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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...

Using swarm intelligence algorithms to determine the composition of a multi-project

One of the promising ways to reduce the dependence of domestic industry on the supply of critical goods, components and raw materials necessary for the construction and effective functioning of multi-stage production and technological chains is to intensify processes for their import substitution, including through various measures of state support. However, the critical need for a wide range of products requires the selection of the most “promising” projects for inclusion in program-target documents using a set of criteria (sometimes even non-financial). As a result, there arises an urgent scientific and practical task of developing approaches to the formation of multi-projects (a set of projects) that can qualify for state support under various programs to reduce import dependence and overcome the technological backwardness of Russian industry, based on the use of modern economic and mathematical methods. In the application to this task, a multi-project can be represented as a “set” (unrelated projects), a “chain” (rigid sequence of projects) or a “network” (projects with complex logical-temporal interrelations). The specifics of each type determine the conditions and impose restrictions on the processes of selecting components for inclusion in their composition, which consists of finding the best combination of projects and/or programs, i. e., it is reduced to a task of conditional multidimensional optimization. In the absence of a requirement to find a “strictly optimal” composition, one can use metaheuristic methods that are capable of finding solutions close to these in an “acceptable” time. Among them, the largest and most well-known class are swarm intelligence algorithms based on the principles of collective behavior of a population of living organisms. To form the composition of multi-projects, the article proposes to use algorithms inspired by the collective behavior of a pack of wolves (Grey Wolf Optimizer) and a school of fish (Fish School Search) to satisfy their food needs. To increase the efficiency of their use for solving the task of finding the best composition of a “set” and “network” of projects, their hybridization with fuzzy logic methods (in particular, fuzzy clustering and fuzzy-logical inference) was proposed. Read more...

Fuzzy bioinspired method for forming a set of candidates for linear positions

Line personnel occupy the vast majority of positions in many organizations, which determines the importance of timely and successful filling of such vacancies. The search for candidates for such positions is carried out through mass recruitment, which is characterized by high labor intensity, budgetary and time constraints, and the need for regular repetition due to high staff turnover rates. The noted features make it impossible to carry out this process without the use of modern software. Since mass recruitment does not require finding the best candidate for each vacancy and is limited to searching for specialists based on formal criteria from their resume, the main share of labor and time costs falls on the primary selection of candidates. Existing software does not have sufficient functionality to effectively automate this process. Given the need to process large volumes of multidimensional data, they do not provide a comprehensive accounting of different types of candidate characteristics and automatic adjustment of selection criteria taking into account their priority for the vacancy being filled. To solve the problem, an automated method for forming a set of candidates for linear positions was developed. It is based on the integrated use of an adaptive neuro-fuzzy inference system and a bioinspired algorithm inspired by the behavior of a fish school. The developed hybrid method was implemented as a computer program using the Python language. The results of its testing showed the convergence of the optimization algorithm, and their comparison with manual selection confirmed the prospects for using it to solve tasks of mass recruitment of line personnel. Read more...