A theoretical framework and some promising findings of grey wolf optimizer, part I: analytical model of sampling distribution and stability analysis

TL;DR

This paper develops a theoretical framework for the grey wolf optimizer, analyzing its sampling distribution and stability, providing insights into its convergence behavior through probabilistic analysis and numerical verification.

Contribution

It introduces a novel analytical model of GWO's sampling distribution and stability, advancing theoretical understanding of its convergence properties.

Findings

Sampling distribution characteristics of GWO solutions analyzed

Order-1 and order-2 stability of GWO proved under stagnation assumption

Numerical simulations verify theoretical stability results

Abstract

This paper proposes a theoretical framework of the grey wolf optimizer (GWO) based on several interesting theoretical findings, involving sampling distribution, order-1 and order-2 stability, and global convergence analysis. In the part I of the paper, the characteristics of the sampling distribution of the new solution and the probabilistic stability of the GWO are carefully discussed based on the well-known stagnation assumption for simplification purposes. Firstly, the characteristics of the sampling distribution of the new solution, mainly related to the shape of the joint probability density function (PDF), are discussed under the assumption that the original solution before updating is constant. Then, the assumption that the original solution is constant is eliminated to perform the sampling distribution analysis, based on which several characteristics of the new solution are provided, containing the shape of the joint PDF and central moments of any positive integer order. Finally, the order-1 and order-2 stability of the GWO under stagnation assumption is introduced and proved as the inference of conclusions above, which are all verified by numerical simulations.