research-article

Differential evolution using mutation strategy with adaptive greediness degree control

Authors:

Meng WanAuthors Info & Claims

GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 73 - 80

https://doi.org/10.1145/2576768.2598236

Published: 12 July 2014 Publication History

Abstract

Differential evolution (DE) has been demonstrated to be one of the most promising evolutionary algorithms (EAs) for global numerical optimization. DE mainly differs from other EAs in that it employs difference of the parameter vectors in mutation operator to search the objective function landscape. Therefore, the performance of a DE algorithm largely depends on the design of its mutation strategy. In this paper, we propose a new kind of DE mutation strategies whose greediness degree can be adaptively adjusted. The proposed mutation strategies utilize the information of top t solutions in the current population. Such a greedy strategy is beneficial to fast convergence performance. In order to adapt the degree of greediness to fit for different optimization scenarios, the parameter t is adjusted in each generation of the algorithm by an adaptive control scheme. This way, the convergence performance and the robustness of the algorithm can be enhanced at the same time. To evaluate the effectiveness of the proposed adaptive greedy mutation strategies, the approach is applied to original DE algorithms, as well as DE algorithms with parameter adaptation. Experimental results indicate that the proposed adaptive greedy mutation strategies yield significant performance improvement for most of cases studied.

References

[1]

R. M. Storn and K. V. Price, "Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces," TR-95-012, 1995 {Online}. Available: http://icsi.berkeley.edu/~storn/litera.html.

[2]

R. M. Storn and K. V. Price, "Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces," J. Global Optim., vol. 11, pp. 341--359, 1997.

Digital Library

[3]

K. V. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization. Berlin, Germany: Springer-Verlag, 2005.

Digital Library

[4]

K. V. Price, "An introduction to differential evolution," in New Ideas in Optimization, D. Corne, M. Dorigo, and V. Glover, Eds. London, U.K.: McGraw-Hill, 1999, pp. 79--108.

Digital Library

[5]

S. Das and P. N. Suganthan, "Differential evolution: a survey of the state-of-the-art," IEEE Trans Evol. Comput., vol. 15, no. 1, pp. 4--31, Feb. 2011.

Digital Library

[6]

F. Neri and V. Tirronen, "Recent advances in differential evolution: A survey and experimental analysis," Artif. Intell. Rev., vol. 33, no. 1/2, pp. 61--106, Feb. 2010.

Digital Library

[7]

N. Noman and H. Iba, "Accelerating differential evolution using an adaptive local search," IEEE Trans. Evol. Comput., vol. 12, no. 1, pp. 107--125, Feb. 2008.

Digital Library

[8]

D. K. Tasoulis, V. P. Plagianakos, and M. N. Vrahatis, "Clustering in evolutionary algorithms to efficiently compute simultaneously local and global minima," in Proc. IEEE CEC, vol. 2. 2005, pp. 1847--1854.

[9]

M. G. Epitropakis, V. P. Plagianakos, and M. N. Vrahatis, "Balancing the exploration and exploitation capabilities of the differential evolution algorithm," in Proc. IEEE CEC, 2008, pp. 2686?--2693.

[10]

M. G. Epitropakis, D. K. Tasoulis, N. G. Pavlidis, V. P. Plagianakos, and M. N. Vrahatis, "Enhancing differential evolution utilizing proximitybased mutation operators," IEEE Trans Evol. Comput., vol. 15, no. 1, pp. 99--119, Feb. 2011.

Digital Library

[11]

A. K. Qin, V. L. Huang, and P. N. Suganthan, "Differential evolution algorithm with strategy adaptation for global numerical optimization," IEEE Trans. Evol. Comput., vol. 13, no. 2, pp. 398--417, Apr. 2009.

Digital Library

[12]

R. Mallipeddi, P. Suganthan, Q. Pan, and M. Tasgetiren, "Differential evolution algorithm with ensemble of parameters and mutation strategies," Appl. Soft Comput., vol. 11, no. 2, pp. 1679--1696, Mar. 2011.

Digital Library

[13]

W. Gong, Z. Cai, C. X. Ling, and H. Li, "Enhanced differential evolution with adaptive strategies for numerical optimization," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 41, no. 2, pp. 397--413, Apr. 2011.

Digital Library

[14]

Y. Wang, Z. Cai, and Q. Zhang, "Differential evolution with composite trial vector generation strategies and control parameters," IEEE Trans. Evol. Comput., vol. 15, no. 1, pp. 55--66, Feb. 2011.

Digital Library

[15]

H. Y. Fan and J. Lampinen, "A trigonometric mutation operator to differential evolution," J. Global Optim., vol. 27, no. 1, pp. 105--129, 2003.

Digital Library

[16]

P. Kaelo and M. M. Ali, "Differential evolution algorithms using hybrid mutation," Comput. Optimization Appl., vol. 37, pp. 231--246, Jun. 2007.

Digital Library

[17]

S. Das, A. Abraham, U. K. Chakraborty, and A. Konar, "Differential evolution using a neighborhood-based mutation operator," IEEE Trans. Evol. Comput., vol. 13, no. 3, pp. 526--553, Jun. 2009.

Digital Library

[18]

J. Q. Zhang and A. C. Sanderson, "JADE: Adaptive differential evolution with optional external archive," IEEE Trans. Evol. Comput., vol. 13, no. 5, pp. 945--958, Oct. 2009.

Digital Library

[19]

S. M. Islam, S. Das, S. Ghosh, S. Roy, and P. N. Suganthan, "An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 2, pp. 397--413, Apr. 2012.

