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首页> 外文期刊>International Journal of Bio-Inspired Computation >Hybrid multi-objective differential evolution (H-MODE) for optimisation of polyethylene terephthalate (PET) reactor
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Hybrid multi-objective differential evolution (H-MODE) for optimisation of polyethylene terephthalate (PET) reactor

机译:混合多目标差分进化(H-MODE)用于优化聚对苯二甲酸乙二醇酯(PET)反应器

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摘要

Multi-objective evolutionary algorithms (MOEAs) are used to solve the optimisation problems with more than one objective to be optimised simultaneously having conflict among each other. Due to the limitations of traditional deterministic algorithms tohandle complex and nonlinear search space, several EAs are developed in the recent past. The multi-objective differential evolution (MODE) algorithm is already tested and found to be a reliable algorithm due to their ability to handle non-linear problems efficiendy. Though MODE is accurate in terms of converging to the global Pareto front, traditional method has their advantage in terms of speed. We combined these two algorithms and developed hybrid strategy of MODE thus, achieving both accuracy and speed. Hybrid MODE (H-MODE) algorithm is applied on multi-objective optimisation of industrial wiped fdm polyethylene terephthalate reactor. The results of the present study are compared with those obtained using MODE algorithm. Smooth and well diverse Pareto optimal front is observed with a much faster speed using H-MODE.
机译:多目标进化算法(MOEA)用于解决优化问题,其中一个以上的目标同时相互冲突而被优化。由于传统确定性算法在处理复杂和非线性搜索空间方面的局限性,最近开发了几种EA。多目标差分进化(MODE)算法已经过测试,由于其能够有效处理非线性问题,因此是一种可靠的算法。尽管MODE在收敛到全球Pareto前沿方面是准确的,但传统方法在速度方面具有优势。我们结合了这两种算法,并开发了MODE的混合策略,从而实现了准确性和速度。混合模式(H-MODE)算法应用于工业刮水式fdm聚对苯二甲酸乙二酯反应器的多目标优化。将本研究的结果与使用MODE算法获得的结果进行比较。使用H-MODE可以以更快的速度观察到平滑且多样化的帕累托最优前端。

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