2025/07/02 by Ioannis D. Bougas, Bougas, Ioannis D., Pavlos Doanis +18
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #Applied Physics (physics.app-ph) #B.7.1 #B.7.2 #B.8.2 #C.2.1 #Computational Physics (physics.comp-ph) #D.1.0 #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #I.6.3 #J.2 #J.6 #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Systems and Control (eess.SY) #Vehicle Routing Optimization Methods #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2507.03022
openalex publication_date 2025/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.