METAHEURISTIC OPTIMIZATION METHOD BASED ON THE STEM CELL BEHAVIOR MODEL

Abstract

The paper discusses optimization methods that are based on processes occurring in nature. Such methods have become increasingly used to solve complex problems. However, such methods have some drawbacks, which stimulates the development of new and more advanced optimization methods. Solving NP complete problems requires optimal methods that will meet all design requirements, so there is a need to develop new and more advanced methods for solving this class of problems. As such a method, the authors propose an optimization method based on a model of the behavior of stem cells in the natural environment. The conducted studies of the proposed method provide solutions that can overcome many of the shortcomings of standard optimization approaches, such as getting into the local optimum or low convergence rate of the algorithm based on the method under consideration. The purpose of this work is to develop an optimization method and an algorithm based on it for solving a complex objective function. The scientific novelty lies in the development of an optimization method based on the stem cell behavior model for solving NP complete problems. The aim of the work is to create conditions for theoptimal search for a solution to complex functions by applying the search method and, based on it, an algorithm for the behavior of stem cells. The practical value of the work lies in the development of a new metaheuristic optimization method for the efficient solution of NP complete problems. Also in the work, a comparative analysis with well-known competitors was carried out. The main difference of the proposed method from other known methods is the use of a new approach of bioinspired search based on the behavior of stem cells, which, as shown by practical comparison, has an advantage over known analogues. The results of a practical comparison of methods and algorithms based on them showed the advantages of the approach proposed in the work on known test functions. After analyzing the problem of creating methods, algorithms and software for solving NP complete problems, we can conclude that the development of such approaches is currently an urgent task.