Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Modern digital ad platforms are confronted with huge challenges in optimizing for several business goals simultaneously over others, like click-through rate maximization alone. Classic optimization techniques show huge limitations in tackling short-term user engagement measures vs. long-term customer value generation, thereby leading to inefficient resource distribution and wasted revenue potential. Multi-objective reinforcement learning appears as a revolutionary solution that can help counter temporal mismatches between advertising actions and subsequent business consequences. Sophisticated algorithmic paradigms with deep neural networks, context-aware bandits, and distributed optimization support high-level decision-making operations that account for multiple stakeholder priorities in parallel. Evolutionary computation-based feature selection algorithms provide effective processing of high-dimensional input spaces while preserving computational tractability in extensive ad trading platforms. Hierarchical reinforcement learning systems display even better performance through expertise-oriented agent coordination mechanisms that harmonize conflicting objectives without compromising overall system coherence. Performance evaluation techniques using Pareto frontier analysis and dynamic weighting systems offer capable assessment functionality for multi-dimensional optimization scenarios. Implementation challenges such as computational complexity, attribution model complexity, and scalability needs call for distributed system designs and federated learning techniques. Contextual adaptation mechanisms support real-time response to shifting user preferences and market conditions without compromising on predictive accuracy across a wide range of demographic segments. The combination of advanced reward function construction with temporal credit assignment models provides strong models with the ability to optimize customer lifetime value in addition to instantaneous engagement metrics.