A Hybridization of Local and Global Search for Dynamic Multi-Objective Optimization Problem


Dynamic multi-objective optimization problems are challenging and currently not-well understood class of optimization problems but it is important since many real-world optimization problems change over time. When changes appear in the problem, there is a necessity to adapt to the changes in such a way that the convergence rate is sufficiently high. The work is devoted to the analysis of the efficiency of Pareto local search algorithms for dynamic multi-objective optimization problems as a method to increase the convergence rate. We propose a hybrid of global and local search algorithms, we use NSGA-2 algorithm as a global optimizer and L-BFGS-B as a local search algorithm. The IEEE CEC2018 benchmark set is used for the experimental comparison of investigated approaches. The experimental results show that the proposed hybridization of NSGA-2 and a local search algorithm can efficiently identify the Pareto front in the case of not intense changes in the environment.

The article is not prepared yet for the html view. Check back soon.

Copyright information

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

About this article

Publication Date

27 February 2023

eBook ISBN



European Publisher



Print ISBN (optional)


Edition Number

1st Edition




Cite this article as:

Rurich, M., & Sherstnev, P. (2023). A Hybridization of Local and Global Search for Dynamic Multi-Objective Optimization Problem. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 321-327). European Publisher. https://doi.org/10.15405/epct.23021.39