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Evolutionary approaches to solve three challenging engineering tasks

机译：解决三项艰巨工程任务的进化方法

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摘要

Three applications of evolutionary algorithms, namely the optimization of the nuclear core reload design, the synthesis of multi- layer optical coatings, and the synthesis of chemical engineering plants, are presented in this paper. The examples demonstrate the applicability of the evolutionary approach to solve complex real-world problems. These problems are often mixed-integer, variable- dimensional and multi-criteria optimization problems. Additionally, instead of an objective function, given in a closed form, complex simulation models are used as an objective function, thus prohibiting success by means of classical analysis. Although standard evolutionary algorithms are not able to solve the three complex tasks presented here, enhanced evolutionary algorithms (EAs) clearly demonstrate their potential to do so. Through the three examples, we show the necessary ingredients for tackling real-world optimization problems. In addition to adequate representations and appropriate evolutionary operators, one needs to integrate expert knowledge and heuristic operators to guide the search. Finally, parallel EAs are needed to improve the reliability and speed up of evolutionary algorithms.
机译：本文介绍了进化算法的三个应用，即核芯重载设计的优化，多层光学涂层的合成和化学工程工厂的合成。这些示例说明了进化方法解决复杂的现实世界问题的适用性。这些问题通常是混合整数，可变维和多准则优化问题。另外，代替以封闭形式给出的目标函数，还使用复杂的仿真模型作为目标函数，从而通过经典分析来阻止成功。尽管标准的进化算法无法解决此处介绍的三个复杂任务，但是增强型进化算法（EA）清楚地表明了其潜力。通过这三个示例，我们展示了解决实际优化问题的必要要素。除了适当的表示形式和适当的进化算子之外，还需要整合专家知识和启发式算子来指导搜索。最后，需要并行EA以提高可靠性并加快进化算法的速度。

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