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Directions for modifying the artificial bee colony algorithm to optimize control parameters for complex systems

In recent years, bioinspired algorithms based on the use of a population approach and a probabilistic search strategy have become especially popular among researchers involved in multidimensional and multicriteria optimization. Such algorithms are based on the principles of cooperative behavior of a decentralized self-organizing colony of living organisms (bees, ants, birds, etc.) to achieve certain goals (for example, to meet nutritional needs). However, their practical application encounters a number of difficulties leading to a decrease in convergence. This article discusses the possibility of modifying the artificial bee colony algorithm by using a hybridization strategy with various data mining methods. One of these difficulties is the lack of a reasonable approach to determining initial search positions. As a solution, it is proposed to divide the population into clusters, the centers of which will be the initial positions. The need for interaction between individuals makes it advisable to use fuzzy clustering, which allows the formation of intersecting clusters. Another difficulty is associated with the choice of “free” parameters, for which the authors have not developed recommendations for choosing their optimal values. To solve this problem, it is proposed to use the idea of coevolution, which consists in the parallel launch of several interacting subpopulations, for each of which different “settings” are applied. The proposed algorithm is applicable to multidimensional optimization tasks, in which it is necessary to find such a combination of different types of elements belonging to some “large” population that will ensure the achievement of the maximum effect under given restrictions. Examples of such tasks are determining the species and quantitative composition of plants to form the terrestrial ecosystem of a carbon farm or mass recruiting, which consists of selecting a large number of personnel for the same positions. Read more...

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. Read more...

Neuro-fuzzy model of resource provision of innovative activity of an industrial enterprise

A neuro-fuzzy model of resource provision of innovative activity of an industrial enterprise is proposed. The model implements a two-stage procedure for describing and managing innovative activity of an industrial enterprise: at the first stage, interaction resources are classified based on the supplemented VRIO analysis of the interaction profile; at the second stage, an innovative activity strategy is selected. The neuro-fuzzy model of resource provision is based on stacking of private machine learning models, such as the k-nearest neighbors method, random forest, and multilayer perceptron. The classification results of private models are combined using a trained tree of fuzzy inference systems that performs the final classification, which ensures an increase in its accuracy compared to individual private models. A distinctive feature of the model is the use of a fuzzy logical inference system to assess the probability of resource availability used in planning the need for it, which allows taking into account expert judgments as input data. Testing of the neuro-fuzzy model, carried out in the MatLab software system using the example of solving the problem of assessing the resource provision of an innovation process during the interaction of a regional instrument-making enterprise with one of the counterparties, demonstrated the model’s performance and high accuracy of classifying the resources of innovative interaction. Read more...