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Energy prices are booming to high levels with the ongoing global events and massive energy demand post-COVID, triggering panic among-st the general public. In this regard, energy demand management can play an important role here by letting both the entities (utility and customer) take control over the consumption pattern long enough until renewables take over the energy supply from the conventional resources using real-time device scheduling algorithms. To achieve this phenomenon, both participating entities need proper prioritization and their interests to be properly taken care of. In this paper sensitivity analysis of varying priorities to the objectives (customer cost and peak-to-average power ratio) representing both the entities has been carried out. A real-time device scheduling algorithm is implemented to improve the consumption pattern of appliances consisting of multiple objectives. Proper way of normalization is developed to retain the original essence of these objectives. The analysis embeds novel swap operations that are incorporated with standard particle swarm optimization (PSO) and its variants under varying pricing schemes. In order to demonstrate the proposed DR-based real-time optimization, a residence in Chicago, Illinois, USA is taken as a case study for this work. The results demonstrate the quickness and proper optimizations of the PSO variants with butterfly-PSO performing the best among-st the rest. Continuous real-time pricing obtained the best solution with apt quickness among-st its counterparts for each time-slot and for cumulative time of the day. An overall reduction in each entity's metrics is obtained in this work. [ABSTRACT FROM AUTHOR]