Visualizing and Characterizing the Parameter Configuration Landscape of Particle Swarm Optimization using Physical Landform Classification

Abstract

When designing effective parameter tuning and/or self-adaptive mechanisms for meta-heuristic optimizers, any insights about the configuration process and its associated landscape are of great benefit. Recently, the parameter configuration landscape (PCL) was proposed as a mechanism to formally study and characterize the landscape induced by the control parameter values of meta-heuristic search techniques. As an extension, the use of geomorphon landform types to further characterize and visualize the PCL was recently proposed. This study adopts the geomorphon classification scheme and applies it to particle swarm optimization (PSO). The methodology is applied on 20 minimization benchmark problems with various problem dimensions and swarm sizes, thereby providing deep insights into the PCL associated with PSO.