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Bulletin of Abai KazNPU. Series of Physical and mathematical sciences

OVERVIEW OF HEURISTIC AND METAHEURISTIC ALGORITHMS

Published September 2020

123

184

D Nurserik+
Kazakh National University named after Al-Farabi, Almaty
F.R Gusmanova+
Kazakh National University named after Al-Farabi, Almaty,
G.А Abdulkarimova+
Kazakh National Pedagogical University after Abai, Almaty,
Қ.S Dalbekova+
University of international business, Almaty
Kazakh National University named after Al-Farabi, Almaty
Kazakh National University named after Al-Farabi, Almaty,
Kazakh National Pedagogical University after Abai, Almaty,
University of international business, Almaty
Abstract

The article discusses the use of heuristic algorithms for optimization problems. The algorithms for stochastic
optimization are described, which constitute the main properties of the metaheuristic and its classes. Evolutionary
algorithms are described in general terms. In particular, the main steps and properties of genetic algorithms are presented.
The main goal of this article is to solve the vehicle routing problem using a metaheuristic algorithm. The vehicle
routing problem is a complex combinatorial NP-complete optimization problem. It is shown that the metaheuristic
approach to solving the problem allows one to obtain a suboptimal solution without examining the entire space of possible
solutions. The genetic algorithm belongs to the group of evolutionary algorithms. The definitions are briefly given to the
terms characteristic of the genetic algorithm: gene, chromosome, personality (descendant), population, descendant
operators, crossing, mutation, crossover. Application of the theory of finite automata in a genetic algorithm is described.
The terminology and scheme of the genetic algorithm for solving various problems are proposed.

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How to Cite

[1]
Нұрсерік, Д., Гусманова, Ф., Абдулкаримова, Г. and Дальбекова, Қ. 2020. OVERVIEW OF HEURISTIC AND METAHEURISTIC ALGORITHMS. Bulletin of Abai KazNPU. Series of Physical and mathematical sciences. 71, 3 (Sep. 2020), 242–247. DOI:https://doi.org/10.51889/2020-3.1728-7901.37.