ALGORITHMIC THINKING AS A TOOL FOR SOLVING OPTIMIZATION PROBLEMS

Abstract

In today's world, the growing complexity of technological processes and the amount of information processed require effective methods for solving optimization problems that are relevant in various fields, from logistics to engineering. Optimization tasks involve finding the best solution among a set of options, which may include minimizing costs or maximizing productivity. Algorithmic thinking is becoming an important tool for a structured approach to solving these problems, as it allows us to analyze data, create efficient algorithms, and choose optimal strategies.

Algorithmic thinking is an important tool that allows you to analyze data, formulate clear instructions, and choose the best ways to achieve a goal. This thinking is also the basis of many modern technologies that automate processes and increase efficiency. In the context of task optimization, the main task is to find and determine the best way to use available resources according to certain criteria.

This paper explores the connection between algorithmic thinking and important scientific and practical tasks, in particular through the use of greedy algorithms, dynamic programming, and genetic algorithms. Each of these methods has its advantages and disadvantages that affect their application in practice. In particular, genetic algorithms that model evolutionary processes demonstrate a high level of adaptability and can successfully solve complex optimization problems, which makes them especially useful in the modern world.

The purpose of this paper is to reveal the essence of algorithmic thinking as a tool for solving optimization problems, to explore its basic principles and methods, and to demonstrate how this approach can be applied in practice. The main principles of algorithmic thinking include breaking down a problem into subtasks, searching for and evaluating solutions, and creating clear step-by-step instructions leading to a solution. Each of these principles makes algorithmic thinking a powerful tool for solving optimization problems, increasing its application in various fields of activity.

The conclusions of the study emphasize the importance of algorithmic thinking in process automation and effective solution of complex problems.

Further research will focus on developing hybrid methods that combine classical and modern algorithms to achieve even greater efficiency. Future prospects include the introduction of newer approaches such as machine learning and quantum computing, which will allow solving even more complex optimization problems faced by modern society. This study also aims to stimulate further research in the field of algorithmic thinking, contributing to the development of new methods and approaches for efficiently solving complex problems.