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首页> 外文会议>Youth Academic Annual Conference of Chinese Association of Automation >Research on the surface machining plan of hull shell based on PSO optimized NSGA-II algorithm
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Research on the surface machining plan of hull shell based on PSO optimized NSGA-II algorithm

机译：基于PSO优化的NSGA-II算法的船体外壳表面加工计划研究

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The reasoning engine is the core component of the intelligent decision support system for the surface forming of the hull shell. It is composed of a series of processes of ship plate unfolding, case matching, model database support and scheme optimization, and finally the processing scheme is obtained. In the research of the model database, since the processing parameters corresponding to the same deformation amount are often not unique, the optimization goal is to reduce the production time of the hull outer plate surface forming and reduce the production energy consumption under the condition of sufficient ship plate deployment accuracy. User needs, looking for a processing plan that meets the needs of users from a variety of processing plans inferred. Therefore, this paper uses particle swarm optimization (PSO) to optimize non-dominant sorting genetic algorithm II (NSGA-II) with an elite strategy to select an appropriate optimization objective function to obtain the Pareto optimal solution set, and then filter out the best processing plan.
机译：推理引擎是智能决策支持系统的核心组件，用于船体壳的表面形成。它由一系列船板展开，案例匹配，模型数据库支持和方案优化组成，最后获得处理方案。在模型数据库的研究中，由于对应于相同变形量的处理参数通常不是唯一的，所以优化目标是减少船体外板表面的生产时间，并降低了足够的条件下的生产能耗。船板部署准确性。用户需求，寻找符合用户从各种处理计划的需求的处理计划推断。因此，本文使用粒子群优化（PSO）来优化非主导分类遗传算法II（NSGA-II），具有精英策略，以选择适当的优化目标函数以获得帕累托最佳解决方案集，然后滤除最佳加工计划。

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