一个多目标遗传算法的优劣主要看三个指标：解集收敛程度，解集分布度以及时间消耗。
The performance of an MOGA can be measured from three aspects: the convergence to the true Pareto optimal front, the diversity of solutions and the time consuming.
这种方法不能收敛于一个平衡结果，而且还产生一个附加问题，即分配结果很大程度上依赖于迭代次数。
This method does not converge to an equilibrium solution and has the additional problem that the results are highly dependent on the specific number of iterations run.
通过子结构间的力传递使子结构发生联系，从而降低了耦合程度，得到了收敛的数值解。
The boundary forces play an important part in linking up substructures. The coupling extent decreases and a convergent numerical solution is gotten.
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