Real-time path planning is a critical challenge in artificial intelligence, especially in robotics and gaming, directly impacting operational efficiency and user experience. Traditional pathfinding algorithms like A* typically optimize for a single objective and face difficulties in continuously changing dynamic environments that require multiobjective optimization. To address these limitations, the Real-Time Dynamic Multiobjective (RDMO) algorithm was introduced, based on an enhanced A* framework, enabling balanced optimization of multiple objectives and rapid adaptation to environmental changes. However, RDMO still struggles with the overhead of full replanning after each change, particularly in environments with frequent localized updates. This study proposes an improved architecture, I-RDMO, which integrates the incremental search principle of the D* Lite algorithm to reduce replanning costs by updating only the affected regions. Experimental results in simulated environments demonstrate that I-RDMO maintains high processing speed, reduces latency, and improves efficiency compared to traditional methods. This solution is well-suited for applications in real-time strategy games, autonomous robotics, and interactive systems requiring fast response in dynamic settings.