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International Conference on Advanced Engineering  Theory and Applications

AETA 2018: AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application pp 197–206Cite as

A Lightweight SHADE-Based Algorithm for Global Optimization - liteSHADE

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 554))

Abstract

In this paper, a novel lightweight version of the Successful-History based Adaptive Differential Evolution (SHADE) is presented as the first step towards a simple, user-friendly, metaheuristic algorithm for global optimization. This simplified algorithm is called liteSHADE and is compared to the original SHADE on the CEC2015 benchmark set in three dimensional settings – 10D, 30D and 50D. The results support the idea, that simplification may lead to a successful and understandable algorithm with competitive or even better performance.

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References

  1. Storn, R., Price, K.: Differential Evolution - A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces, vol. 3. ICSI, Berkeley (1995)

    MATH  Google Scholar 

  2. Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33(1–2), 61–106 (2010)

    Article  Google Scholar 

  3. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)

    Article  Google Scholar 

  4. Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution–an updated survey. Swarm Evol. Comput. 27, 1–30 (2016)

    Article  Google Scholar 

  5. Gämperle, R., Müller, S.D., Koumoutsakos, P.: A parameter study for differential evolution. Adv. Intell. Syst. Fuzzy Syst. Evol. Comput. 10, 293–298 (2002)

    Google Scholar 

  6. Liu, J., Lampinen, J.: On setting the control parameter of the differential evolution method. In: Proceedings of the 8th International Conference on Soft Computing (MENDEL 2002), pp. 11–18 (2002)

    Google Scholar 

  7. Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: IEEE Congress on Evolutionary Computation (CEC), pp. 71–78. IEEE, June 2013

    Google Scholar 

  8. Tanabe, R., Fukunaga, A.S.: Improving the search performance of SHADE using linear population size reduction. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1658–1665. IEEE, July 2014

    Google Scholar 

  9. Guo, S.M., Tsai, J.S.H., Yang, C.C., Hsu, P.H.: A self-optimization approach for L-SHADE incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1003–1010. IEEE, May 2015

    Google Scholar 

  10. Awad, N.H., Ali, M.Z., Suganthan, P.N., Reynolds, R.G.: An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2958–2965. IEEE, July 2016

    Google Scholar 

  11. Brest, J., Maučec, M.S., Bošković, B.: Single objective real-parameter optimization: algorithm jSO. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1311–1318. IEEE, June 2017

    Google Scholar 

  12. Piotrowski, A.P., Napiorkowski, J.J.: Some metaheuristics should be simplified. Inf. Sci. 427, 32–62 (2018)

    Article  MathSciNet  Google Scholar 

  13. Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T., Zamuda, A.: Distance based parameter adaptation for differential evolution. In: IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–7. IEEE, November 2017

    Google Scholar 

  14. Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)

    Article  Google Scholar 

  15. Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T.: Archive analysis in SHADE. In: International Conference on Artificial Intelligence and Soft Computing, pp. 688–699. Springer, Cham (2017)

    Google Scholar 

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Acknowledgments

This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014), further by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2019/002. This work is also based upon support by COST (European Cooperation in Science & Technology) under Action CA15140, Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO), and Action IC1406, High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). The work was further supported by resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz).

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Authors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, 760 01, Zlin, Czech Republic

    Adam Viktorin, Roman Senkerik, Michal Pluhacek, Tomas Kadavy & Roman Jasek

Authors
  1. Adam Viktorin
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  2. Roman Senkerik
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  3. Michal Pluhacek
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  4. Tomas Kadavy
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  5. Roman Jasek
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Corresponding author

Correspondence to Adam Viktorin .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Faculty of Electrical Engineering, VŠB-TUO, Ostrava-Poruba, Czech Republic

    Ivan Zelinka

  2. Department of Electronics, Faculty of Electrical Engineering, VŠB-TUO, Ostrava, Czech Republic

    Pavel Brandstetter

  3. International Cooperation, Research and Training Institute, Ton Duc Thang University, Ho Chi Minh, Vietnam

    Tran Trong Dao

  4. Department of Automatic Control, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Vo Hoang Duy

  5. Department of Mechanical Design Engineering, Pukyong National University, Busan, Korea (Republic of)

    Sang Bong Kim

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Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T., Jasek, R. (2020). A Lightweight SHADE-Based Algorithm for Global Optimization - liteSHADE. In: Zelinka, I., Brandstetter, P., Trong Dao, T., Hoang Duy, V., Kim, S. (eds) AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. AETA 2018. Lecture Notes in Electrical Engineering, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-030-14907-9_20

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  • DOI: https://doi.org/10.1007/978-3-030-14907-9_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14906-2

  • Online ISBN: 978-3-030-14907-9

  • eBook Packages: EngineeringEngineering (R0)

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