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

Abstract

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.