SOCIAL: Social network optimization algorithm via centrality and influence-aware learning

We present SOCIAL (Social Network Optimization Algorithm via Centrality and Influence-based Learning), a structure-aware metaheuristic that reframes black-box engineering optimization as social-network analysis on a small-world graph of candidate solutions. Each solution is a node, and edges specify local neighbor interactions; information flow is governed by an influence-diffusion score that combines structural centrality (betweenness/bridge potential) with relative fitness, enabling agents to preferentially learn from solutions that are both well-positioned in the network and high-quality in the search space. A time-scheduled learning policy shifts from network-driven exploration toward elite-guided exploitation, with adaptive mutation and periodic population synchronization to prevent stagnation while preserving diversity. This networked view yields interpretable search dynamics—identifying leaders, followers, and critical bridges—together with scalable communication over sparse graphs. We assess SOCIAL on 23 benchmark functions and six constrained engineering design problems (gear train, pressure vessel, welded beam, speed reducer, composite laminate, and FGM beam), demonstrating robust performance and competitive ranking-based evaluation against contemporary optimizers. SOCIAL achieves particularly strong results on multimodal, discontinuous, and constraint-dominated problems, while maintaining stability and feasibility rates. The algorithm’s network-based architecture makes it particularly well-suited for materials science and cheminformatics applications, where candidate structures can be modeled as nodes in similarity graphs, enabling optimization of graph-model hyperparameters, sampling policies, and candidate exploration strategies in materials design. The key novelty is using the social-network structure itself as the learning mechanism, providing a general and explainable optimizer for engineering problems where gradients are unavailable, objectives are nonconvex or noisy, and variables are mixed discrete–continuous. • 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.