Improved multidisciplinary collaborative optimization with global stability

To solve the problems that the collaborative optimization results were sensitive to the initial points and usually converged to the local extremes, a new collaborative optimization method with global stability was presented. Two-phase optimization strategy was adopted in this method. In the global optimization phase, a bigger slack factor was employed in system-level optimization to reach the neighborhood of global extreme while ensuring the requirements of interdisciplinary consistency. In the local optimization phase, the slack factor was reduced gradually and the interdisciplinary compatibility was strengthened so as to reach to the global extreme. Two typical examples were adopted to test this optimization method. The results showed that optimization result was not influenced by initial point, the global stability of collaborative optimization was improved. Meanwhile, the presented approach had satisfactory feasibility and convergence speed.