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Parallel Multi-memetic Global Optimization Algorithm for Optimal Control of Polyarylenephthalide’s Thermally-Stimulated Luminescence

  • Maxim SakharovEmail author
  • Anatoly Karpenko
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)

Abstract

This paper presents a modification of the parallel multi-memetic global optimization algorithm based on the Mind Evolutionary Computation algorithm which is designed for loosely coupled computing systems. The algorithm implies a two-level adaptation strategy based on the proposed landscape analysis procedure and utilization of multi-memes. It is also consistent with the architecture of loosely coupled computing systems due to the new static load balancing procedure that allows to allocate more computational resources for promising search domain’s sub-areas while maintaining approximately equal load of computational nodes. The new algorithm and its software implementation were utilized to solve a computationally expensive optimal control problem for a model of chemical reaction’s dynamic for thermally-stimulated luminescence of polyarylenephtalides. Results of the numerical experiments are presented in this paper.

Keywords

Global optimization Parallel algorithms Multi-memetic algorithms 

Notes

Acknowledgments

This work was supported by the RFBR under a grant 18-07-00341.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Bauman MSTUMoscowRussia

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