METAHEURISTICS BASED ON THE BEHAVIOR OF A COLONY OF WHITE MOLES
Abstract
Optimization algorithms inspired by the natural world have turned into powerful tools for solving
complex problems. However, they still have some disadvantages that require the study of new and
more advanced optimization algorithms. In this regard, when solving NP complete problems, there is
a need to develop new methods for solving this class of problems. One of these methods can be
metaheuristics based on the behavior of a colony of white moles. This paper proposes a new
metaheuristic algorithm called the blind white moles algorithm. This algorithm was developed based
on the social behavior of blind moles in search of food and protecting the colony from intruders. The
proposed 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 this
work is to develop an algorithm for optimizing a complex objective function. The scientific novelty
lies in the development of a genetic algorithm based on the behavior of a colony of white moles for
solving NP complete problems. The problem statement in this paper is as follows: to optimize the
search for solutions to complex functions by applying an algorithm based on the behavior of a colony
of white moles. The practical value of the work lies in the creation of a new search architecture that
allows using the developed algorithm for the effective solution of NP complete problems, as well as
conducting a comparative analysis with existing analogues. The fundamental difference from the
known approaches is in the application of a new bioinspired search structure based on the behavior
of a colony of white moles, which will allow to exclude falling into a local minimum or a low convergence
rate. The presented results of the computational experiment showed the advantages of the proposed
multidimensional approach to solving the problems of placing VLSI elements in comparison
with existing analogues. Thus, the problem of creating methods, algorithms and software for solving
NP complete problems is currently of particular relevance.
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