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This article presents a symbiotic learning-based Grey Wolf Optimizer (SL-GWO) formulated through the introduction of symbiotic hunting and learning strategies to achieve a better trade-off between exploration and exploitation while standing immune to the curse of dimensionality. The proposed method improves the performance of the algorithm to effectively handle problems with larger dimensions while avoiding local entrapment, accelerates convergence, and improves the precision and accuracy of exploitation. SL-GWO&#x2019;s symbiotic hunting strategies provide a major overhaul to the exiting hierarchical hunting through population sub-grouping into attacking hunters and experienced hunters with individually crafted dynamic adaptive tuning. The hunting mechanisms are implemented through the inclusion of random omega wolves from the wolfpack thereby reducing the algorithm&#x2019;s excessive dependence on the three dominant wolves and enhancing the population diversity. SL-GWO is tested and validated through a series of benchmarking, engineering and real-world optimization problems and compared against the standard version of GWO, eight of its latest and state-of-the-art variants and five modern meta-heuristics. Different testing scenarios are considered to analyze and evaluate the performance of the proposed method such as the effect of dimensionality (CEC2018 benchmarking suite), convergence speeds, avoidance of local entrapment (CEC2019 benchmarking suite) and constrained optimization problems (four standard engineering problems). Furthermore, two power flow problems namely, the optimal power flow (13 cases for IEEE 30 and 57-bus system) and optimal reactive power dispatch (8 cases for IEEE 30 and 57-bus system) from the recent literature are investigated. The proposed method performed competitively compared to all its competitors with statistically significant performance while requiring lower computational times. The performance for the standard engineering problems and the power flow problems was excellent with good accuracy of the solutions and the least standard deviation rates.

Symbiotic Learning Grey Wolf Optimizer for Engineering and Power Flow Optimization Problems