Performance Assessment of Metaheuristic Algorithms: Firefly, Grey Wolf, and Moth Flame in Coal Pyrolysis Kinetic Parameter Estimation

This study investigates the effectiveness of the Firefly Optimizer (FFA), Grey Wolf Optimizer (GWO), and Moth Flame Optimizer (MFO) metaheuristic algorithms in estimating the kinetic parameters of a single-step coal pyrolysis model. By examining the effects of the algorithmic configuration, the initial parameter estimates, and the search space size on the efficacy and efficiency of the optimization run, the research seeks to encourage the qualified engineering application of these algorithms in the field of pyrolysis modeling. Four critical analyses were conducted: convergence efficiency, robustness and repeatability, parameter tuning, and performance on noisy data. MFO and GWO had comparable fitness scores of 1.05×10-4 and 1.04×10-4 respectively in the optimisation run analysis, while FireFly Algorithm (FFA) fell behind with a score of 1.09×10-4. Regarding the calculation time, FFA showed better results than other optimizers with an execution time of 113.75 seconds. MFO showed initial promise in convergence analysis with speedy convergence, whereas GWO progressively enhanced its solutions. Additionally, GWO was shown to be the most dependable algorithm with the lowest values for average fitness score and execution time at 1.07×10-4 and 38.86 seconds. The combined values of standard deviation in fitness value and execution time for GWO were 1.07×10-6 and 0.35 indicating its robustness towards initial parameters. Similar to this, investigations on repeatability emphasized the reliability of the GWO method. Further, the parameter tuning assessments supported the balanced performance of GWO, and the studies of noise handling discovered GWO to be the most robust to noisy data. Overall, GWO is recommended as a one-stop average solution for the general engineered application; however, algorithm choice hinges on the specific requirement.