V. M. Sineglazov, D. P. Karabetsky, O. V. Klanovets


It is considered the problem of maximum power point tracking for photovoltaic modules. The solution of this problem is based on a static parametric meta-optimization for swarm intelligence algorithms. For the problem solution Particle Swarm Optimization algorithm meta-optimized with Bat Search algorithm is proposed. The basic idea for the algorithms is that they consist of the base algorithm, which is used to solve the main optimization problem and the meta-algorithm used to find an optimal strategy for the base one. The object function for the base algorithm is an object function of the general optimization problem. The meta-algorithm evaluates the meta-function defined at each generation and tries to minimize it by searching for better strategies for the base algorithm. It is considered the example of its application.


Maximum power point tracking; photo-voltaic modules; renewable energy sources; meta-algorithm; swarm intelligence algorithms; Bat Search algorithm; Particle Swarm Optimization


V. M. Sineglazov, "Prospects for the development of the global electric power industry until 2035," Electricity, transmission and distribution. 2011, no. 2. p. 103.

C. Tugcu, I. Ozturk, and А. Alper, "Renewable and non-renewable energy consumption and economic growth relationship revisited: Evidence from G7 countries," Energy Economics, vol. 34, Issue 6, November 2012, 1942 р.

Javad Farzaneh, Reza Keypour, and Mojtaba Ahmadieh Khanesar, “A New Maximum Power Point Tracking Based on Modified Firefly Algorithm for PV System Under Partial Shading Conditions,” Technol Econ Smart Grids Sustain Energy, 2018, 3:9.

J. Wei, Y. Wang, and H. Wang, "A hybrid particle swarm evolutionary algorithm for constrained multi-objective optimization," Computing and Informatics, vol. 29, pp. 701–718, 2010.

Full Text: PDF


  • There are currently no refbacks.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.