潜水器多学科设计中的多目标协同优化方法

针对潜水器设计中涉及多个学科的耦合以及数据信息量大、数据关系复杂的问题,文章介绍了一种新的多目标协同优化算法.该方法将Pareto遗传算法(PGA)引入协同优化框架,二者的有机结合充分发挥各自的优势,该方法利用协同方法的分解协调机制将复杂系统的设计问题分解为一个系统级优化问题和几个学科级优化问题.采用PGA作为系统级优化器,不仅可以得到能够反映多目标优化问题实质的、客观的Pareto解集,而且,由于PGA是无需梯度信息的直接搜索算法,从而从根本上消除了协同优化由系统层一致性约束条件引起的收敛困难问题.在PGA与协同优化框架结合的过程中,采用目标函数的归一化处理、分级罚函数法、浮点数编码、群体分级和Paveto解集过滤器等技术提高算法的计算效率和可靠性,并通过一个数学算例和一个载人潜水器的例子证明了多目标协同优化算法的有效性.

Multi-objective collaborative optimization in multidisciplinary design for submersible

In order to handle the multidisciplinary nature and large quantities of data and complex relations of databases encountered in the submersible design,a novel integration of Pareto Genetic Algorithm (PGA),one of the multi-objective optimization methods within the collaborative optimization framework,which remain the main metrics of CO architecture and ability of PGA to seeking non-inferior solution set,is introduced in this paper.Introduction of PGA which is a direct search algorithm to CO can relieve the convergence difficulties in system-level.At the same time,the PGA enables the designer to select the fittest solution among the Pareto optimal set in according with their preference and the nature of the design problem. Some strategies are used,such as regularization of objectives,graded penalized function technique to remove constraints,float code,Pareto rank of population and Pareto set filter of objective in the integration of PGA within CO.The proposed framework is tested by a mathematical example and implemented to conduct the conceptual design of a Human Occupied Vehicle (HOV) that includes four subspace designs: hydrodynamics,structure,propulsion,weight and volume.