Assessing and estimating essential parameters for a metabolic pathway by using a mathematical model is a
significant step in Systems Biology. However, estimating process often faces numerous obstacles, for example when the
number of unknown parameters escalates or data has noise, gets trapped in local minima and or having repeated
exploration of poor solution during search process. Thus, this study proposes an improved Bee Memory Differential
Evolution algorithm (IBMDE), which is a combination of the Differential Evolution algorithm (DE), the Kalman Filter
(KF), the Artificial Bee Colony algorithm (ABC), and a memory feature to solve the aforementioned problems. The
implemented metabolic pathways for this improved estimation algorithm were glycerol and pyruvate synthesis pathways.
IBMDE was successful in generating the estimated optimal kinetic parameter values with noticeable reduction in errors
(81.36% and 99.46% respectively) and faster convergence times (6.19% and 15.72% respectively) compared to DE, the
Genetic Algorithm (GA), the Nelder Mead (NM), and the Simulated Annealing (SA). The results indicated that, most
importantly, the kinetic parameters produced by IBMDE had enhanced the production of desired metabolites than the
other estimation algorithms. Besides that, the results also demonstrated the reliability of IBMDE as an estimation
algorithm in terms of lower error.