期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 49, 期 4, 页码 1403-1416出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2802912
关键词
Adaptive framework; coordinate system; differential evolution (DE); nature-inspired optimization algorithms (NIOAs); particle swarm optimization (PSO)
类别
资金
- Innovation-Driven Plan in Central South University [2018CX010]
- National Natural Science Foundation of China [61673397, 61673331, 61672478]
- EU Horizon 2020 Marie Sklodowska-Curie Individual Fellowships [661327]
- Engineering and Physical Sciences Research Council of U. K. [EP/K001310/1]
- Hunan Provincial Natural Science Fund for Distinguished Young Scholars [2016JJ1018]
- Graduate Innovation Fund of Hunan Province of China [CX2017B062]
- EPSRC [EP/K001310/1] Funding Source: UKRI
- Marie Curie Actions (MSCA) [661327] Funding Source: Marie Curie Actions (MSCA)
The performance of many nature-inspired optimization algorithms (NIOAs) depends strongly on their implemented coordinate system. However, the commonly used coordinate system is fixed and not well suited for different function landscapes, NIOAs thus might not search efficiently. To overcome this shortcoming, in this paper we propose a framework, named ACoS, to adaptively tune the coordinate systems in NIOAs. In ACoS, an Eigen coordinate system is established by making use of the cumulative population distribution information, which can be obtained based on a covariance matrix adaptation strategy and an additional archiving mechanism. Since the population distribution information can reflect the features of the function landscape to some extent, NIOAs in the Eigen coordinate system have the capability to identify the modality of the function landscape. In addition, the Eigen coordinate system is coupled with the original coordinate system, and they are selected according to a probability vector. The probability vector aims to determine the selection ratio of each coordinate system for each individual, and is adaptively updated based on the collected information from the offspring. ACoS has been applied to two of the most popular paradigms of NIOAs, i.e., particle swarm optimization and differential evolution, for solving 30 test functions with 30D and 50D at the 2014 IEEE Congress on Evolutionary Computation. The experimental studies demonstrate its effectiveness.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据