A meta optimisation analysis of particle swarm optimisation velocity update equations for watershed management learning

作者：Mason Karl^*; Duggan Jim; Howley Enda

来源：Applied Soft Computing, 2018, 62: 148-161.

DOI：10.1016/j.asoc.2017.10.018

• Particle swarm optimisation
• PSO
• Velocity update equation
• Constriction
• Inertia
• Meta optimisation
• Watershed management
• Function approximation
• Neural networks
• Learning

Particle swarm optimisation (PSO) is a general purpose optimisation algorithm used to address hard optimisation problems. The algorithm operates as a result of a number of particles converging on what is hoped to be the best solution. How the particles move through the problem space is therefore critical to the success of the algorithm. This study utilises meta optimisation to compare a number of velocity update equations to determine which features of each are of benefit to the algorithm. A number of hybrid velocity update equations are proposed based on other high performing velocity update equations. This research also presents a novel application of PSO to train a neural network function approximator to address the watershed management problem. It is found that the standard PSO with a linearly changing inertia, the proposed hybrid Attractive Repulsive PSO with avoidance of worst locations (AR PSOAWL) and Adaptive Velocity PSO (AV PSO) provide the best performance overall. The results presented in this paper also reveal that commonly used PSO parameters do not provide the best performance. Increasing and negative inertia values were found to perform better.

• 出版日期2018-1