A Global Convergence Algorithm with Stochastic Search for Constrained Optimization Problems

In this paper we investigate the global optimization problems with bounded variables and linear equality constraints. We suggest an approach to drawing sample points randomly from the feasible region. A new population-based global optimization algorithm is proposed. We also show that the algorithm converges to the global optimal solution with probability one. The method is easily extended to global optimization problems with general constraints.