Population of Hyperparametric Solutions for the Design of Metaheuristic Algorithms: An Empirical Analysis of Performance in Particle Swarm Optimization

Particle Swarm Optimization (PSO) is one of the most famous swarm-based algorithms used for solving optimization problems. PSO has received growing attention within many fields of the research community. Since its inception, some prominent improvements have been created. Within the broad spectrum of proposals that have emerged in the last few decades, improvements have been made to swarm initialization; new parameters have been introduced, such as the constraint on the inertia weight coefficient, and even mutation operators have been introduced to the PSO. However, the PSO has drawbacks and shortcomings, such as lack of convergence, loss of diversity, or stagnation at local minima. This paper proposes a population-based approach to hyperparametric solutions; the central premise is that each of the swarm particles has different parameters so that each has unique characteristics to promote exploitation-exploration and guide a heuristic with healthy diversity; empirical analysis and statistical tests performed on the proposed algorithm show the feasibility of the approach compared to improved versions of PSO found in the literature.