Methods of optimizing the arrangement of vector graphic objects on the plane

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Паламарчук Д. Ю., Tymchenko O. V., Демченко В. О. № 2 (86) 40-48 Image Image

The article is dedicated to the study and comparative analysis of three optimization methods: simulated annealing, simulation modelling, and genetic algorithm, in the context of the problem of optimal placement of vector graphic objects on a plane. The complexities encountered when solving this problem and the specifics of working with vector images are described. The analysis of the latest research and publications is conducted, where the essence of optimal solutions and methods for their finding are considered. A mathematical model of the problem of optimal placement of objects on a plane is presented. The structure, features, algorithms, advantages and disadvantages of the methods of simulated annealing, simulation modelling, and genetic algorithm are considered. The choice of method should be based on the specific requirements of the problem, including execution time, accuracy, global search capability, and ease of implementation. The application of the simulated annealing method shows its effectiveness in problems with a large search space, but requires a well- chosen cooling schedule for best results. Simulation modelling shows its suitability for systems with a large number of parameters and a high degree of uncertainty, although it can be time-consuming. The genetic algorithm proves to be effective for finding optimal solutions in problems that allow natural encoding of solutions as chromosomes, but requires the correct definition of genetic operators for each specific problem. Conclusions are provided based on the results of research of these methods as potential solutions for optimizing the placement of vector graphic objects on a plane. This research aims to assist scientists and engineers in selecting the most effective method for solving a specific problem, as well as to stimulate further research in the field of optimization of placement of vector graphic objects.

Keywords: optimization, simulation modelling, vector graphic objects, genetic algorithm, annealing simulation method, comparative analysis.

doi: 10.32403/0554-4866-2023-2-86-40-48


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