Abstract: Aiming at the problems of Pareto dominance failure, weak optimization ability, and slow convergence speed of multi-objective firefly algorithm in solving many-objective optimization problems, we propose a many-objective firefly algorithm based on reference point guidance and multiple cooperation strategies (MaOFA-RR). This algorithm presets a set of uniformly distributed reference points in the objective space. By examining the distance relationship between fireflies and reference points, it distinguishes between guide fireflies and ordinary fireflies to replace Pareto dominance, thereby increasing selection pressure. Three evolutionary strategies are employed to update the positions of the fireflies: guide fireflies explore the local space, while ordinary fireflies learn from guide fireflies or explore the global space based on a distance threshold, enhancing the algorithm’s optimization capability and convergence speed. Finally, the algorithm ingeniously integrates the idea of opposition-based learning. In the process of opposition-based learning, for each firefly in the population, its opposite position is calculated in the solution space. By adding these opposite solutions to the original population, the algorithm effectively expands the scope of population search, significantly improving the possibility of discovering better solutions. We conduct comprehensive experiments by comparing MaOFA-RR with 8 recent many-objective evolutionary algorithms. The experimental results show that MaOFA-RR exhibits efficient performance in handing many-objective optimization problems.