.png){kind=icon}
返回
Expert-guided evolutionary algorithm for layout design of complex space stations
.png){kind=icon}
PRE
AI
DOI：10.1080/17517575.2016.1150521.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The layout of a space station should be designed in such a way that different equipment and instruments are placed for the station as a whole to achieve the best overall performance. The station layout design is a typical nondeterministic polynomial problem. In particular, how to manage the design complexity to achieve an acceptable solution within a reasonable timeframe poses a great challenge. In this article, a new evolutionary algorithm has been proposed to meet such a challenge. It is called as the expert-guided evolutionary algorithm with a tree-like structure decomposition (EGEA-TSD). Two innovations in EGEA-TSD are (i) to deal with the design complexity, the entire design space is divided into subspaces with a tree-like structure; it reduces the computation and facilitates experts' involvement in the solving process. (ii) A human-intervention interface is developed to allow experts' involvement in avoiding local optimums and accelerating convergence. To validate the proposed algorithm, the layout design of one-space station is formulated as a multi-disciplinary design problem, the developed algorithm is programmed and executed, and the result is compared with those from other two algorithms; it has illustrated the superior performance of the proposed EGEA-TSD.
Keyword:
Industrial informatics
multidisciplinary design optimisation
expert-guided evolutionary algorithms (EGEA)
space station design
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
2.8K
被引数:
1.8K
机构
引用论文
In Silico Analysis of Physical-Chemical Properties, Target Potential, and Toxicology of Pure Compounds from Natural Products天然产物中纯化合物的物理化学性质、靶点潜力及毒理学在计算机上的分析
OAAI
OAAI分享收藏A human-computer cooperative particle swarm optimization based immune algorithm for layout design.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容