A Self-Adaptive Cauchy Evolutionary Programming to Solve Vessel Loading Problem

CHENG Wenjuan; XIONG Yan; LIANG Xiao

Article Contents

Article Navigation > Chinese Journal of Electronics > 2013 > 22(1): 51-54

CHENG Wenjuan, XIONG Yan, LIANG Xiao. A Self-Adaptive Cauchy Evolutionary Programming to Solve Vessel Loading Problem[J]. Chinese Journal of Electronics, 2013, 22(1): 51-54.

Citation:

CHENG Wenjuan, XIONG Yan, LIANG Xiao. A Self-Adaptive Cauchy Evolutionary Programming to Solve Vessel Loading Problem[J]. Chinese Journal of Electronics, 2013, 22(1): 51-54.

CHENG Wenjuan, XIONG Yan, LIANG Xiao. A Self-Adaptive Cauchy Evolutionary Programming to Solve Vessel Loading Problem[J]. Chinese Journal of Electronics, 2013, 22(1): 51-54.

Citation:

CHENG Wenjuan, XIONG Yan, LIANG Xiao. A Self-Adaptive Cauchy Evolutionary Programming to Solve Vessel Loading Problem[J]. Chinese Journal of Electronics, 2013, 22(1): 51-54.

PDF( 312 KB)

A Self-Adaptive Cauchy Evolutionary Programming to Solve Vessel Loading Problem

1.
School of Computer and Information, Hefei University of Technology, Hefei 230009, China;
2.
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China;
3.
Dispatching and Communication Center, Anhui Electric Power Corporation, Hefei 230022, China

Funds: This work is supported in part by the National Natural Science Foundation of China (No.51274078, No.61170233, No.61232018, No.61272472, No.61202404, No.61272317) and China Postdoctoral Science Foundation (No.2011M501060).

Received Date: 2012-08-01
Rev Recd Date: 2012-12-01
Publish Date: 2013-01-05

Abstract

Abstract

A self-adaptive Cauchy evolutionary programming (ACEP) is proposed to solve vessel loading problem. The self-adaptive parameter r of Cauchy mutation is used to change the search step in time and the character of local period parameters k. The analysis on the search step, Markov chain, the operations of selection and competition and the empirical experiments of ACEP in solving the vessel loading problem are carried out. The results show that ACEP can outperform the fast evolutionary programming.
- Self-adaptive Cauchy evolutionary programming,
- Markov chain,
- Fast evolutionary programming

FullText(HTML)

References(10)

References

Y. Tsujimura, M. Gen, “Genetic algorithms for solving multiprocessorscheduling problems”, SEAL 1996, pp.106-115, 1996.

K.A. Folly, G.K. Venayagamoorthy, “Effects of learning rate onthe performance of the population based incremental learningalgorithm”, Proceedings of the 2009 International Joint Conferenceon Neural Networks, Atlanta, USA, pp.861-868, 2009.

C.Y. Lee, X. Yao, “Evolutionary programming using mutationsbased on Levy probability distribution”, IEEE Trans on EvolutionaryComputation, Vol.8, No.5, pp.1-13, 2004.

L.J. Fogel, A.J. Owens, M.J. Walsh, Artificial Intelligencethrough Simulated Evolution, Wiley, New York, USA, 1996.

H.P. Schwefel, Evolution and Optimum Seeking, Wiley, NewYork, USA, 1995.

Xin Yao, Yong Liu, Guangming Lin, “Evolutionary programmingmade faster”, IEEE Transactions on Evolutionary Computation,Vol.3, No.2, pp.82-102, 1999.

Yong Liu, Xin Yao, “How to control search step size in fastevolutionary programming”, Proceedings of the 2002 IEEECongress on Evolutionary Computation, IEEE Press, USA,pp.652-656, 2002.

Liang Xiao, Yue Lihua, Xiong Yan, Cheng Wenjuan and LiuShichen, “On the analysis of evolutionary programming withself-adaptive Cauchy operation”, Chinese Journal of Electronics,Vol.21, No.2, pp.309-312, 2012.

Xiong Yan, Liang Xiao, Miao Fuyou, “A novel pauli evolutionaryquantum algorithm for combinatorial optimization”, ChineseJournal of Electronics, Vol.19, No.3, pp.399-402, 2010.

Liu Feng, Liu Zuizhong, Zhang Zhuosheng, “Markov processanalyses and convergences of evolutionary programming”, ActaElectronica Sinica, Vol.26, No.8, pp.76-79, 1998. (in Chinese)

Relative Articles

Supplements(0)

Cited By

Proportional views