Optimizing Hybrid Algorithmic Frameworks for Enhanced Computational Efficiency in Multi-Objective Simulations

Abstract

In recent years, the growth of computational models for complex systems has underscored the need for enhanced algorithmic frameworks. This study introduces a novel hybrid optimization approach that integrates Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) to address the challenges faced in multi-objective simulations. Leveraging an extensive suite of benchmark problems, we perform a rigorous comparative analysis against traditional optimization methods. The empirical findings indicate significant reductions in computational time by up to 35% while maintaining solution quality, evidenced by improved Pareto front representations. Our results advocate for the applicability of the proposed framework across various computational disciplines, thereby laying a foundation for future research in algorithmic efficiency. This research contributes to the ongoing discourse on optimizing computational resources while addressing multi-objective problems, a critical concern in the field of computational science.