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We introduce a robust technique that allows generating optical solitons beams through a numerical approach of a variational approximation by using for the first time, to the best of our knowledge, the symbiotic organism search (SOS). This bio-inspired algorithm is able to optimize the design of beam parameters on-demand, such as amplitude and width, for several complex structured light beams in a plethora of nonlinear media. We report some SOS optimized optical structures such as fundamental, vortices and azimuthons optical beams for the case of Kerr, saturation and competing nonlinearities. We propagate the optimized optical beams obtained by SOS and we report that the beams show a self-trapped dynamics during their evolution. Furthermore, the nature of this algorithm allows a straightforward scalability for optimization of multiple parameters. Remarkably, this optimization can be used for different structured light beams at the same time, contrary to the most of numerical methods known.

Nanovectors (NVs), based on nanostructured matter such as nanoparticles (NPs), have proven to perform as excellent drug delivery systems. However, due to the great variety of potential NVs, including NPs materials and their functionalization, in addition to the plethora of molecules that could transport, this fields presents a great challenge in terms of resources to find NVs with the most optimal physicochemical properties such as particle size and drug loading, where most of efforts rely on trial and error experimentation. In this regard, Artificial intelligence (AI) and metaheuristic algorithms offer efficient of the state-of-the-art modelling and optimization, respectively. This review focuses, through a systematic search, on the use of artificial intelligence and metaheuristic algorithms for nanoparticle synthesis in drug delivery systems. The main findings are: neural networks are better at modelling NVs properties than linear regression algorithms and response surface methodology, there is a very limited number of studies comparing AI or metaheuristic algorithm, and there is no information regarding the appropriateness of calculations of the sample size. Based on these findings, multilayer perceptron artificial neural network and adaptive neuro fuzzy inference system were tested for their modelling performance with a NV dataset; finding the latter the better algorithm. For metaheuristic algorithms, benchmark functions were optimized with cuckoo search, firefly algorithm, genetic algorithm and symbiotic organism search; finding cuckoo search and symbiotic organism search with the best performance. Finally, methods to estimate appropriate sample size for AI algorithms are discussed.

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