TAHO: tri-swarm adaptive hybrid optimizer for optimal power flow in integrated transmission-distribution systems

The increasing integration of renewable energy resources and active distribution networks has significantly increased the complexity of optimal power flow (OPF) problems in integrated transmission-distribution (T&D) power systems. To address these challenges, this paper proposes a novel tri-swarm adaptive hybrid optimizer (TAHO) that integrates particle swarm optimization (PSO), grey wolf optimizer (GWO), and jellyfish search (JS) within a unified adaptive optimization framework. The proposed method effectively balances exploration and exploitation to improve convergence stability and optimization accuracy. A multi-objective OPF model is developed to minimize generation cost, power loss, and voltage deviation under operational constraints. Experimental results on integrated IEEE 30-bus and IEEE 33-bus systems demonstrate that the proposed TAHO achieves superior performance with the minimum fitness value of 0.0008, faster convergence within 75 iterations, and the lowest standard deviation of 0.0005 compared with PSO, GWO, and JS. Benchmark evaluations further confirm the robustness and strong global search capability of the proposed framework for renewable-integrated smart grid optimization and real-time OPF applications.