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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...

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...