Optimizing spectral and energy efficiency trade-off for massive MIMO systems using improved multi-objective Ali Baba and the forty thieves algorithm

This paper proposes an enhanced multi-objective Ali Baba and the forty thieves (MOAFT) algorithm, incorporating an instant sorting mechanism to dynamically evaluate and retain high-quality non-dominated solutions during optimization. The mechanism operates by instantaneously sorting candidate solutions based on dominance and crowding distance criteria, enabling the recovery of promising yet overlooked individuals in the population. This approach enhances diversity preservation without significant computational overhead. The algorithm’s performance was rigorously tested on Zitzler–Deb–Thiele benchmark functions and compared against established metaheuristic optimizers, where it consistently outperformed competitors. The improved multi-objective Ali Baba and the forty thieves algorithm was applied to the study utilizing massive multiple-input multiple-output technology, addressing the critical challenge in cellular communications: the joint improvement of spectral efficiency and energy efficiency. Spectral efficiency, which relates to communication quality, and energy efficiency, which addresses challenges associated with depleting resources, are key objectives in this context. When implemented on this problem, the improved MOAFT achieved an average inverted generational distance (IGD) of 0.581 and a minimum IGD of 0.473 across 100 simulations, demonstrating its superiority in estimating the Pareto optimal front and retaining diversity.