Nanostructured Biosensors: Influence of Adhesion Layer, Roughness and Size on the LSPR: A Parametric Study

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Selective and Collaborative Optimization Methods for Plasmonics: A Comparison

International audienceIn this paper, we optimize the size parameters of hollow nanospheres and nanoshells used in cancer photothermal therapy and we focus on two practical therapy cases: the visible range for shallow cancer and the near infrared for deep cancer. For this, we consider analytical models: the Mie theory for coated spheres. The investigated optimization methods are the Evolutionary Method (EM) and the Particle Swarm Optimization (PSO) which are based on competitiveness and collaborative algorithms, respectively. A comparative study is achieved by checking the efficiency of the optimization methods, to improve the nanoparticles efficiency

Nanoshells for photothermal therapy: a Monte-Carlo based numerical study of their design tolerance

The optimization of the coated metallic nanoparticles and nanoshells is a current challenge for biological applications, especially for cancer photothermal therapy, considering both the continuous improvement of their fabrication and the increasing requirement of efficiency. The efficiency of the coupling between illumination with such nanostructures for burning purposes depends unevenly on their geometrical parameters (radius, thickness of the shell) and material parameters (permittivities which depend on the illumination wavelength). Through a Monte-Carlo method, we propose a numerical study of such nanodevice, to evaluate tolerances (or uncertainty) on these parameters, given a threshold of efficiency, to facilitate the design of nanoparticles. The results could help to focus on the relevant parameters of the engineering process for which the absorbed energy is the most dependant. The Monte-Carlo method confirms that the best burning efficiency are obtained for hollow nanospheres and exhibit the sensitivity of the absorbed electromagnetic energy as a function of each parameter. The proposed method is general and could be applied in design and development of new embedded coated nanomaterials used in biomedicine applications

Convergence criteria for the particle swarm optimization in a full iterative process

International audienceAlthough the theoretical aspects of the particle swarm optimization (PSO) seem to be forsaken, the few previous modeling studies -even with some assumptions- enlarged our knowledge of the PSO process. Here, we suggest a new model of PSO where all the N particles of the swarm and their components are considered. The iterative process is formulated by a 3N×3N block triangular matrix and its spectral radius is evaluated and displayed. Besides, the convergence related parametrization criteria are derived. Compared to previous results, a more restrictive acceleration coefficients criterion is found. Simulations are then carried out on CEC 2017 benchmark functions using eight PSO variants and show better results when considering the more restrictive criterion

Initiation à l’optimisation : métaheuristiques: Problèmes à variables continues

International audienceLes métaheuristiques sont parmi les méthodes d’optimisation les plus faciles à mettre en œuvre pour trouver la solution à des problèmes difficiles voire impossibles à résoudre directement, en s’inspirant de phénomènes issus de la nature et des sciences.Douze méthodes avec variantes sont présentées et les codes en Matlab/GNU octave sont donnés : GA (génétique), DE (évolution différentielle), BBO (biogéographie), RS (recuit simulé), GSO (Gravitationnel), CRO (réaction chimique), PSO (essaim de particules), LUC (lucioles), ABC (colonies d’abeilles artificielles), GWO (loup gris), ACO (colonies de fourmis), BSO (brainstorming).Elles sont caractérisées, comparées et les outils fournis permettent de les combiner, les modifier ad libitum afin de les adapter à des problèmes réels. Des applications à la thermique, l’électronique, l’agriculture, la mécanique permettent d’étendre leur domaine d’application à la résolution de problème inverse, à l’ajustement de modèle à des résultats expérimentaux et à la propagation d’incertitudes

Particle Swarm Optimization with Adaptive Inertia Weight

International audienceIn this paper, a new PSO algorithm with adaptive inertia weight is introduced for global optimization. The objective of the study is to balance local search and global search abilities and alternate them through the algorithm progress. For this, an adaptive inertia weight is introduced using a feedback on particles' best positions. The inertia weight keeps varying to alternate exploration and exploitation. Tests are carried on a set of thirty test functions (the CEC 2014 benchmark functions) and compared with other settings of inertia weight. Results show that the new algorithm is very competitive mainly when increasing the dimension of the search space

Optimized nanocage for cancer photothermal therapy and comparison with other nanoparticles

International audienceThe purpose of this study is to get more efficient gold nanocages for photothermal therapy (PTT) of cancer. Therefore a numerical maximization of the absorption efficiency (generating heat) is achieved. Two therapeutic cases (using visible and infrared laser) are considered. The optimization leads to an improved absorption of the nanocages compared with previous studies. The optimized nanocages are also compared with other gold nanoparticles (nanorods, hollow nanospheres and nanoshells) and are shown to be more efficient when infrared light is used

Chapter Nanostructured Biosensors: Influence of Adhesion Layer, Roughness and Size on the LSPR: A Parametric Study

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Model and optimization of nanoantenna

International audienc

Model and optimization of nanoantenna

International audienc