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摘要:
Estimation of distribution algorithms are a class of evolutionary optimization algorithms based on probability distribution model. In this article, a Pareto-based multi-objective estimation of distribution algorithm with multivariate T-copulas is proposed. The algorithm employs Pareto-based approach and multivariate T-copulas to construct probability distribution model. To estimate joint distribution of the selected solutions, the correlation matrix of T-copula is firstly estimated by estimating Kendall’s tau and using the relationship of Kendall’s tau and correlation matrix. After the correlation matrix is estimated, the degree of freedom of T-copula is estimated by using the maximum likelihood method. Afterwards, the Monte Carte simulation is used to generate new individuals. An archive with maximum capacity is used to maintain the non-dominated solutions. The Pareto optimal solutions are selected from the archive on the basis of the diversity of the solutions, and the crowding-distance measure is used for the diversity measurement. The archive gets updated with the inclusion of the non-dominated solutions from the combined population and current archive, and the archive which exceeds the maximum capacity is cut using the diversity consideration. The proposed algorithm is applied to some well-known benchmark. The relative experimental results show that the algorithm has better performance and is effective.
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篇名 Estimation of Distribution Algorithm with Multivariate <i>T</i>-Copulas for Multi-Objective Optimization
来源期刊 智能控制与自动化(英文) 学科 医学
关键词 Estimation of Distribution Algorithm Pareto-Based Approach T-Copulas Multi-Objective Optimization
年,卷(期) 2013,(1) 所属期刊栏目
研究方向 页码范围 63-69
页数 7页 分类号 R73
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研究主题发展历程
节点文献
Estimation
of
Distribution
Algorithm
Pareto-Based
Approach
T-Copulas
Multi-Objective
Optimization
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
智能控制与自动化(英文)
季刊
2153-0653
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
250
总下载数(次)
0
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