Balancing competing objectives in bigel formulations using many-objective optimization algorithms and different decision-making methods.

Many-objective optimization, which deals with balancing multiple competing objectives to find compromised solutions, is essential for solving complex problems. This study explores evolutionary algorithms for optimizing the microstructural, rheological, stability, and drug release properties of bigel systems formulated using structured almond oil, mixed organogelators, and carbopol. The oleogel was identified as the dispersed phase, with droplet sizes ranging from 1.43 µm to 7.37 µm, indicating improved characteristics compared to other bigels. Each formulation exhibited non-Newtonian shear-thinning and thixotropic behaviors, which were positively influenced by the proportions of the excipients. After undergoing multiple stress cycles, highly concentrated bigels exhibited phase separation. Unexpectedly, bigels with lower viscosity exhibited reduced rates of drug release. FT-IR and HPLC analyses confirmed the compatibility and stability of drug-excipient interactions, with impurities remaining below 4%. This study emphasizes the complex interactions within mixed lipid-based bigels, requiring many-objective optimization techniques to address conflicting objectives. The objectives of optimization involve simultaneously minimizing microstructural properties while maximizing structural recovery and drug release properties. This led to conflicting objectives, where achieving higher structural recovery did not align with the desired drug release rate. Additionally, more stable formulations did not meet the optimal microstructural objectives. To resolve these conflicts, an RSM-MaOEAs approach was applied, employing various decision-making methods. Among EAs, RSM-RVEA notably achieved exceptional convergence. Furthermore, three MaOEAs-integrated decision-making methods-WSM, WPM, NED-and the RSM-desirability, offered potential solutions. Overall, this research proposes a robust framework for compromising the bigels' performance and stability, with broader applications in drug delivery and related fields.

Dragonfly algorithm-support vector machine approach for prediction the optical properties of blood.

Knowledge of the optical properties of blood plays important role in medical diagnostics and therapeutic applications in laser medicine. In this paper, we present a very rapid and accurate artificial intelligent approach using Dragonfly Algorithm/Support Vector Machine models to estimate the optical properties of blood, specifically the absorption coefficient, and the scattering coefficient using key parameters such as wavelength (nm), hematocrit percentage (%), and saturation of oxygen (%), in building very highly accurate Dragonfly Algorithm-Support Vector Regression models (DA-SVR). 1000 training and testing sets were selected in the wavelength range of 250-1200 nm and the hematocrit of 0-100%. The performance of the proposed method is characterized by high accuracy indicated in the correlation coefficients (R) of 0.9994 and 0.9957 for absorption and scattering coefficients, respectively. In addition, the root mean squared error values (RMSE) of 0.972 and 2.9193, as well as low mean absolute error values (MAE) of 0.2173 and 0.2423, this result showed a strong match with the experimental data. The models can be used to accurately predict the absorption and scattering coefficients of blood, and provide a reliable reference for future studies on the optical properties of human blood.