Accelerating differential evolution algorithm with Gaussian sampling based on estimating the convergence points

TL;DR

This paper introduces a Gaussian sampling method based on estimating convergence points to accelerate differential evolution algorithms, demonstrating improved convergence speed on benchmark functions.

Contribution

It proposes a novel convergence point estimation and Gaussian sampling strategy to enhance differential evolution performance, adaptable to other population-based algorithms.

Findings

Accelerates DE on most CEC2013 benchmark functions.

Estimation of convergence points improves convergence speed.

Method is easily extendable to other algorithms.

Abstract

In this paper, we propose a simple strategy for estimating the convergence point approximately by averaging the elite sub-population. Based on this idea, we derive two methods, which are ordinary averaging strategy, and weighted averaging strategy. We also design a Gaussian sampling operator with the mean of the estimated convergence point with a certain standard deviation. This operator is combined with the traditional differential evolution algorithm (DE) to accelerate the convergence. Numerical experiments show that our proposal can accelerate the DE on most functions of 28 low-dimensional test functions on the CEC2013 Suite, and our proposal can easily be extended to combine with other population-based evolutionary algorithms with a simple modification.