###### Abstract
Some examples of recent works are as follows: DE and ABC algorithms were used to identify the kinetic parameters in CO2 hydrogenation for the production of hydrocarbons [19]; PSO was used to calibrate parameters of anaerobic digestion model [20]; and GRASP was used for post-disaster route prediction for a multi-period work-troops scheduling problem on the example of the city of Port-au-Prince in Haiti after the 2010 earthquake [21]. More information on metaheuristic algorithms can be found in [22; 23; 24; 25]. Among metaheuristic methods, evolutionary and behavioral ones stand out. Behavioral methods are multi-agent methods based on modeling the intellectual behavior of agent colonies (swarm intelligence). Swarm intelligence has become a research interest of many scientists and practitioners in the last decades. The bees algorithm was proposed by [PERSON], [PERSON] et al. in 2005 [26; 27]. It mimics the food foraging behaviour of honey bee colonies. Later, the algorithm was explored and discussed in [28; 29; 30]. An analysis of BA can be found in [31]. An overview of the major branches of the bee method was published in [32]. According to this overview, there is the basic bees algorithm (BBA), where local areas remain fixed; shrinking-based bees algorithm (ShBA), where local areas may shrink in the case of stagnation, and finally, standard bees algorithm (SBA), where, in addition to the shrinkage, there is also a procedure to abandon local areas under certain conditions. The artificial bee colony (ABC) algorithm was introduced by [PERSON] in 2005 [33]. More about this algorithm, including its performance and convergence, can be found in [34; 35]. An essential difference of the ABC algorithm from BA is that bees can leave their local area if there is not enough nectar. This can be very useful when looking for a global extremum: the algorithm can converge much faster. The undoubted advantage of these algorithms is that it can be generalized to problems of any dimension, and it can be easily be run in parallel on supercomputers. In this work, the standard bees algorithm (SBA) is modified and adapted for identifying parameters for reaction-dominated pore-scale transport. The algorithm used here is called the modified bee colony (MBC) algorithm to distinguish it from the SBA. The MBC is adapted to specifics of the considered class of problems, which are characterized by the presence of a non-convex functional, having the shape of a narrow valley with steep banks (see the plots in Section 4). The peculiarities of MBC are discussed in more details in Section 4. MBC was implemented in Python together with the other tested methods. The MBC algorithm is used to identify admissible sets of parameters in a Langmuir isotherm in the case of reaction-dominated transport. Note that the goal here is not to identify just a point in the parameter space but to identify sets (called admissible sets here) such that, for each point from an admissible set, the functional is smaller than a prescribed threshold. Such parameter identification problems are important in many industrial processes. The remainder of the paper is organized as follows. Pore-scale reactive transport is considered in Section 2. At the pore scale, single phase laminar flow described by incompressible Stokes equations and solute transport described by a convection-diffusion equation are considered. The surface reaction is accounted for in the boundary conditions. Henry and Langmuir adsorption isotherms are considered [36]. Section 3 is dedicated to the description of the used computational algorithm. Finite element method is exploited after triangulation of the computational domain. The numerical investigation of the grid convergence and sensitivity with respect to parameters is also presented in this Section. A modified bee colony algorithm is described in detail in Section 4. The algorithm has been tested on benchmark functions, and a comparative statistical analysis between MBC, SBA and ABC for 100 independent runs is presented.