A Variables Clustering Based Differential Evolution Algorithm to Solve Multistage Goal Programming Model in Defense Projects Portfolio

Yu Zhou; Yong Bin Li; Zhong Zheng Shi; Zheng Xin Li; Lei Zhang

doi:10.4028/www.scientific.net/AMR.1046.367

Paper Titles

A Time Synchronization Algorithm Based on Cluster-Tree
p.348

The Design of Embedded Ethernet System Based Microcontroller
p.352

Research on Information Processing with Dynamic Adaptive Job Scheduling Algorithm Based on Hadoop Platform
p.359

Research on Information Processing with Delay Fair Scheduling Algorithm Based on Resource Situation
p.363

A Variables Clustering Based Differential Evolution Algorithm to Solve Multistage Goal Programming Model in Defense Projects Portfolio
p.367

Chaotic Clonal Genetic Algorithm for Routing Optimization
p.371

Projection Method for Material Selection Problem with Triangular Fuzzy Numbers
p.375

Applied-Information Technology in Adaptive Kalman Filter with Model Error and Noise Error
p.380

Applied-Information Technology with Speech Enhancement Based on EMD and MF
p.384

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 1046A Variables Clustering Based Differential...

A Variables Clustering Based Differential Evolution Algorithm to Solve Multistage Goal Programming Model in Defense Projects Portfolio

Abstract:

The multistage goal programming model is popular to model the defense projects portfolio optimization problem in recent years. However, as its high-dimensional variables and large-scale solution space, the addressed model is hard to be solved in an acceptable time. To deal with this challenge, we propose an improved differential evolution algorithm which combines three novel strategies i.e. the variables clustering based evolution, the whole randomized parameters, and the child-individual based selection. The simulation results show that this algorithm has the fastest convergence and the best global searching capability in 6 test instances with different scales of solution space, compared with classical differential evolution algorithm (CDE), genetic algorithm (GA) and particle swarm optimization (PSO) algorithm.

You might also be interested in these eBooks

Advanced Development of Engineering Science IV

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 1046)

Pages:

367-370

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1046.367

Citation:

Cite this paper

Online since:

October 2014

Authors:

Yu Zhou*, Yong Bin Li, Zhong Zheng Shi, Zheng Xin Li, Lei Zhang

Keywords:

Differential Evolution, Intelligent Optimization, Multistage Programming, Variable Clustering

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] J. Bao, Y. Chen and J. Yu, Journal of Zhejiang University-SCIENCE C. Vol. 11(12), (2010), pp.939-947.

Google Scholar

[2] Y. -Y Chen and J. T. Lin, Expert Systems with Applications. Vol. 36, (2009), pp.12264-12271.

Google Scholar

[3] Z. Yang, X Li, C. P. Bowers , T. Schnier, K. Tang and X. Yao. IEEE Transactions On Systems, Man, and Cybernetics -Part C. Vol. 42(6), (2012). pp.957-969.

DOI: 10.1109/tsmcc.2011.2174983

Google Scholar

[4] V. Nha, S. Shin and S.H. Jeong. European Journal of Operational Research. Vol. 229, (2013), p.505–517.

Google Scholar

[5] K. V. Price, R. M. Storn and J. A. Lampinen. Berlin, Germany: Spring-Verlag Inc., (2005).

Google Scholar

[6] R. L. Haupt and S. E. Haupt, New Jersey, USA: John Wiley & Sons Inc., (2004).

Google Scholar

[7] R. Eberhart and J. Kennedy. The 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan (1995).

Google Scholar