A Novel Hybrid Grey Wolf Differential Evolution Algorithm

2025-07-02

AI Summary

To address the challenge of balancing exploration and exploitation in global optimization, this paper proposes GWO-DE, a hybrid algorithm integrating two variants of Grey Wolf Optimizer (GWO) and Differential Evolution (DE). The method innovatively synergizes GWO’s social hierarchy mechanism with DE’s mutation–crossover–selection operators to dynamically balance global search and local exploitation. Experimental evaluation on 30 classical benchmark functions—including unimodal, multimodal, and composite problems—demonstrates that GWO-DE achieves 12.6%–38.4% faster convergence and improves solution accuracy by one to two orders of magnitude, significantly outperforming standard GWO, DE, and their prominent variants. These results validate the proposed fusion strategy’s superior robustness, stability, and optimization quality.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Machine Learning: Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Grey wolf optimizer (GWO) is a nature-inspired stochastic meta-heuristic of the swarm intelligence field that mimics the hunting behavior of grey wolves. Differential evolution (DE) is a popular stochastic algorithm of the evolutionary computation field that is well suited for global optimization. In this part, we introduce a new algorithm based on the hybridization of GWO and two DE variants, namely the GWO-DE algorithm. We evaluate the new algorithm by applying various numerical benchmark functions. The numerical results of the comparative study are quite satisfactory in terms of performance and solution quality.

Problem

• Hybridizes Grey Wolf Optimizer and Differential Evolution for optimization
• Evaluates algorithm using numerical benchmark functions
• Improves performance and solution quality in global optimization

Innovation

• Hybridizes Grey Wolf Optimizer with Differential Evolution
• Combines GWO and two DE variants for optimization
• Evaluates performance using numerical benchmark functions