Applying Autonomous Hybrid Agent-based Computing to Difficult Optimization Problems

Mateusz Godzik; Jacek Dajda; Marek Kisiel-Dorohinicki; Aleksander Byrski; Leszek Rutkowski; Patryk Orzechowski; Joost Wagenaar; Jason H. Moore

doi:10.1016/j.jocs.2022.101858

arXiv · 2210.13205

Applying Autonomous Hybrid Agent-based Computing to Difficult Optimization Problems

Mateusz Godzik·Jacek Dajda·Marek Kisiel-Dorohinicki·Aleksander Byrski·Leszek Rutkowski·Patryk Orzechowski·Joost Wagenaar·Jason H. Moore

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Abstract

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.

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BibTeX RIS

Mateusz Godzik, Jacek Dajda, Marek Kisiel-Dorohinicki, Aleksander Byrski, Leszek Rutkowski, Patryk Orzechowski, Joost Wagenaar, Jason H. Moore. 2022-10-24. Applying Autonomous Hybrid Agent-based Computing to Difficult Optimization Problems. https://doi.org/10.1016/j.jocs.2022.101858

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