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首页> 外文会议>IEEE Congress on Evolutionary Computation >Crude Oil Distillation Optimization Using Surrogate-aided Constrained Evolutionary Optimization
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Crude Oil Distillation Optimization Using Surrogate-aided Constrained Evolutionary Optimization

机译：基于替代辅助约束进化优化的原油蒸馏优化

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摘要

Evolutionary optimization of a crude oil distillation operation is a time-consuming task. Therefore, this paper proposes a surrogate-aided constrained evolutionary optimization algorithm (SACEO), in which surrogate models' establishment and management are combined to search for an optimal result. By establishing global and local surrogate models with the goal of maximizing profit, an adaptive constrained optimizer is developed for the global and local surrogate models' infilling operations and optimization searching. Thus, time-consuming strict mechanism models can gradually be approximated by continuously updating the surrogate models until the optimized results are obtained. The optimization results obtained for benchmark systems and the crude oil distillation system indicate that SACEO is similar to other constrained optimization algorithms in terms of its optimization accuracy and stability, while the number of evaluations of time-consuming models can be considerably reduced. Thus, the economic efficiency of crude oil distillation processes can be improved while satisfying the production conditions.
机译：原油蒸馏操作的进化优化是一项耗时的任务。因此，本文提出了一种代理辅助的约束进化优化算法（SACEO），该算法结合了代理模型的建立和管理，以寻求最优结果。通过建立以最大化利润为目标的全局和局部代理模型，为全局和局部代理模型的填充操作和优化搜索开发了一个自适应约束优化器。因此，可以通过不断更新代理模型直到获得优化结果来逐渐近似耗时的严格机制模型。从基准系统和原油蒸馏系统获得的优化结果表明，SACEO在优化精度和稳定性方面与其他约束优化算法相似，同时可以大大减少耗时模型的评估次数。因此，可以在满足生产条件的同时提高原油蒸馏工艺的经济效率。

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