© 2022 Elsevier B.V.This study proposes a robust method to improve the search performance of multi-objective evolutionary algorithms (MOEAs) using a Pareto-based archiving mechanism and a crowding distance-based archive handling mechanism. The aim of the proposed method is to provide a sustainable diversity in the objective and decision spaces and to establish a stable exploitation-exploration balance in both spaces. To this purpose: 1) reference space combinations are defined, 2) strategies consisting of reference space combinations are developed to improve the performance of the crowding-distance calculation, and 3) a dynamic switching mechanism is proposed to implement these strategies. In the proposed DSC method, non-dominated solutions are represented in three different reference spaces: the decision space, the objective space, and the unified result of these two spaces. Binary combinations of these three spaces were created and strategies using different reference spaces were developed. The switching mechanism was designed to implement these strategies dynamically. Crowding-distance calculation was performed with reference to the space vector selected by this switching mechanism. The proposed DSC method was tested on multimodal multi-objective optimization problems (MMOPs) and real-world engineering problems incorporating both alternating current optimal power flow (AC-OPF) and alternating current/direct current optimal power flow (AC/DC-OPF). According to the experimental study results, the proposed DSC-MOAGDE algorithm has about 30% better success rate on MMOPs compared to its competitors. Similarly, the proposed method was able to optimize cost by 6.66%, 24.15%, 52.13%, 56.72%, and 120.21% better than the MMODE_ICD, MO_RING_PSO_SCD, MOAGDE, NSWOA and SSMOPSO algorithms in AC-OPF and AC/DC-OPF real-world problems. The source codes of the DSC-MOAGDE: https://se.mathworks.com/matlabcentral/fileexchange/119378-dsc-moagde-a-novel-theory-and-a-powerful-algorithm?s_tid=srchtitle.