项目名称: 动态环境下基于聚类的自学习多种群算法研究
项目编号: No.61203306
项目类型: 青年科学基金项目
立项/批准年度: 2013
项目学科: 自动化学科
项目作者: 李长河
作者单位: 中国地质大学(武汉)
项目金额: 26万元
中文摘要: 现实中很多优化问题是变化的,而且变化是很难或者不能被检测到的。如何合理地保持种群多样性和处理环境的变化是演化计算求解这类动态优化问题面临的两个挑战。为了解决以上挑战,本项目将设计一个基于聚类的自学习多种群算法框架。该算法框架利用演化过程中种群的反馈信息,推断出与环境匹配的个体数量,并用聚类的办法生成子种群,从而能在变化的环境中合理地保持种群的多样性。本项目首次利用层次聚类方法生成多个种群,可以有效地解决多种群方法中尚未深入讨论和解决的困难,比如怎样定义种群的数量及如何产生每个种群等。另外,该算法框架仅利用种群自身的反馈信息来维持种群的多样性,不依赖于环境变化的检测。它适用于变化很难或者不能被检测到的动态环境,这是目前大部分优化算法不能做到的。因此,该研究可以为基于多种群的演化算法求解动态优化问题提供一个有效的通用平台,对推动理论研究的实际应用具有重要意义。
中文关键词: 动态优化;多种群;粒子群优化;;
英文摘要: Many real world optimization problems are changing overtime, and changes are difficult or impossible to be detected. Evolutionary computation has been subjected to two fundamental challenges when solving dynamic optimization problems (DOPs). They are how to maintain the social diversity and how to handle the dynamism, respectively. In order to solve the two issues, this proposal proposes a general framework of self-learning multi-population methods using clustering in dynamic environments. To maintain the population diversity in changing environments, this framework employs some feedback information obtained from the whole populations to estimate a proper number of individuals needed. A hierarchical clustering method is used for the first time to create multiple populations to solve DOPs. This framework provides an efficient way to solve some difficult issues which have never been discussed in the literature of multi-population methods for DOPs, e.g., how to define search areas of sub-populations, how to define the number of sub-populations, and how to create sub-populations. Because only feedback from populations themselves is used to maintain the population diversity, this framework can be used in dynamic environments with any change types, including the environments where changes are hard or impossible to be
英文关键词: Dynamic optimization;multi-population;particle swarm optimization;;