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學術報告:Symposium on Clustering and Prediction Intelligent Algorithm

審核發布:數學與信息學院 來源單位及審核人: 發布時間:2021-05-20瀏覽次數:38


報告一A Development of Interpretable Rule-Based Architecture Under Privacy Constraints: A Framework of Granular Computing

報告人 Prof. Witold PedryczInformation Science主編

單位阿爾伯塔大學(加拿大)

摘要In data analytics, system modeling, and decision-making models, the aspects of interpretability and explainability are of paramount relevance as emphasized in studies on explainable Artificial Intelligence (XAI). Those requirements are especially timely when the design of models has to be realized when considering strict requirements of privacy and security. We advocate that to efficiently address these challenges, it becomes beneficial to engage the fundamental framework of Granular Computing. It is demonstrated that a conceptualization of information granules can be conveniently carried out with the use of information granules (for example, fuzzy sets, sets, rough sets, and alike). We present a comprehensive discussion of information granules-oriented design of rule-based architectures and their interpretation by engaging an innovative mechanism of federated unsupervised learning using which information granules are constructed.

 

報告二:Objective-Domain Dual Decomposition: An Effective Approach to Optimizing Partially Differentiable Objective Functions

報告人張曉明教授IEEE Fellow           單位:香港浸會大學

摘要This paper addresses a class of optimization problems in which either part of the objective function is differentiable while the rest is nondifferentiable or the objective function is differentiable in only part of the domain. Accordingly, we propose a dual-decomposition-based approach that includes both objective decomposition and domain decomposition. In the former, the original objective function is decomposed into several relatively simple subobjectives to isolate the nondifferentiable part of the objective function, and the problem is consequently formulated as a multiobjective optimization problem (MOP). In the latter decomposition, we decompose the domain into two subdomains, that is, the differentiable and nondifferentiable domains, to isolate the nondifferentiable domain of the nondifferentiable subobjective. Subsequently, the problem can be optimized with different schemes in the different subdomains. We propose a population-based optimization algorithm, called the simulated water-stream algorithm (SWA), for solving this MOP. The SWA is inspired by the natural phenomenon of water streams moving toward a basin, which is analogous to the process of searching for the minimal solutions of an optimization problem. The proposed SWA combines the deterministic search and heuristic search in a single framework. Experiments show that the SWA yields promising results compared with its existing counterparts.

 

報告三:Evolutionary Mult-objective and Many-objective Optimization Algorithm Using Region Decomposition

報告人海林教授           單位:廣東工業大學

摘要:該報告主要介紹在目標空間區域分解的思想,以及基于區域分解的進化多目標算法框架。給出幾種在該框架下設計的一些進化多目標和超多目標優化算法,提出了不均衡多目標最優化問題,并從理論上分析了基于區域分解的進化多目標算法在處理這類問題時的優勢。

 

報告四Design Automation of Intelligent Robotic Systems Based on Evolutionary Computation

報告人:范衠教授             單位:汕頭大學

摘要The main reason why the performance of domestic robots is generally difficult to reach the same level of that of foreign countries mainly lies in the lack of systematic continuous optimization and design automation. How to form a framework of design automation of intelligent robotic systems is the main topic of this report. This report will mainly focus on multi-angle modeling methods of intelligent robotic systems, solving intelligent robot optimization problems by combining constrained multi-objective evolutionary algorithms and machine learning methods, and applying design automation methods to develop intelligent robots.

 

報告五:復雜網絡建模、認知與優化

報告人:劉靜教授(萬人計劃)             單位西安電子科技大學

摘要:復雜網絡已成為研究復雜系統的有力工具。網絡化系統往往是大型或巨型的復雜系統,需要處理海量信息。而要控制進而利用網絡化系統首先要對其進行建模——構建出網絡以刻畫組成部分間的作用關系,進而對其特性進行認知——學習網絡本質特性、結構特點、演化趨勢、各種調整可能帶來的結果等,進而才能對其進行優化——根據需要對復雜系統進行設計、控制、調整。本報告對復雜網絡的建模、認知、優化進行全鏈條的介紹,并介紹基于進化算法的復雜網絡建模、認知、優化方法。

 

報告六:基于集成學習預測的動態多目標優化方法及應用

報告人:王峰教授            單位武漢大學

摘要:動態多目標優化是目前多目標優化領域的一個研究熱點,目前研究最為廣泛的一種思路是通過預測策略指導算法搜索找到最優解。為了更好的應對不同類型的環境變化,克服單一預測模型在不同類型環境變化下的不穩定性,提出了一種基于集成學習的預測策略(Ensemble Learning-based Prediction Strategy, ELPS)幫助算法重初始化種群以適應新的環境。在ELPS中選取四種不同類型的預測模型作為基模型,包括基于種群的自回歸模型、線性預測模型、基于拐點的自回歸模型及隨機初始化模型。當環境發生改變時,ELPS可以通過對歷史種群進行訓練得到一個強預測模型,并重新生成新種群,實現對環境變化的動態響應。通過ELPS的訓練,可以有效提升預測的準確性并提高種群的多樣性。為了進一步驗證算法在實際問題的性能,選取帶時間窗口的多目標動態車輛路徑規劃問題進行研究,進一步提出了一個基于ELPS的用以求解帶時間窗口的多目標動態車輛路徑規劃問題的算法,選取了隨機初始化策略模型、遷入模型和基于種群的預測模型作為基模型,可以實現對環境不同程度的波動的快速響應,加速種群的收斂速度。

 

報告七:Role-based Cooperation in Swarm Intelligence

報告人:夏學文教授          單位閩南師范大學

摘要It is a common phenomenon in human society that allocating different tasks to different people according to their capabilities (or roles). Many studies also verify that assigning different search strategies for different individuals can yield very favorable performance. Furthermore, multi-swarm techniques have been successfully applied in swarm intelligence algorithms (SI) since they can yield very pleasurable performance in keeping population diversity. Inspired by these observations, we integrate multiple roles into SI. In the proposed strategy, the entire population is split into multiple sub-swarms. During the evolutionary process, individuals in each sub-swarm adaptively select their own breeding strategies based on their own roles. Furthermore, the population is regrouped during the evolution process. Thus, different individuals in the same sub-swarm play different roles in a generation. Moreover, an individual may play different roles in different generations, Even a same individual may play different roles in different sub-swarms. Although the role-based cooperation offers a competitive performance testified by the extensive experiments, there are a few problems need to be further studied in future work. The one is the efficiency and effectiveness of the cooperation between different roles need to be in-depth analyzed. The other one is when and how to adjust the population size of different roles to satisfy various fitness landscapes and different evolution stages.

 

報告八:深度強化學習求解組合優化問題

報告人王甲海教授           單位:中山大學

摘要智能優化算法(啟發式或者元啟發式)的啟發式規則需人工設計。如啟發式規則能夠通過機器學習方法,特別端到端的深度學習方法,來自動學習得到,就減少了人為設計啟發式規則(算法)的工作量。這個習得的啟發式算法,表示為一個訓練好了的模型,可實現一次(離線)訓練多次(在線)求解,針對沒見過的問題實例,模型快速生成一個優質解,不需像傳統智能優化算法那樣,面對每一個問題的不同算例,都從初始解開始不斷迭代尋優。講座介紹深度強化學習求解組合優化問題的基本技術和方法。


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