Applying Autonomous Hybrid Agent-based Computing to Difficult Optimization Problems

• URL: http://arxiv.org/abs/2210.13205v1
• Date: Mon, 24 Oct 2022 13:28:35 GMT
• Title: Applying Autonomous Hybrid Agent-based Computing to Difficult Optimization Problems
• Authors: Mateusz Godzik, Jacek Dajda, Marek Kisiel-Dorohinicki, Aleksander Byrski, Leszek Rutkowski, Patryk Orzechowski, Joost Wagenaar, Jason H. Moore
• Abstract summary: This paper focuses on a proposed hybrid version of the EMAS. It 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.
• Score: 56.821213236215634
• License: http://creativecommons.org/licenses/by/4.0/
• 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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