Directions of hybridization of swarm intelligence and fuzzy logic algorithms for solving optimization problems in socio-economic systems

Effective functioning of complex socio-economic systems in conditions of uncertainty is impossible without solving many problems of supporting management decision-making. These include improving the quality of manufactured products, reducing production costs, ensuring energy and resource conservation, reducing transportation costs, increasing the reliability of the supply chain, forming a balanced portfolio of projects, and others. Their mathematical formulation in a typical case requires searching for a global extremum of the objective function; in the case of a multi-criteria formulation, it involves convolutions of criteria that must be met taking into account various constraints. In this case, finding an optimal solution is usually not necessary, and a result close to it is considered acceptable. Some of the most popular methods for solving problems in this simplified formulation include stochastic methods, which allow us to obtain a solution in 102–103 times less time than the execution time of algorithms based on exhaustive search. Of particular interest recently has been metaheuristic methods, which are inspired by the cooperative behavior of a decentralized self-organizing colony of living organisms (bees, ants, bacteria, cuckoos, wolves, etc.) to achieve certain goals, usually to satisfy food needs. According to the relatively recently proven “no free lunch” theorem, there is no universal algorithm capable of producing better results regardless of the problem being solved. For this reason, the focus of developers' efforts is shifting toward creating and improving specialized algorithms. This paper aims to establish approaches to constructing methods based on swarm intelligence and fuzzy logic algorithms. Based on their classification and analysis, possible directions for the “development” of swarm intelligence algorithms at various stages of their implementation (initiation of a population, migration of individuals, quality assessment and screening of unpromising solutions) are proposed by introducing elements of fuzziness to increase their efficiency in solving problems of multidimensional optimization of parameters of complex socio-economic systems.