A Projection Pursuit Dynamic Cluster Model Based on a Memetic Algorithm

Hongli Zhang; Cong Wang; Wenhui Fan

doi:10.1109/TST.2015.7350018

Tsinghua Science and Technology 2015, 20(6): 661-671 https://doi.org/10.1109/TST.2015.7350018

Open Access | Issue | Published: 17 December 2015

A Projection Pursuit Dynamic Cluster Model Based on a Memetic Algorithm

Show Author's Information Hide Author's Information Hongli Zhang(

), Cong Wang, Wenhui Fan

School of Electrical Engineering, Xinjiang University, Urumqi 830047, China.

Department of Automation, Tsinghua University, Beijing 100084, China.

Keywords:

particle swarm optimization, projection pursuit dynamic cluster, memetic algorithm, simulated annealing algorithm

Cite this article:

Zhang H, Wang C, Fan W. A Projection Pursuit Dynamic Cluster Model Based on a Memetic Algorithm. Tsinghua Science and Technology, 2015, 20(6): 661-671. https://doi.org/10.1109/TST.2015.7350018

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Abstract

A Projection Pursuit Dynamic Cluster (PPDC) model optimized by Memetic Algorithm (MA) was proposed to solve the practical problems of nonlinearity and high dimensions of sample data, which appear in the context of evaluation or prediction in complex systems. Projection pursuit theory was used to determine the optimal projection direction; then dynamic clusters and minimal total distance within clusters (min TDc) were used to build a PPDC model. 17 agronomic traits of 19 tomato varieties were evaluated by a PPDC model. The projection direction was optimized by Simulated Annealing (SA) algorithm, Particle Swarm Optimization (PSO), and MA. A PPDC model, based on an MA, avoids the problem of parameter calibration in Projection Pursuit Cluster (PPC) models. Its final results can be output directly, making the cluster results objective and definite. The calculation results show that a PPDC model based on an MA can solve the practical difficulties of nonlinearity and high dimensionality of sample data.

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About this article

A Projection Pursuit Dynamic Cluster Model Based on a Memetic Algorithm

Show Author's information Hide Author's Information Hongli Zhang(

), Cong Wang, Wenhui Fan

School of Electrical Engineering, Xinjiang University, Urumqi 830047, China.

Department of Automation, Tsinghua University, Beijing 100084, China.

Abstract

Keywords: particle swarm optimization, projection pursuit dynamic cluster, memetic algorithm, simulated annealing algorithm

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Publication history

Received: 09 November 2015

Accepted: 16 November 2015

Published: 17 December 2015

Issue date: December 2015

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (No. 51575469).