Abstract
Particle Swarm Optimization (PSO) is an effective tool for solving nonlinear, non-convex optimization problems, offering a quick and efficient way to identify rational asset allocations. In PSO, each particle represents a specific asset allocation and moves within the search space to optimize the defined criteria. Particles update their positions based on personal experience (individual best) and the collective experience of the swarm (global best), gradually converging toward an optimal solution. Research on applying PSO to asset allocation demonstrates that this algorithm not only optimizes expected returns but also minimizes risk in investment portfolios. With its adaptability and computational speed, PSO can become a valuable tool for investors in formulating flexible and effective asset allocation strategies in a volatile financial environment.
Published Version
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