We proposed a novel stochastic search method, called Greedy Diffusion Search (GDS). It holds the ability to escape from local minima for multi modal problems.

Combining GDS with limited memory BFGS, we propose a hybrid global optimization method to solve constrained optimization problems.

To evaluate the effectiveness of the proposed algorithm, some benchmark problems as well as some examples from the literature are tested. The outcomes are highly satisfactory.