A Fast Multi-objective Evolutionary Approach for Designing Large-Scale Optical Mode Sorter

A. Panichella, Giuseppe Di Domenico

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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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.
Original languageEnglish
Title of host publicationThe Genetic and Evolutionary Computation Conference
Publication statusAccepted/In press - Jul 2023
EventThe Genetic and Evolutionary Computation Conference - Lisbon, Lisbon, Portugal
Duration: 15 Jul 202319 Jul 2023


ConferenceThe Genetic and Evolutionary Computation Conference
Abbreviated titleGECCO
Internet address


  • Many-objective optimization
  • Mode Sorter
  • Optical and Photonics Technology
  • Evolutionary Algorithms
  • Machine learning


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