  In recent years, several swarm intelligence optimization algorithms have been
proposed to be applied for solving a variety of optimization problems. However,
the values of several hyperparameters should be determined. For instance,
although Particle Swarm Optimization (PSO) has been applied for several
applications with higher optimization performance, the weights of inertial
velocity, the particle's best known position and the swarm's best known
position should be determined. Therefore, this study proposes an analytic
framework to analyze the optimized average-fitness-function-value (AFFV) based
on mathematical models for a variety of fitness functions. Furthermore, the
optimized hyperparameter values could be determined with a lower AFFV for
minimum cases. Experimental results show that the hyperparameter values from
the proposed method can obtain higher efficiency convergences and lower AFFVs.
