A Bare Bones Grey Wolf Optimizer for Global Numerical Optimization

TL;DR

This paper introduces a simplified version of the Grey Wolf Optimizer called BBGWO, which uses a probabilistic approach to improve understanding and performance in global numerical optimization tasks.

Contribution

The paper presents a novel bare bones variant of GWO that replaces complex updates with a normal distribution-based random vector, supported by theoretical analysis and simulations.

Findings

BBGWO achieves competitive optimization performance.

Theoretical analysis confirms the probabilistic update mechanism.

Simulation results validate effectiveness across benchmark problems.

Abstract

In order to better understand and analyze the currently widely used population-based metaheuristic optimization algorithms, , this paper proposes a novel computational intelligence algorithm called bare bones grey wolf optimizer (BBGWO) inspired by a bare bones mechanism. In the BBGWO, the complex updating mechanism of solutions is replaced by a random vector that obeys a normal distribution, whose mean and variance are derived by theoretically studying the probability distribution of the new solution of the original GWO. The corresponding theoretical analysis and simulation results verify the good optimization performance of the proposed BBGWO algorithm.