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Competition of Strategies in jSO Algorithm

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Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing (SEMCCO 2019, FANCCO 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1092))

Abstract

A newly proposed variant of the efficient jSO algorithm employing competition of eight strategies (cSO) is proposed. The main idea is to select the most proper strategy and adapt the setting to each solved problem. One more mutation variant and one more type of crossover are added to jSO, and moreover, the popular mechanism of Eigen coordinate system is applied. All eight strategies compete to be used in the next generations based on the successes in previous generations. The proposed cSO method has more wins over jSO significantly in more real-world problems than fails. The original jSO strategy is never the most frequently used strategy, compared with other newly employed strategies.

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References

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

    Google Scholar 

  2. Bujok, P., Poláková, R.: Migration model of jSO algorithm. In: 2018 25th International Conference on Systems Signals and Image Processing (IWSSIP). IEEE, New York (2018). IEEE Slovenia Section, University of Maribor

    Google Scholar 

  3. Bujok, P., Poláková, R.: Eigenvector crossover in the efficient jSO algorithm. MENDEL Soft Comput. J. 25, 65–72 (2019)

    Google Scholar 

  4. Bujok, P.: Tvrdík: enhanced success-history based parameter adaptation for differential evolution and real-world optimization problems. In: Papa, G., Mernik, M. (eds.) Bioinspired Optimization Methods and Their Applications, BIOMA, Bled, Slovenian, pp. 159–171 (2016)

    Google Scholar 

  5. Bujok, P., Tvrdík, J.: A comparison of various strategies in differential evolution. In: Matoušek, R. (ed.) MENDEL: 17th International Conference on Soft Computing, Brno, Czech Republic, pp. 48–55 (2011)

    Google Scholar 

  6. Bujok, P., Tvrdík, J., Poláková, R.: Evaluating the performance of SHADE with competing strategies on CEC 2014 single-parameter test suite. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 5002–5009 (2016)

    Google Scholar 

  7. 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 

  8. Das, S., Suganthan, P.N.: Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Technical report, Jadavpur University, India and Nanyang Technological University, Singapore (2010)

    Google Scholar 

  9. Elsayed, S.M., Sarker, R.A., Essam, D.L.: GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems. In: 2011 IEEE Congress of Evolutionary Computation (CEC), pp. 1034–1040 (2011)

    Google Scholar 

  10. Kotyrba, M., Volna, E., Bujok, P.: Unconventional modelling of complex system via cellular automata and differential evolution. Swarm Evol. Comput. 25, 52–62 (2015)

    Article  Google Scholar 

  11. Storn, R., Price, K.V.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  13. Tvrdík, J.: Competitive differential evolution. In: Matoušek, R., Ošmera, P. (eds.) MENDEL 2006, 12th International Conference on Soft Computing, pp. 7–12. University of Technology, Brno (2006)

    Google Scholar 

  14. Wang, Y., Li, H.X., Huang, T., Li, L.: Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Appl. Soft Comput. 18, 232–247 (2014)

    Article  Google Scholar 

  15. Wu, G., Mallipeddi, R., Suganthan, P.N.: Ensemble strategies for population-based optimization algorithms - a survey. Swarm Evol. Comput. 44, 695–711 (2019)

    Article  Google Scholar 

  16. Zamuda, A., Sosa, J.D.H.: Success history applied to expert system for underwater glider path planning using differential evolution. Expert Syst. Appl. 119, 155–170 (2019)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Ostrava, 30. dubna 22, 70200, Ostrava, Czech Republic

    Petr Bujok

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  1. Petr Bujok
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Correspondence to Petr Bujok .

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Editors and Affiliations

  1. University of Maribor, Maribor, Slovenia

    Aleš Zamuda

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  3. Division of Control and Instrumentation, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Bujok, P. (2020). Competition of Strategies in jSO Algorithm. In: Zamuda, A., Das, S., Suganthan, P., Panigrahi, B. (eds) Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing. SEMCCO FANCCO 2019 2019. Communications in Computer and Information Science, vol 1092. Springer, Cham. https://doi.org/10.1007/978-3-030-37838-7_11

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  • DOI: https://doi.org/10.1007/978-3-030-37838-7_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37837-0

  • Online ISBN: 978-3-030-37838-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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