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

Tariq Ali; Muzna Sarwar; Farrukh Jamal; Mohammad Hijji; Husam S. Samkari; Mohammed F. Allehyani; Muhammad Ayaz; Tariq Ali; Muzna Sarwar; Farrukh Jamal; Mohammad Hijji; Husam S. Samkari; Mohammed F. Allehyani; Muhammad Ayaz

doi:10.3934/math.2026683

AIMS Mathematics

2026, Volume 11, Issue 6: 16635-16671. doi: 10.3934/math.2026683

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

1.
Artificial Intelligence and Sensing Technology Research Center (AIST), University of Tabuk, Tabuk 71491, Saudi Arabia
2.
Faculty of Computers and Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia
3.
Department of Statistics, Faculty of Computing, Islamia University of Bahawalpur, Pakistan
4.
Department of Statistics, Faculty of Science, University of Tabuk, Tabuk, Saudi Arabia
5.
Department of Electrical Engineering, University of Tabuk, Tabuk 47713, Saudi Arabia

Received: 10 April 2026 Revised: 01 June 2026 Accepted: 02 June 2026 Published: 10 June 2026
MSC : 34A08, 68T07

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.
- optimal power flow,
- hybrid metaheuristic optimization,
- distribution systems,
- renewable energy integration,
- power system
Citation: Tariq Ali, Muzna Sarwar, Farrukh Jamal, Mohammad Hijji, Husam S. Samkari, Mohammed F. Allehyani, Muhammad Ayaz. TAHO: tri-swarm adaptive hybrid optimizer for optimal power flow in integrated transmission-distribution systems[J]. AIMS Mathematics, 2026, 11(6): 16635-16671. doi: 10.3934/math.2026683

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Abstract

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.

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