p-PSO: A Penalized Particle Swarm Optimization Technique for Finding D-Optimal Designs with Mixed Factors in Generalized Linear Models
Researchers have developed a new optimization technique called p-PSO, designed to address the complexities of finding D-optimal designs for generalized linear models (GLMs). This method is particularly useful when dealing with mixed factors (both discrete and continuous) and the challenge of an unknown Fisher information matrix. The p-PSO approach introduces a general-purpose penalty formulation that can be integrated with various optimization algorithms, offering a more robust and efficient solution for these complex statistical problems. AI