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Enhancing AGDE Algorithm Using Population Size Reduction for Global Numerical Optimization

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The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) (AMLTA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 723))

Abstract

Adaptive guided differential evolution algorithm (AGDE) is a DE algorithm that utilizes the information of good and bad vectors in the population, it introduced a novel mutation rule in order to balance effectively the exploration and exploitation tradeoffs. It divided the population into three clusters (best, better and worst) with sizes 100p%, NP-2 * 100p% and 100p% respectively. Where p is the proportion of the partition with respect to the total number of individuals in the population (NP). AGDE selects three random individuals, one of each partition to implement the mutation process. Besides, a novel adaptation scheme was proposed in order to update the value of crossover rate without previous knowledge about the characteristics of the problems. This paper introduces enhanced AGDE (EAGDE) with non-linear population size reduction, which gradually decreases the population size according to a non-linear function. Moreover, a newly developed rule developed to determine the initial population size, that is related to the dimensionality of the problems.

The performance of the proposed algorithm is evaluated using CEC2013 benchmarks and the results are compared with the state-of-art DE and non-DE algorithms, the results showed a great competitiveness for the proposed algorithm over the other algorithms, and the original AGDE.

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Author information

Authors and Affiliations

  1. Decision Support Department, Faculty of Computers and Information, Cairo University, Giza, 12613, Egypt

    Ali Khater Mohamed, Ehab Zaki Elfeky & Mohamed Saleh

  2. Operations Research Department, Institute of Statistical Studies and Research, Cairo University, Giza, 12613, Egypt

    Ali Wagdy Mohamed

Authors
  1. Ali Khater Mohamed
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  2. Ali Wagdy Mohamed
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  3. Ehab Zaki Elfeky
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  4. Mohamed Saleh
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Corresponding authors

Correspondence to Ali Khater Mohamed or Ali Wagdy Mohamed .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information Sciences, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Faculty of Computers and Information, Department of Computer Science and Engineering, Mansoura University, Dakahlia, Egypt

    Mohamed Elhoseny

  4. Arab Academy for Science, Technology and Maritime Transport (AASTMT), Dokki, Egypt

    Mohamed Mostafa

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Mohamed, A.K., Mohamed, A.W., Elfeky, E.Z., Saleh, M. (2018). Enhancing AGDE Algorithm Using Population Size Reduction for Global Numerical Optimization. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-74690-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74689-0

  • Online ISBN: 978-3-319-74690-6

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