# Pitfalls in Metaheuristics Solving Stoichiometric-Based Optimization Models for Metabolic Networks
An example of this is the prediction of cell growth of the cyanobacteria _Synechocystis_ in [13]; it has also been used in the degradation of glucose by anaerobic digestion to predict the distribution of metabolites and reveal the transformation of carbon in order to evaluate the conversion of ethanol, propionic acid, and butyric acid into acetic acid [14]. FBA has served as a study in medicine, where flux activities were calculated to study differences in metabolic pathways, comparing breast cancer subtypes [15]. The FBA methodology has been used to evaluate metabolic fluxes in different strains of microalgae using three heterotrophic and mixotrophicotrophic growth conditions. The first microalgae to use FBA was the microalgae _Chlamydomonas reinhardtii_[16], with which the first metabolic map was obtained. Later, the microalga _Chlorella vulgaris_ was utilized for the study of lipid production [17]; both had biomass production as their sole objective function. Although this approach has been widely used, the distribution of metabolites through the pathways is conditioned by the metabolites present in the objective function equation and in the experimental parameters; blocking some metabolic pathways results in a distribution of fluxes with zero values. Moreover, FBA has been widely used in the search for maximizing the production of compounds of interest, but cellular metabolism in its natural state does not guide metabolic pathways toward the production of a particular metabolite. In the search for a better understanding of microalgae metabolism, new optimization techniques have emerged, such as metaheuristics for multi-objective optimization [18]. These techniques seek a more uniform distribution and are closer to the reality of what happens at the metabolic level by simultaneously optimizing various functions with conflicting objectives. Multi-objective optimization is generally based on the search for solutions to different conflicting objectives that must be optimized simultaneously. Multi-objective optimization is of great importance and has been carried out at a technological and scientific level. Some examples in the chemical industry have been reported in optimizing operating unit processes, biorefinery, reaction engineering, prevention and control, etc. [19]. It has also been utilized in the biology and medicine sector [20], and in metabolic engineering [18]. Multi-objective optimization contrasts with open-access Cobrapy FBA, which only maximizes or minimizes one objective function and where only one solution is obtained. In multi-objective optimization, a set of solutions is obtained. The solutions obtained are called non-dominated because no other solution in the search space is better than the others when all objectives are considered simultaneously. This set of solutions is known as Pareto optimal solutions [21]. Between the methods that have been used for multi-objective optimization of microorganisms are evolutionary algorithms such as NSGAII [18], MOMO, based on the Bio-objective model, and exact mathematical methods that spend a lot of computational resources [22]. Metaheuristic algorithms originate from the natural evolution of biological groups; they are part of artificial intelligence and are born from natural computing and heuristic methods (partial search algorithms). Compared to mathematical methods that generate a large computational expense, these methods provide sufficiently good solutions to an optimization algorithm with an acceptable computational time and space [23]. Multi-Objective Evolutionary Algorithms (MOEAs) are widely recognized in the scientific community as an approach to solving multi-objective optimization problems. In particular, the NSGAII (multi-objective EA based on non-dominated classification) proposed by [24] has been quite effective when handling two or three objectives [25; 26]. The MOEA/D evolutionary algorithm based on decomposition is tested because it has the characteristic that it works correctly when there are more than three objective functions.