A novel improved horned lizard optimization algorithm to identify optimal parameters of adaptive fuzzy logic MPPT for performance boosting of PEM fuel cell

Abstract

Horned Lizard Optimization Algorithm (HLOA) is a newly developed swarm-based metaheuristic technique that emulates the defensive behaviors of the horned lizard in nature. Like other algorithms, HLOA has certain limitations, including the tendency to become trapped in local optima due to a rapid loss of population diversity during the optimization process. This often results in premature convergence, particularly in complex optimization problems. To address these issues, this paper introduces an improved version of HLOA, named iHLOA, which incorporates two distinct strategies. First, the strengthened convergence strategy is utilized to improve the quality of individuals and accelerate the algorithmʼs convergence. Second, the mutation strategy is integrated to significantly boost population diversity, enhancing HLOAʼs ability to escape local minima. Various validation tests conducted on the CEC-2022 benchmark test demonstrate the effectiveness of the iHLOA algorithm in tackling global optimization challenges. Additionally, iHLOA was applied to determine the optimal gains for adaptive Fuzzy Logic Control (FLC) based MPPT to maximize energy harvested from the Proton Exchange Membrane Fuel Cell (PEMFC). The results demonstrate iHLOAʼs superiority over other algorithms, including the Seagull Optimization Algorithm (SOA), Black Widow Optimization Algorithm (BWOA), Sinh Cosh Optimizer (SCHO), Osprey Optimization Algorithm (OOA), Whale Optimization Algorithm (WOA), Greylag Goose Optimization (GGO), and the standard HLOA. iHLOA achieved the best performance with a value of 1.7755, followed by SCHO with 1.7806, while GGO recorded the worst performance at 1.8494. Additionally, iHLOA demonstrated superior stability with the lowest standard deviation (STD) of 0.0122, followed by SOA with 0.0193, while GGO had the highest STD of 0.1101. Furthermore, compared with the classical FLC-MPPT, the proposed FLC-MPPT based on iHLOA achieves faster tracking speeds and reduces oscillations around the MPP in a steady state.