Dynamic optimization in large-scale networks, such as Internet of Things (IoT) and wireless sensor networks (WSNs), demands adaptive and decentralized algorithms to manage energy, trust, and routing efficiently. This paper proposes a novel bio-inspired optimization approach, namely the Hybrid Termite-Tuna Swarm Algorithm (HTTSA), which combines the decentralized search capability of termite behavior with the exploration-exploitation balance of Tuna Swarm Optimization (TSO). The termite-inspired component performs localized pheromone-based discovery to construct viable routing candidates, while the tuna-inspired module performs global search to converge on optimal paths based on multi-objective metrics such as energy, trust, and hop count. Extensive simulations demonstrate that HTTSA outperforms existing algorithms in terms of network lifetime, energy efficiency, and resilience to dynamic changes. The proposed model is especially suited for privacy-aware, energy-constrained environments in IoT applications.