidly. Simulation results validate their effectiveness.