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Particle swarm optimization and evolutionary methods for plasmonic biomedical applications

Abstract : In this paper the Evolutionary Method (EM) and the Particle Swarm Optimization (PSO), which are based on competitiveness and collaborative algorithms respectively, are investigated for plasmonic design. Actually, plasmonics represents a rapidly expanding interdisciplinary field with numerous devices for physical, biological and medicine applications. In this study, four EM and PSO algorithms are tested in two different plasmonic applications: design of surface plasmon resonance (SPR) based biosensors and optimization of hollow nanospheres used in curative purposes (cancer photothermal therapy). Specific problems-in addition of being multimodal and having different topologies are related to plasmonic design; therefore the most efficient optimization method should be determined through a comparative study. Results of simulations enable also to characterize the optimization methods and depict in which case they are more efficient.
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https://hal.inria.fr/hal-01810029
Contributor : Thomas Grosges <>
Submitted on : Thursday, June 7, 2018 - 2:00:08 PM
Last modification on : Saturday, February 15, 2020 - 1:52:11 AM

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Sameh Kessentini, Dominique Barchiesi, Thomas Grosges, Marc Lamy de la Chapelle. Particle swarm optimization and evolutionary methods for plasmonic biomedical applications. 2011 IEEE Congress on Evolutionary Computation (CEC), Jun 2011, New Orleans, United States. ⟨10.1109/CEC.2011.5949903⟩. ⟨hal-01810029⟩

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