Application of Portfolio Optimization Based on Multi-Strategy Improved Artificial Bee Colony Algorithm

Portfolio optimization, regarded as a significant issue within the realm of finance, derives its complexity from the uncertainties and diversities inherent in the market. Throughout this endeavor, the pursuit of an effective methodology to maximize investment returns under a specified level of risk has consistently stood as a shared objective among scholars and practitioners alike. Traditional optimization algorithms, despite their varied theoretical foundations, exhibit limitations when confronted with high-dimensional and nonlinear challenges. In contrast, swarm intelligence algorithms, particularly the Artificial Bee Colony (ABC) algorithm, introduce new opportunities for addressing these issues due to their biologically inspired characteristics. Nevertheless, the conventional ABC algorithm reveals bottlenecks such as slow convergence rates and a propensity to become trapped in local optima when applied to the complexities of financial scenarios. Consequently, recent research has progressively integrated multi-strategy enhancements into the ABC algorithm, seeking to explore more adaptive and efficient optimization pathways. By amalgamating various optimization techniques, including Logistic mapping, reverse learning, elite strategies, and the Metropolis algorithm, the aim is to transcend the limitations of traditional algorithms and enhance their practical efficacy in portfolio optimization.