A Hybridization of Local and Global Search for Dynamic Multi-Objective Optimization Problem
Abstract
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.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
About this article
Publication Date
27 February 2023
Article Doi
eBook ISBN
978-1-80296-960-3
Publisher
European Publisher
Volume
1
Print ISBN (optional)
-
Edition Number
1st Edition
Pages
1-403
Subjects
Hybrid methods, modeling and optimization, complex systems, mathematical models, data mining, computational intelligence
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