Algorithm for estimating the convergence of stochastic Pareto optimization

The research is devoted to the development of an algorithm for estimating the convergence of stochastic Pareto optimization. The relevance of the work is due to the need to reduce the computational costs that arise with large multi-criteria calculations, where it is necessary to take into account many conflicting criteria to find optimal solutions. One of the problems in this context is finding a compromise between the accuracy of the Pareto front and the resources needed to calculate it. In multicriteria optimization, it is important to evaluate convergence in order to avoid an excessive number of iterations, which may be ineffective in terms of improving the result. The problem lies in finding the optimal number of iterations, at which the Pareto front reaches sufficient accuracy, and further iterations do not lead to a significant improvement in the quality of solutions. The aim of the study is to develop an algorithm that allows us to evaluate the convergence of the Pareto front and determine when it is possible to complete the optimization process without losing the quality of solutions. The results can be useful for specialists involved in multi-criteria optimization tasks and the development of algorithms based on stochastic conditions.