Comparative Study of Heuristic vs. Metaheuristic Optimization in Pension Fund Management

Pension fund management requires balancing long-term security with dynamic market uncertainties, making optimization techniques essential for investment allocation and risk control. Traditional heuristic approaches, such as rule-based allocation and greedy algorithms, offer simplicity and computational efficiency but often struggle to adapt to nonlinear financial environments. In contrast, metaheuristic methods, including Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization, provide more robust search capabilities, enabling the exploration of complex solution spaces with higher accuracy. This paper presents a comparative study of heuristic and metaheuristic optimization methods in pension fund management, evaluating their effectiveness across parameters such as risk-return trade-offs, convergence speed, scalability, and adaptability to market volatility. Experimental results highlight that while heuristics remain valuable for real-time decision-making with limited data, metaheuristics outperform them in delivering optimized portfolio allocations under uncertain conditions. The study contributes to financial engineering by offering insights into how hybrid models can leverage the strengths of both approaches to enhance fund sustainability and ensure more reliable retirement outcomes.