Evolutionary multi-agent systems (EMASs) are very good at dealing with difficult, multi-dimensional problems, their efficacy was proven theoretically based on analysis of the relevant Markov-Chain based model. Now the research continues on introducing autonomous hybridization into EMAS. This paper focuses on a proposed hybrid version of the EMAS, and covers selection and introduction of a number of hybrid operators and defining rules for starting the hybrid steps of the main algorithm. Those hybrid steps leverage existing, well-known and proven to be efficient metaheuristics, and integrate their results into the main algorithm. The discussed modifications are evaluated based on a number of difficult continuous-optimization benchmarks.
@article{arxiv.2210.13205,
title = {Applying Autonomous Hybrid Agent-based Computing to Difficult Optimization Problems},
author = {Mateusz Godzik and Jacek Dajda and Marek Kisiel-Dorohinicki and Aleksander Byrski and Leszek Rutkowski and Patryk Orzechowski and Joost Wagenaar and Jason H. Moore},
journal= {arXiv preprint arXiv:2210.13205},
year = {2022}
}