Print Email Facebook Twitter A Fast Multi-objective Evolutionary Approach for Designing Large-Scale Optical Mode Sorter Title A Fast Multi-objective Evolutionary Approach for Designing Large-Scale Optical Mode Sorter Author Panichella, A. (TU Delft Software Engineering) Di Domenico, Giuseppe (attocube systems AG) Contributor Paquete, Luís (editor) Date 2023 Abstract Spatial mode division de-multiplexing of optical signals has many real-world applications, such as quantum computing and both classical and quantum optical communication. In this context, it is crucial to develop devices able to efficiently sort optical signals according to the optical mode they belong to and route them on different paths. Depending on the mode selected, this problem can be very hard to tackle. Recently, researchers have proposed using multi-objective evolutionary algorithms (MOEAs) ---and NSGA-II in particular--- combined with Linkage Learning (LL) to automate the process of design mode sorter. However, given the very large-search scale of the problem, the existing evolutionary-based solutions have a very slow convergence rate. In this paper, we proposed a novel approach for mode sorter design that combines (1) stochastic linkage learning, (2) the adaptive geometry estimation-based MOEA (AGE-MOEA-II), and (3) an adaptive mutation operator. Our experiments with two- and three-objectives (beams) show that our approach is faster (better convergence rate) and produces better mode sorters (closer to the ideal solutions) than the state-of-the-art approach. A direct comparison with the vanilla NSGA-II and AGE-MOEA-II also further confirms the importance of adopting LL in this domain. Subject Many-objective optimizationMode SorterOptical and Photonics TechnologyEvolutionary AlgorithmsMachine learning To reference this document use: http://resolver.tudelft.nl/uuid:5ec5593d-9574-49a6-aa67-c85902fba3f3 DOI https://doi.org/10.1145/3583131.3590479 Publisher ACM/IEEE ISBN 979-8-4007-0119-1 Source GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Event Genetic and Evolutionary Computation Conference, 2023-07-15 → 2023-07-19, Lisbon, Lisbon, Portugal Series GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Part of collection Institutional Repository Document type conference paper Rights © 2023 A. Panichella, Giuseppe Di Domenico Files PDF 3583131.3590479.pdf 891.85 KB Close viewer /islandora/object/uuid:5ec5593d-9574-49a6-aa67-c85902fba3f3/datastream/OBJ/view