МЕТАЭВРИСТИКА НА ОСНОВЕ ПОВЕДЕНИЯ КОЛОНИИ БЕЛЫХ КРОТОВ

Abstract

Optimization algorithms inspired by the natural world have turned into powerful tools for solvingcomplex problems. However, they still have some disadvantages that require the study of new andmore advanced optimization algorithms. In this regard, when solving NP complete problems, there isa need to develop new methods for solving this class of problems. One of these methods can bemetaheuristics based on the behavior of a colony of white moles. This paper proposes a newmetaheuristic algorithm called the blind white moles algorithm. This algorithm was developed basedon the social behavior of blind moles in search of food and protecting the colony from intruders. Theproposed solution will be able to overcome many disadvantages of conventional optimization algorithms,including falling into the trap of local minima or a low convergence rate. The purpose of thiswork is to develop an algorithm for optimizing a complex objective function. The scientific noveltylies in the development of a genetic algorithm based on the behavior of a colony of white moles forsolving NP complete problems. The problem statement in this paper is as follows: to optimize thesearch for solutions to complex functions by applying an algorithm based on the behavior of a colonyof white moles. The practical value of the work lies in the creation of a new search architecture thatallows using the developed algorithm for the effective solution of NP complete problems, as well asconducting a comparative analysis with existing analogues. The fundamental difference from theknown approaches is in the application of a new bioinspired search structure based on the behaviorof a colony of white moles, which will allow to exclude falling into a local minimum or a low convergencerate. The presented results of the computational experiment showed the advantages of the proposedmultidimensional approach to solving the problems of placing VLSI elements in comparisonwith existing analogues. Thus, the problem of creating methods, algorithms and software for solvingNP complete problems is currently of particular relevance.