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Abstract

This paper presents the modified global optimization co-algorithm based on the Mind Evolutionary Computation (MEC) algorithm for optimizing optical system designs. This kind of systems requires high precision. As a result optimization algorithms tend to converge slowly to a local optimum while trying to guarantee high quality of solutions. The concept of a co-algorithm helps to overcome this issue by identifying the most promising search areas and allocating there more computational resources. The outline of the proposed co-algorithm as well as its software implementation are described in the paper. The algorithm was utilized to optimize the structure of an optical Cooke triplet lens and helped identify various designs with best optical properties. All results are presented in the paper.

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References

  1. Karpenko, A.P.: Modern algorithms of search engine optimization. In: Nature-Inspired Optimization Algorithms, p. 446. Bauman MSTU Publication, Moscow (2014)

    Google Scholar 

  2. Weise, T.: Global Optimization Algorithms - Theory and Application. University of Kassel, p. 758 (2008)

    Google Scholar 

  3. Sakharov, M., Karpenko, A.: Multi-memetic mind evolutionary computation algorithm based on the landscape analysis. In: 7th International Conference Theory and Practice of Natural Computing, TPNC 2018, Dublin, Ireland, 12–14 December 2018, pp. 238–249, Proceedings. Springer (2018). https://doi.org/10.1007/978-3-030-04070-3

  4. Agasiev, T., Karpenko, A.: The program system for automated parameter tuning of optimization algorithms. Procedia Comput. Sci. 103, 347–354 (2017). https://doi.org/10.1016/j.procs.2017.01.120

    Article  Google Scholar 

  5. Fuerschbach, K.: Freeform, φ-polynomial optical surfaces: optical design, fabrication and assembly, PhD Thesis. http://hdl.handle.net/1802/28531

  6. Chengyi, S., Yan, S., Wanzhen, W.: A survey of MEC: 1998–2001. In: 2002 IEEE International Conference on Systems, Man and Cybernetics IEEE SMC2002, Hammamet, Tunisia, 6–9 October 2002, vol. 6, pp. 445–453. Institute of Electrical and Electronics Engineers Inc. (2002)

    Google Scholar 

  7. Sakharov, M., Karpenko, A.: Performance investigation of mind evolutionary computation algorithm and some of its modifications. In: Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI 2016), pp. 475–486. Springer (2016). https://doi.org/10.1007/978-3-319-33609-1_43

  8. Lakshminarayan, H., Banerjee, S.: Genetic algorithm in the structural design of Cooke triplet lenses. In: Design and Engineering of Optical Systems II, vol. 3737. International Society for Optics and Photonics (1999). https://doi.org/10.1117/12.360005

  9. Vasiljevic, D.M.: Optimization of the Cooke triplet with various evolution strategies and damped least squares. In: Optical Design and Analysis Software, vol. 3780. International Society for Optics and Photonics (1999). https://doi.org/10.1117/12.363779

  10. Bociort, F., Van Driel, E., Serebriakov, A.: Networks of local minima in optical system optimization. Opt. Lett. 29(2), 189–191 (2004). https://doi.org/10.1364/OL.29.000189

    Article  Google Scholar 

  11. Houllier, T.: Search algorithms and optical systems design. In: Zemax ENVISION 2019, Paris, France, 26–28 March 2019

    Google Scholar 

  12. Jie, J., Zeng, J.: Improved mind evolutionary computation for optimizations. In: Proceedings of 5th World Congress on Intelligent Control and Automation, Hang Zhou, China, pp. 2200–2204 (2004)

    Google Scholar 

  13. Jie, J., Han, C., Zeng, J.: An extended mind evolutionary computation model for optimizations. Appl. Math. Comput. 185, 1038–1049 (2007)

    MATH  Google Scholar 

  14. Born, M., Emil, W., Bhatia, A.B.: Principles of Optics, seventh (expanded) edn. Cambridge University Press, Cambridge (1999)

    Google Scholar 

  15. Sakharov, M., Karpenko, A.: Parallel multi-memetic global optimization algorithm for optimal control of polyarylenephthalide’s thermally-stimulated luminescence. In: Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019. Advances in Intelligent Systems and Computing, vol. 991, pp. 191–201. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-21803-4_20

  16. Sakharov, M.K., Karpenko, A.P.: Adaptive load balancing in the modified mind evolutionary computation algorithm. Supercomput. Front. Innovations 5(4), 5–14 (2018). https://doi.org/10.14529/jsfi180401

    Article  Google Scholar 

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Correspondence to Maxim Sakharov .

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Sakharov, M., Houllier, T., Lépine, T. (2020). Mind Evolutionary Computation Co-algorithm for Optimizing Optical Systems. In: Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19). IITI 2019. Advances in Intelligent Systems and Computing, vol 1156. Springer, Cham. https://doi.org/10.1007/978-3-030-50097-9_48

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