Pareto local search (PLS) serves as an important component in multi-objective combinatorial optimization. Nevertheless, achieving a balance between convergence and diversity remains a challenge, as few studies have leveraged knowledge from the search archive to effectively guide the PLS process. This paper proposes an archive entropy-guided Pareto local search algorithm (AEG-PLS). In the proposed method, the objective space is partitioned into subregions using a set of reference vectors. The archive entropy is then computed for each subregion to assess population diversity. To enhance diversity in less explored areas, a PLS is initiated using a well-performing solution selected from the subregion with the lowest entropy, thus indicating the weakest diversity. This approach promotes a more balanced trade-off between convergence and diversity throughout the optimization process. Experimental results on 25 multi-objective combinatorial optimization benchmark instances demonstrate that the proposed AEG-PLS achieves competitive performance in terms of both Inverted Generational Distance and Hypervolume metrics when compared to nine state-of-the-art multi-objective evolutionary algorithms.