Differential Evolution with Better and Nearest Option for Function Optimization

TL;DR

This paper introduces NbDE, a novel differential evolution algorithm that incorporates better and nearest options inspired by whale swarm strategies, demonstrating superior convergence and accuracy on benchmark functions.

Contribution

The paper proposes a new DE variant called NbDE, integrating niching-based mutation inspired by whale swarm algorithms to enhance optimization performance.

Findings

NbDE outperforms existing algorithms in convergence speed.

NbDE achieves higher accuracy on benchmark functions.

NbDE demonstrates robustness across different dimensions.

Abstract

Differential evolution(DE) is a conventional algorithm with fast convergence speed. However, DE may be trapped in local optimal solution easily. Many researchers devote themselves to improving DE. In our previously work, whale swarm algorithm have shown its strong searching performance due to its niching based mutation strategy. Based on this fact, we propose a new DE algorithm called DE with Better and Nearest option (NbDE). In order to evaluate the performance of NbDE, NbDE is compared with several meta-heuristic algorithms on nine classical benchmark test functions with different dimensions. The results show that NbDE outperforms other algorithms in convergence speed and accuracy.