Application of the modified method simulating the behavior of a flock of Moths to solve the optimal open loop control problem of a mobile robot movement

6

Abstract

The article proposes a modification of the metaheuristic optimization method simulating the behavior of a swarm of moths, which belongs to the group of bio-inspired methods. A step-by-step algorithm is formed and the efficiency of the modified method is studied on a generally accepted set of test functions of many variables with a complex structure of level surfaces, on the problem of optimal open loop control with a known exact solution, as well as on the problem of determining the parameters of the tension/compression spring with constraints such as inequalities. The advantage of the modified method over the original version is shown. It is demonstrated that the method allows finding solutions of sufficiently good quality in a time acceptable from a practical point of view. A solution to an applied problem of finding the optimal open loop control of a mobile robot on a plane in the presence of obstacles is given. The goal of the control is to achieve a given end point while minimizing the elapsed time and fulfilling the condition of enveloping forbidden areas. To find the control law as a function of time, a piecewise constant approximation was used, which allows reducing the problem to finding a finite number of unknown parameters. The solutions of the terminal problem of speed of response with different structure and parameters of the composite quality functional are considered using the sequential application of the developed modified method simulating the behavior of a swarm of moths, the random search method with sequential reduction of the study area and the path-relinking method. The results of comparison with known solutions are presented.

General Information

Keywords: global optimization, metaheuristic optimization algorithms, optimal open loop control

Journal rubric: Optimization Methods

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2025150105

Received: 19.12.2024

Accepted:

For citation: Panteleev A.V., Nadorov I.S. Application of the modified method simulating the behavior of a flock of Moths to solve the optimal open loop control problem of a mobile robot movement. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2025. Vol. 15, no. 1, pp. 81–109. DOI: 10.17759/mda.2025150105. (In Russ., аbstr. in Engl.)

References

  1. Panteleev A.V. Metaheuristic Algorithms for Optimizing the Laws of Control of Dynamic Systems. Moscow, Faktorial Publ., 2020. 564 p.
  2. Handbook of Metaheuristics / Eds M. Gendreau, J-Y. Potvin. N.Y.: Springer, 2019. 610 p.
  3. Karpenko A.P. Modern algorithms for search engine optimization. Algorithms inspired by nature. Moscow, Izd-vo MGTU im. N.E. Baumana, 2021. 448 p.
  4. Niculina Dragoi E., Dafinescu V. Review of metaheuristics inspired from the animal kingdom. Mathematics, 2021. Vol. 9, 2335.
  5. Tzanetos A., Fister I., Dounias G. A comprehensive database of nature-inspired algorithms. Data in Brief., 2020. Vol. 31, 105792.
  6. Bio-inspired computation: Where we stand and what’s next / Del Ser J., Osaba E., Molina D., Yang X.-S., Salcedo-Sanz S., Camacho D., Das S., Suganthan P.N., Coello Coello C.A., Herrera F. Swarm and Evolutionary Computation, 2019. Vol. 48, pp. 220–250.
  7. Panteleev A.V., Karane M.M.S. Multi-agent and bio-inspired methods for technical systems optimization. M.: Izd-vo Dobroe slovo i Ko, 2024. 336 p.
  8. Sergeyev Y.D., Kvasov D.E., Mukhametzhanov M.S. On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget. Scientific Reports, 2018. Vol. 8, P. 453.
  9. Sergeyev Y.D., Kvasov D.E. Deterministic Global Optimization: An Introduction to the Diagonal Approach. Springer, 2017. 136 p.
  10. Mirjalili S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 2015. Vol. 89, pp. 228–249.
  11. Xiaodong Zhao, Yiming Fang, Le Liu, Miao Xu, Qiang Li. A covariance-based Moth–flame optimization algorithm with Cauchy mutation for solving numerical optimization problems. Applied Soft Computing, 2022. Vol. 119.
  12. Wolpert D.H., Macready W.G. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1997. Vol. 1, pp. 67–82.
  13. X.-S. Yang, Test problems in optimization. Engineering Optimization: An Introduction with Metaheuristic Applications (Eds Xin-She Yang), John Wiley & Sons, 2010.
  14. Diveev A.I., Konstantinov S.V. Study the practical convergence of evolutionary algorithms for the optimal program control of a wheeled robot. Journal of Computer and Systems Sciences International. 2018. Vol. 57, no. 4, pp. 561–580.
  15. Konstantinov S.V., Diveev A.I., Balandina G.I., Baryshnikova A.A. Comparative Research of Random Search Algorithms and Evolutionary Algorithms for the Optimal Control Problem of the Mobile Robot. 13th Intern. Symp. “Intelligent Systems” (INTELS’18). Procedia Computer Science, 150, 2019. pp. 462–470.
  16. Hoare C.A.R. Algorithm 64: Quicksort. Communications of the ACM, 1961. Vol. 4, P. 321.
  17. Golinski J. An adaptive optimization system applied to machine synthesis. Mech. Mash.Theory, 1973. Vol. 8, no. 3, pp. 419–436.
  18. Luus R. Iterative Dynamic Programming. London, Chapman & Hall/CRC, 2000. 331 p.
  19. Glover. F., Marti R., Laguna M. Fundamentals of scatter search and path-relinking. Control & Cybernetics, 2000. Vol. 39, pp. 653–684.

Information About the Authors

Andrey V. Panteleev, Doctor of Physics and Matematics, Professor, Head of the Department of Mathematical Cybernetics, Institute of Information Technologies and Applied Mathematics, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-2493-3617, e-mail: avpanteleev@inbox.ru

Ivan S. Nadorov, Student of the Institute "Computer Science and Applied Mathematics", Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0009-0008-2085-2987, e-mail: nnadorovivan@gmail.com

Metrics

 Web Views

Whole time: 22
Previous month: 0
Current month: 22

 PDF Downloads

Whole time: 6
Previous month: 0
Current month: 6

 Total

Whole time: 28
Previous month: 0
Current month: 28