A New Galactic Swarm Optimization Algorithm Enhanced with Grey Wolf Optimizer for Training Artificial Neural Networks

Abstract

This paper proposes a new Galactic Swarm Optimization (GSO) algorithm enhanced with Grey Wolf Optimizer (GWO).
The proposed algorithm is used to train a feedforward Neural Network for function approximation. Galactic swarm optimization is
a popular swarm algorithm that has been used to solve optimization problems. It is motivated by stars movement and the
superclusters of a galaxy in the universe. The algorithm allows using multiple levels of exploitation and exploration of search
space. At the explorative level, different sub-populations independently explore search space and at the exploitation level, the best
solution of different sub-populations is considered as a super swarm and moves towards finding the best solution position found by
the super swarm. The algorithm uses Particle Swarm Optimization (PSO) algorithm’s update equation in both levels. PSO has been
proven to get stuck in the local minimum due to its ability to converge prematurely. In this work, Galactic swarm optimization
enhanced with grey wolf optimizer is proposed. The use of the entire pack of wolves for exploring the search space in the GWO has
proven to escape local minima. Thus, the GSO’s explorative phase is done with the GWO and for the exploitation phase quickly
converging PSO is used. The proposed algorithm ability is tested by training a feedforward neural network for function
approximation of benchmark optimization problems. The proposed GSOGWO outperformed the classical GSO in most of the
functions.