A novel memory-based artificial gorilla troops optimizer for installing biomass distributed generators in unbalanced radial networks

Optimizing unbalanced distribution networks through the strategic integration of distributed generators (DGs)
has long been recognized as a significant challenge. Selecting the optimal sizes and locations for these generators
is crucial for minimizing network power loss and enhancing voltage profiles. The previously published methods
have been plagued by issues such as slow convergence rates, entrapment in local optima, complexity, and
extensive computational requirements. Addressing these limitations, this paper introduces an efficient methodology:
the Memory-based Artificial Gorilla Troops Optimizer (MGTO). This approach leverages memory-based
mechanisms to enhance exploration and decision-making, facilitating the seamless integration of various biomass
DGs (BDGs) into unbalanced IEEE 37-bus radial networks. The immigration of gorillas during the exploration
phase is enriched through the utilization of stored memories of candidate trajectories within the search space,
enabling the silverback to make informed decisions. Furthermore, a multi-objective variant of MGTO is developed
in collaboration with Fuzzy Decision-Making (FDM), allowing for the simultaneous optimization of multiple
targets. To demonstrate the MGTO effectiveness, it is rigorously compared against a comprehensive set of
established optimization algorithms, including the Honey Badger Algorithm (HBA), Runge Kutta Optimizer
(RUN), and others. The results proved the dominance of the proposed MGTO by getting minimum power loss and
voltage fluctuation of 0.364 % and 15.4 %, respectively, while in the multi-objective problem, the best results are
0.513 % loss and 17.9% voltage fluctuation. The results proved the consistency of the proposed MGTO in
installing different BDGs into an unbalanced distribution network.