A multi -hybrid algorithm is proposed in this paper based on the Kepler Optimization algorithm (KOA), Red Panda Optimization (RPO), Meerkat Optimization (MO), and Grey Wolf Optimizer (GWO). The proposed multi -hybrid ...
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A multi -hybrid algorithm is proposed in this paper based on the Kepler Optimization algorithm (KOA), Red Panda Optimization (RPO), Meerkat Optimization (MO), and Grey Wolf Optimizer (GWO). The proposed multi -hybrid algorithm, known as the Kepler Red Meerkat Grey (krmg) algorithm, incorporates the concepts of iterative division and mutation operators for enhanced operation. The krmg algorithm utilizes a new population shrinking mechanism to reduce the population size over subsequent iterations for reducing the computational burden. To evaluate the efficiency of the krmg algorithm in solving global optimization challenges, it has been tested on IEEE CEC 2014, CEC 2017, CEC 2019, as well as CEC 2022 benchmark test challenges. Also, the performance of the krmg algorithm has been evaluated for six constraint engineering design optimization problems. Furthermore, the krmg algorithm has also been evaluated for the parameter identification of proton exchange membrane fuel cells (PEMFC) on three distinct PEMFC modules, including the BCS 500 W, Ballard Mark V, as well as 250 W stack. Experimental and statistical comparison with respect to jDE100, FROBL-GJO, LX-TLA, RW-GWO, LSHADE-EpSin, EBOwithCMAR, jSO, SHADE, SaDE, JADE and others, prove that the proposed krmg is statistically significant with respect to other algorithms under comparison, and can be considered as a potential candidate for future research.
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