Constraint Multi-objective Optimization based on Genetic Shuffled Frog Leaping Algorithm

Sun Lu-peng, Ma Ge

Abstract

To solve the convergence problem of the constrained multi-objective optimization, combining the advantages of genetic algorithm and shuffled frog leaping algorithm,a method based on genetic shuffled frog leaping algorithm. To use the genetic operators and the packet improved shuffled frog leaping algorithm and avoid falling into local optimal, accelerating the convergence speed. Experiments show that the improved algorithm is efficient and reasonable, can reduce the execution time of the multi-objective optimization problem, improve the quality of optimal solution.

References

  1. E. Zitzler, L. Thiele, ”Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach”, IEEE Transactions on Evolulionary Compution , vol.3,no.4, November 1999,257-271.
  2. H-P. Sehwefel, ”Kybernetische Evolution als Strategie der experimentellen Forschung in derStromungstechnik,” Diploma thesis, Technical University of Berlin,1965.
  3. K. Deb, Multi-Objective Optimizarion using Evolutional Algorithms. Chicester,UK:John Wiley&Sons,2001
  4. Eusuff M & K E Lansey. Optimization of water distribution network design using the shuffled frog leaping algorithm[J]. Water Resources Planning and Management,2003,129(3):210-225.
  5. Rahirni-Vahed, A., Mirzaei, A. H.. A hybrid multi-objective shuffled frog- leaping algorithm for a mixed-model assembly line sequencing Problem[J]. Computer & Industrial Engineering (2007), doi:10.1016/j.eie.z007.06.007
  6. Ziyang Zhen & Daobo Wang & Yuanyuan Liu. Improved Shuffled Frog Leaping Algorithm for Continuous optimization Problem[C].IEEE Congress on Evolutionary ComPutation ,2009:2992-2995
  7. Emadl Elbeltagi & Tarek Hegazy & Donald Grierson. Comparison among five evolutionary-based optimization algorithm[J].Advanced Engineering Informaties,2005,19(l):43- 53
  8. E. Zitzler, M. Laumanns, L.Thiele,”SPEA2:Improving the strength pareto evolutionary algorithm,” in Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Athens, Greece, 2002,95-100.
  9. 1. Sunlupeng (1970.3-), male, Henan Zhengzhou, Master Degree, lecturer, research direction of computer application.Email:slp2060@163.com
  10. 2. Mage (1983.1-), male, Henan Zhengzhou, Master Degree, lecturer, research direction of computer application.Email:mage0608@163.com
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Paper Citation


in Harvard Style

Lu-peng S. and Ge M. (2015). Constraint Multi-objective Optimization based on Genetic Shuffled Frog Leaping Algorithm . In Proceedings of the Information Science and Management Engineering III - Volume 1: ISME, ISBN 978-989-758-163-2, pages 94-99. DOI: 10.5220/0006019700940099


in Bibtex Style

@conference{isme15,
author={Sun Lu-peng and Ma Ge},
title={Constraint Multi-objective Optimization based on Genetic Shuffled Frog Leaping Algorithm},
booktitle={Proceedings of the Information Science and Management Engineering III - Volume 1: ISME,},
year={2015},
pages={94-99},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006019700940099},
isbn={978-989-758-163-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Information Science and Management Engineering III - Volume 1: ISME,
TI - Constraint Multi-objective Optimization based on Genetic Shuffled Frog Leaping Algorithm
SN - 978-989-758-163-2
AU - Lu-peng S.
AU - Ge M.
PY - 2015
SP - 94
EP - 99
DO - 10.5220/0006019700940099