A new comprehensive learning marine predator algorithm for extracting the optimal parameters of supercapacitor model

This paper proposes an improved metaheuristic approach of comprehensive learning marine predator algorithm (CLMPA) to identify the optimal parameters of the supercapacitor equivalent circuit. The division of the iteration numbers among the Marine Predators Algorithm phases causes trapping the particles in the local solutions as they did not have adequate trials to discover the search landscape. Therefore, in this paper, the authors used the principle of the comprehensive learning strategy to guarantee to share the best experiences among all the particles with the target of avoiding the immature convergence. The sum squared error between the experimental and estimated voltages is considered as an objective function. Eight parameters to be identified are R_i, R_d, R_l, R_lea, C_i0, C_i1, C_d, and C_l, two SCs are considered in the analysis with values of 470 F and 1500 F. Other optimization approaches of manta ray foraging optimizer (MRFO), water cycle algorithm (WCA), multi-verse optimizer (MVO), vortex search algorithm (VSA), marine predators algorithm (MPA), Archimedes optimization algorithm (AOA), Jellyfish search algorithm (JS), and Runge–Kutta Based Algorithm (RUN) are programmed and compared to the proposed CLMPA. Moreover, some reported works are considered in comparison. The obtained results confirmed the competence and preference of the proposed approach in constructing a reliable equivalent circuit of SC that converges to the real one.