###### Abstract  
Parameter identification is an important research topic with a variety of applications in industrial and environmental problems. Usually, a functional has to be minimized in conjunction with parameter identification; thus, there is a certain similarity between the parameter identification and optimization. A number of rigorous and efficient algorithms for optimization problems were developed in recent decades for the case of a convex functional. In the case of a non-convex functional, the metaheuristic algorithms dominate. This paper discusses an optimization method called modified bee colony algorithm (MBC), which is a modification of the standard bees algorithm (SBA). The SBA is inspired by a particular intelligent behavior of honeybee swarms. The algorithm is adapted for the parameter identification of reaction-dominated pore-scale transport when a non-convex functional has to be minimized. The algorithm is first checked by solving a few benchmark problems, namely finding the minima for Shekel, Rosenbrock, Himmelbau and Rastrigin functions. A statistical analysis was carried out to compare the performance of MBC with the SBA and the artificial bee colony (ABC) algorithm. Next, MBC is applied to identify the three parameters in the Langmuir isotherm, which is used to describe the considered reaction. Here, 2D periodic porous media were considered. The simulation results show that the MBC algorithm can be successfully used for identifying admissible sets for the reaction parameters in reaction-dominated transport characterized by low Pecklet and high Damkholer numbers. Finite element approximation in space and implicit time discretization are exploited to solve the direct problem. In the case of heterogeneous reactions at the pore scale, the species transport is coupled to surface reaction via boundary conditions. When the reaction rates are not known, their identification falls into the class of boundary value inverse problems [3; 4; 5; 6]. The additional information which is needed to identify the parameters is often provided in the form of the dynamic change of the concentration at the outlet (e.g., so-called breakthrough curves). In the literature, inverse problems in porous media flow are discussed mainly in connection with parameter identification for macroscopic, Darcy-scale problems. An overview on inverse problems in groundwater Darcy-scale modeling can be found in [7]. The identification of parameters for pore-scale models is discussed in this paper, and the algorithms from [7] and other papers discussing parameter identification at the macro scale cannot be applied here without modification. Let us shortly mention some general approaches for solving inverse problems. Different algorithms can be applied for solving parameter identification problems, see, e.g., [8; 9]. Many of these algorithms exploit deterministic methods based on Tikhonov regularization technique [10; 11] and target minimizing a functional of the difference between measured and computed quantities. An important part of such algorithms is the definition of a feasible set of parameters on which the functional is minimized. Local or global optimization procedures are used in the optimization [12; 13]. In this sense, it could be observed that there is a certain similarity between the mathematical formulation of an optimization problem and of a parameter identification problem. Metaheuristic methods are a relatively new and rapidly developing class of optimization methods. There are various metaheuristic algorithms: genetic algorithms (GA) [14], particle swarm optimization (PSO) [15], genetic programming (GP) [16], differential evolution (DE) [17], and greedy randomized adaptive search procedure (GRASP) [18], to name just a few. Metaheuristic algorithms are the working horse in studying different and distinct applications.