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A New Optimization Paradigm to Solve High-dimensional Expensive Problems
瀏覽次數:日期:2021-01-12編輯:信科院 科研辦

報告人:Mengchu Zhou,美國國家發明家科學院院士、IEEE Fellow、IFAC Fellow、AAAS Fellow、CAA Fellow,美國新澤西理工學院杰出教授。

報告時間:2021年1月13日 (星期三) 上午10:00 - 11:30

報告地點:Zoom在線會議

https://us02web.zoom.us/j/2810019605?pwd=S09LNnl5dHdXajZBbEJJOVd4TVlmUT09

Meeting ID: 281 001 9605

Passcode: HNU2020 

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報告摘要:High-dimensional computationally expensive problems (HEPs) in which a single fitness evaluation consumes hours or even days have attracted increasing attention from both academia and industry. Exponentially expanding search space and complex landscape make HEPs extremely challenging to be solved by traditional algorithms with limited computational resources. Therefore, an Autoencoder-embedded Evolutionary Optimization (AEO) framework is invented to deal with them. To be specific, high-dimensional search space can be compressed to informative low-dimensional space by using an autoencoder as a dimension reduction tool. The search operation conducted in this low-dimensional space facilitates the population in convergence towards the optima. To balance the exploration and exploitation ability during optimization, two sub-populations are adopted to coevolve in a distributed fashion, wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary process. Dynamic information exchange is conducted between them after each cycle to promote sub-population diversity. Moreover, surrogate models can be incorporated into AEO (SAEO) to further boost its performance by reducing unnecessary fitness evaluation. Both AEO and SAEO are validated by testing benchmark functions with dimensions varying from 30 to 200. Compared with the state-of-the-art algorithms for HEPs, AEO shows extraordinarily high efficiency for these challenging problems while SAEO can greatly improve the performance of AEO in most cases, thus opening new directions for various evolutionary algorithms under AEO to tackle HEPs and greatly advancing the field of high-dimensional computationally expensive optimization.



報告人簡介:周孟初教授是美國新澤西理工學院的杰出教授,1995年獲終身教職?,F為美國國家發明家科學院院士、IEEE Fellow、IFAC Fellow(國際自動控制聯合會會士)、AAAS Fellow(美國科學促進會會士)、CAA Fellow(中國自動化學會會士)。自1990年起在新澤西理工大學電氣與計算機工程系任教,從事Petri網理論與工程應用,智能自動化、工業4.0、物聯網、人工智能、大數據分析、云服務計算、邊緣計算等方面的研究。周教授總共發表了900余篇期刊、會議論文,其中包括12本專著,450余篇IEEE Trans. 文,28項國際專利。在十年的時間里得到了美國國家科學基金(NSF)、國防部、美國國家標準技術院、國家航空航天署及新澤州科委等政府部門以及工程基金會及十多家公司的一千二百多萬美元的研究資助,主持并參與了五十多個研究課題。周博士獲得了許多嘉獎,主要有1994年美國制造工程師協會頒發的“計算機集成制造系統大學領先獎”; 2000年德國洪堡基金會的美國資深科學家洪堡研究獎; 2010年IEEE Systems, Man and Cybernetics學會年會的Franklin V. Taylor 最佳論文獎; 2015IEEE Systems, Man and Cybernetics學會的Norbert Wiener;2019年新澤西理工學院卓越研究勛章; 2020年新澤西研究與開發委員會愛迪生發明獎。周博士高被引作者并于 2012 列在工程領域的高被引作者第一名。


邀請人:李肯立


聯系人:陳建國



 

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