A stochastic approach to global optimization of nonlinear programming problem with many equality constraints

摘要

Many practical problems often lead to large nonconvex nonlinear programming problems that have many equality constraints. The global optimization algorithms of these problems have received much attention over the last few years. Generally, stochastic algorithms are suitable for these problems, but not efficient when there are too many equality constraints. Therefore, a global optimization algorithm for solving these problems is proposed in this paper. The new algorithm, based on a feasible set strategy, uses a stochastic algorithm and a deterministic local algorithm. The convergence of the algorithm is analyzed. This algorithm is applied to practical problem, and the numerical results illustrate the accuracy and efficiency of the algorithm.