CAJ | 학술논문

随着对象模型描述的系统性和完整性的提高，过程优化问题的复杂程度逐步增加，对优化算法的性能提出了更高的要求。现有的非线性规划算法在求解性能上各有优劣，本文提出了一种基于收敛深度控制的多元混合非线性规划算法，将各个非线性规划算法视为元算法，利用收敛深度来控制这些元算法之间的相互协作，更好地发挥元算法各自的优势，从而提高求解大规模复杂优化问题的能力。采用空分系统的数据校正问题以及脱丙烷塔和脱丁烷塔联塔系统的优化问题对多元混合算法进行了测试，数值结果表明相比各个单独的非线性规划算法而言，多元混合算法具有更好的求解性能。
수착대상모형묘술적계통성화완정성적제고，과정우화문제적복잡정도축보증가，대우화산법적성능제출료경고적요구。현유적비선성규화산법재구해성능상각유우렬，본문제출료일충기우수렴심도공제적다원혼합비선성규화산법，장각개비선성규화산법시위원산법，이용수렴심도래공제저사원산법지간적상호협작，경호지발휘원산법각자적우세，종이제고구해대규모복잡우화문제적능력。채용공분계통적수거교정문제이급탈병완탑화탈정완탑련탑계통적우화문제대다원혼합산법진행료측시，수치결과표명상비각개단독적비선성규화산법이언，다원혼합산법구유경호적구해성능。
With the improvement of systematicness and integrality of the object model description, the complexity of process optimization problem is increased and then higher requirements are put forward for the performance of optimization algorithm. The existing nonlinear programming algorithms have both merits and demerits in their solving performance. A convergence depth control based polynary hybrid nonlinear programming algorithm was proposed in this paper. Each nonlinear programming algorithm was regarded as a meta-algorithm. In order to take full advantage of each meta-algorithm, convergence depth was used to control interactive cooperation among them and then the ability for solving large-scale complex optimization problem could be enhanced. Data reconciliation problem of air separation system and optimization problem of depropanizer and debutanizer distillation column systems were taken to test the proposed algorithm. The numerical results showed that the solving ability of the polynary hybrid nonlinear programming algorithm was better than the single nonlinear programming algorithm.