Wireless mesh networks (WMNs) have emerged as a promising solution for resilient, scalable, cost-effective broadband connectivity in scenarios such as disaster recovery, smart cities and rural deployments. However, inherent characteristics like dynamic topology, interference, limited spectrum availability and heterogeneous Quality of Service (QoS) demands create design challenges with trade-offs between conflicting performance goals. These interdependencies make Multi-Objective Optimization (MOO) a critical approach in WMN research, enabling simultaneous consideration of multiple objectives, including maximizing throughput, minimizing latency, reducing energy consumption and improving fairness. The objective of this survey is to systematically review, classify and analyze MOO techniques for WMNs, identify research trends and underexplored areas and highlight potential directions for future work. We categorize existing approaches into Integer Linear Programming (ILP), heuristic, metaheuristic, evolutionary, learning-based and hybrid algorithms. Our analysis of sixteen design aspects shows heuristic methods are most applied (32%), followed by metaheuristic (26%), hybrid (21%), evolutionary (11%), learning-based (8%) and ILP (2%). Throughput-centric optimization dominates (44%), while security, mobility and power control remain underexplored (<5%). We also identify gaps in multi-objective interdependencies, with previous studies covering only 24.22% of potential relationships studied. Our findings advocate integrated optimization of mobility-aware routing and cross-layer security to enhance scalability and robustness of next-generation WMNs.
Amir et al. (Thu,) studied this question.