Methods for comparative analysis of evolutionary design methods in software for solving multicriteria optimization problems

The relevance of the study is due to the need to improve methods for solving multi-criteria transportation problems, which represent an important class of optimization problems with a wide range of practical applications. Traditional approaches often fail to handle the computational complexity of such problems, while existing heuristic methods require additional adaptation and parameter tuning.In this regard, this paper aims to identify the most effective configurations of evolutionary algorithms for solving multi-criteria transportation problems in terms of both solution quality and speed. The leading approach to studying this problem is the comparative analysis of various configurations of evolutionary algorithms on a large set of test tasks (about 85 thousand unique tasks with 4 criteria), allowing for a comprehensive examination of the features of each algorithm under different parameters. The paper presents the results of analyzing the effectiveness of about 50 configurations of evolutionary algorithms, reveals patterns of how various parameters influence solution quality and speed, identifies optimal configurations for each type of algorithm, and justifies the advantage of a combined approach to problem-solving. The materials of the paper are of practical value for software developers in the field of logistics and transportation systems, as well as for researchers working on optimization and evolutionary design issues, as they enable the creation of more efficient automated systems for solving multi-criteria transportation problems.

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