Abstract
This paper presents the analysis of the difference in control parameter adaptation between jSO and DISH algorithms. The DISH algorithm uses a distance based parameter adaptation and therefore, is based on the distance between successful offspring and its parent solution rather than on the difference in their corresponding objective function values. The DISH algorithm outperforms the jSO algorithm on the CEC 2015 benchmark set and the adaptation behavior on functions, where the performance is significantly different, is analyzed and commented. The findings from this paper might be used in the future design of jSO based single-objective optimization algorithms.
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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Storn, R., Price, K.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces, vol. 3. ICSI, Berkeley (1995)
Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 71–78. IEEE, June 2013
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. IEEE, June 2017
Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T., Zamuda, A.: Distance based parameter adaptation for differential evolution. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–7. IEEE, November 2017
Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T., Zamuda, A.: Distance based parameter adaptation for success-history based differential evolution. Swarm and Evolutionary Computation (2018, in press). https://doi.org/10.1016/j.swevo.2018.10.013. ISSN 2210–6502
Karafotias, G., Hoogendoorn, M., Eiben, Á.E.: Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans. Evol. Comput. 19(2), 167–187 (2015)
Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)
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. IEEE, July 2014
Brest, J., Maučec, M.S., Bošković, B.: iL-SHADE: Improved L-SHADE algorithm for single objective real-parameter optimization. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 1188–1195. IEEE, July 2016
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T. (2019). Analyzing Control Parameters in DISH. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11508. Springer, Cham. https://doi.org/10.1007/978-3-030-20912-4_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-20912-4_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20911-7
Online ISBN: 978-3-030-20912-4
eBook Packages: Computer ScienceComputer Science (R0)