報告題目：Evolutionary Multimodal Optimization: Decision-Making and Visualization
報 告 人：Dr. Ran Cheng（程然） Research Fellow
單 位：英國伯明翰大學(xué)計算智能與應(yīng)用研發(fā)中心
報告時間：2017年11月15日（周三）上午8：30
報告地點：逸夫樓408會議室
報告摘要：
Multimodal optimization generally refers to single-objective optimization involving multiple optimal (or near-optimal) solutions. Thanks to the population based nature of evolutionary algorithms, which are able to obtain a set of candidate solutions in a single run, the evolutionary multimodal optimization has attracted increasing interest recently. In this talk, I will introduce our recent research in evolutionary multimodal optimization. On one hand, I will present how to perform decision-makings in multimodal optimization using evolutionary multiobjective optimization based techniques. On the other hand, I will introduce a visualization method for benchmark studies of multimodal optimization.
報告題目：Evolutionary Many-Objective Optimisation: Pushing the Boundaries
報 告 人：Dr. Miqing Li（李密青） Research Fellow
單 位：英國伯明翰大學(xué)計算智能與應(yīng)用研發(fā)中心
報告時間：2017年11月15日（周三）上午9：15
報告地點：逸夫樓408會議室
報告摘要：
Many-objective optimisation refers to a class of optimisation problems that have more than three objectives. The last decade has witnessed the emergence of many-objective optimisation as a booming topic in a wide range of complex modern real-world scenarios. However, in contrast to conventional multi-objective optimisation which involves two or three objectives, many-objective optimisation poses far great challenges to the area of nature-inspired search algorithms. In this talk, I will introduce several pieces of our work in solving the challenges from perspectives of algorithm design, performance assessment, test problem construction and visualisation in many-objective optimisation. In particular, I will present a simple but effective method to make Pareto-based algorithm well suited to many-objective optimisation and then a test problem suite to aid the visual examination of many-objective optimisers.
報告題目：Multi-objective evolutionary algorithms for solving complex optimization problems
報 告 人：張興義 教授、博士生導(dǎo)師
單 位：安徽大學(xué)計算機科學(xué)與技術(shù)學(xué)院生物智能與知識發(fā)現(xiàn)研究所
報告時間：2017年11月15日（周三）上午10：00
報告地點：逸夫樓408會議室
報告摘要：
Multi-objective evolutionary algorithms have been verified to be a useful technology for solving optimization problems during the last two decades, however, much work still deserves further investigations when addressing complex optimization tasks. In this talk, I will first briefly introduce the multi-objective evolutionary algorithms, and then mainly focus on threemulti-objective evolutionary algorithms recently suggested by us to tackle complex optimization problems. The three works included in this presentation are: 1) a knee point driven evolutionary algorithm for many-objective optimization problems, 2) a decision variable clustering based evolutionary algorithm for large-scale optimization problems, and 3) a multi-objective evolutionary algorithm for task-oriented pattern mining task.
報告題目：“分而治之”的協(xié)同演化策略在大規(guī)模優(yōu)化中的應(yīng)用
報 告 人：楊鳴 副教授 碩士生導(dǎo)師
單 位： 中國地質(zhì)大學(xué)計算機學(xué)院
報告時間：2017年11月15日（周三）上午10：45
報告地點： 逸夫樓408會議室
報告摘要：
大規(guī)模優(yōu)化是大數(shù)據(jù)優(yōu)化的重要組成部分，也是當(dāng)今優(yōu)化研究領(lǐng)域的難點和前沿。協(xié)同演化（Cooperative Co-evolution）算法將問題分解為若干個子問題，并分別對每個子問題進行優(yōu)化求解。這種“分而治之”的優(yōu)化策略降低了問題的求解難度。本報告主要講解如何將大規(guī)模優(yōu)化問題中的變量進行分組及如何對每個子問題進行優(yōu)化求解。
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