The impact of particles initialization in PSO: Parameter estimation as a case in point

Despite the intense research focused on the investigation of the functioning settings of Particle Swarm Optimization, the particles initialization functions - determining the initial positions in the search space - are generally ignored, especially in the case of real-world applications. As a matter of fact, almost all works exploit uniform distributions to randomly generate the particles coordinates. In this article, we analyze the impact on the optimization performances of alternative initialization functions based on logarithmic, normal, and lognormal distributions. Our results show how different initialization strategies can affect - and in some cases largely improve - the convergence speed, both in the case of benchmark functions and in the optimization of the kinetic constants of biochemical systems.