Digital Library

[20]

M. G. Epitropakis, D. K. Tasoulis, N. G. Pavlidis, V. P. Plagianakos, and M. N. Vrahatis, "Enhancing differential evolution utilizing proximitybased mutation operators," IEEE Trans Evol. Comput., vol. 15, no. 1, pp. 99--119, Feb. 2011.

Digital Library

[21]

W. Gong and Z. Cai, "Differential evolution with ranking-based mutation operators," IEEE Trans. Cybern.,vol. 43, no. 6, pp. 2066--2081, Dec. 2013.

[22]

Y. Cai and J. Wang, "Differential evolution with neighborhood and direction information for numerical optimization," IEEE Trans. Cybern., vol. 43, no. 6, pp. 2202--2215, Dec. 2013.

[23]

P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y.-P. Chen, A. Auger, and S. Tiwari, "Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization," Nanyang Technol. Univ., Singapore, KanGAL Rep. No. 2005005, May 2005, IIT Kanpur, India.

[24]

J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer, "Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems," IEEE Trans. Evol. Comput., vol. 10, no. 6, pp. 646--657, Dec. 2006.

Digital Library

[25]

S. Rahnamayan, H. R. Tizhoosh, and M. M. A. Salama, "Opposition based differential evolution," IEEE Trans. Evol. Comput., vol. 12, no. 1, pp. 64--79, Feb. 2008.

Digital Library

[26]

J. Derrac, S. Garcá, D. Molina, and F. Herrera, "A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms," Swarm Evol. Comput., vol. 1, no. 1, pp. 3--18, Mar. 2011.

[27]

J. Rönkkönen, S. Kukkonen, and K. V. Price, "Real-parameter optimization with differential evolution," in Proc. IEEE Congr. Evol. Comput., 2005, pp. 506--513.

[28]

U. K. Chakraborty, Advances in Differential Evolution. Berlin, Germany: Springer, 2008.

Digital Library

Cited By

Qiao KLiang JQu BYu KYue CSong H(2022)Differential Evolution with Level-Based Learning MechanismComplex System Modeling and Simulation10.23919/CSMS.2022.00042:1(35-58)Online publication date: Mar-2022
https://doi.org/10.23919/CSMS.2022.0004
Mohamed AHadi AMohamed A(2021)Differential Evolution Mutations: Taxonomy, Comparison and Convergence AnalysisIEEE Access10.1109/ACCESS.2021.30772429(68629-68662)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3077242
Bajer D(2019) Adaptive -tournament mutation scheme for differential evolution Applied Soft Computing10.1016/j.asoc.2019.105776(105776)Online publication date: Sep-2019
https://doi.org/10.1016/j.asoc.2019.105776
Show More Cited By

Index Terms

Differential evolution using mutation strategy with adaptive greediness degree control
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1478 pages

ISBN:9781450326629

DOI:10.1145/2576768

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Copyright © 2014 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Publication History

Published: 12 July 2014

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Ministry of Science and Technology of the People's Republic of China

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GECCO '14

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SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

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GECCO '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Qiao KLiang JQu BYu KYue CSong H(2022)Differential Evolution with Level-Based Learning MechanismComplex System Modeling and Simulation10.23919/CSMS.2022.00042:1(35-58)Online publication date: Mar-2022
https://doi.org/10.23919/CSMS.2022.0004
Mohamed AHadi AMohamed A(2021)Differential Evolution Mutations: Taxonomy, Comparison and Convergence AnalysisIEEE Access10.1109/ACCESS.2021.30772429(68629-68662)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3077242
Bajer D(2019) Adaptive -tournament mutation scheme for differential evolution Applied Soft Computing10.1016/j.asoc.2019.105776(105776)Online publication date: Sep-2019
https://doi.org/10.1016/j.asoc.2019.105776
Gokul KPooja RJeyakumar G(2018)Empirical Evidences to Validate the Performance of Self-Switching Base Vector Based Mutation of Differential Evolution Algorithm2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)10.1109/ICACCI.2018.8554928(2213-2218)Online publication date: Sep-2018
https://doi.org/10.1109/ICACCI.2018.8554928
Sato JAkashi TYamada TIto K(2018)Affine template matching by differential evolution with adaptive two‐part searchIEEJ Transactions on Electrical and Electronic Engineering10.1002/tee.2284414:4(615-622)Online publication date: 5-Dec-2018
https://doi.org/10.1002/tee.22844
Gokul KPooja RGowtham KJeyakumar G(2017)A self-switching base vector selection mechanism for differential mutation of differential evolution algorithm2017 International Conference on Communication and Signal Processing (ICCSP)10.1109/ICCSP.2017.8286647(1545-1549)Online publication date: Apr-2017
https://doi.org/10.1109/ICCSP.2017.8286647
Zheng LZhang STang KZheng S(2017)Differential evolution powered by collective informationInformation Sciences: an International Journal10.1016/j.ins.2017.02.055399:C(13-29)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.ins.2017.02.055
Das SMullick SSuganthan P(2016)Recent advances in differential evolution – An updated surveySwarm and Evolutionary Computation10.1016/j.swevo.2016.01.00427(1-30)Online publication date: Apr-2016
https://doi.org/10.1016/j.swevo.2016.01.004
Wang YLiu ZLi JLi HYen G(2016)Utilizing cumulative population distribution information in differential evolutionApplied Soft Computing10.1016/j.asoc.2016.07.01248:C(329-346)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.07.012

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