.png){kind=icon}
.png){kind=icon}
Return
SOCIAL: Social network optimization algorithm via centrality and influence-aware learning
.png){kind=icon}
OA
AI
DOI：10.1016/j.asoc.2026.114914.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• SOCIAL: small-world search via centrality-weighted knowledge diffusion. • Multi-phase schedule shifts from exploration to elite-guided exploitation. • Competitive or superior to 16 metaheuristics on 23 benchmarks, six engineering tasks. • Interpretable search via betweenness, influence propagation, and elite memory. • Reduced parameter sensitivity, qualitative complexity, and convergence analysis.
Keywords:
Metaheuristic optimization
Social network algorithms
Centrality
Influence propagation
Graph-based search
Exploration–exploitation
Adaptive learning
Benchmark functions
Materials science
Cheminformatics
Materials informatics
MOF discovery
Engineering optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
Organization
View more organizations