An Improved Ant Colony Optimization Applied to Attributes Reduction

Deng, Tingquan; Yang, Chengdong; Zhang, Yue-tong; Wang, Xinxia

doi:10.1007/978-3-540-88914-4_1

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Cited by 9 publications

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“…The preliminary version of some of this work appeared in [17]. The authors would like to thank the reviewers for their valuable suggestions for improving this paper.…”

Section: Future Workmentioning

confidence: 97%

An Improved Approach to Attribute Reduction with Ant Colony Optimization

Deng

Wang²,

Zhang

et al. 2010

Fuzzy Information and Engineering

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Attribute reduction problem (ARP) in rough set theory (RST) is an NPhard one, which is difficult to be solved via traditionally analytical methods. In this paper, we propose an improved approach to ARP based on ant colony optimization (ACO) algorithm, named the improved ant colony optimization (IACO). In IACO, a new state transition probability formula and a new pheromone traps updating formula are developed in view of the differences between a traveling salesman problem and ARP. The experimental results demonstrate that IACO outperforms classical ACO as well as particle swarm optimization used for attribute reduction.

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“…The preliminary version of some of this work appeared in [17]. The authors would like to thank the reviewers for their valuable suggestions for improving this paper.…”

Section: Future Workmentioning

confidence: 97%