Ant colony intelligence optimization of asset portfolios under constraints—dynamic risk adjustment and Sharpe ratio maximization

Abstract

This study addresses the challenge of constrained portfolio optimization by proposing a novel framework based on an enhanced Ant Colony Optimization (ACO) algorithm. Building upon the Markowitz mean-variance foundation, we propose a hybrid framework that integrates Ant Colony Optimization (ACO) for discrete asset selection under cardinality constraints with quadratic programming for optimal weight allocation subject to no-short-selling and budget constraints. Dynamic risk estimation is achieved by employing EWMA and GARCH(1,1) models on rolling windows to update the covariance matrix during optimization. The latter is achieved by employing EWMA and GARCH(1,1) models on rolling windows to update the covariance matrix during optimization. We introduce key enhancements to the ACO algorithm, such as an elite retention strategy and an adaptive pheromone update mechanism linked to asset weights, to improve convergence and solution quality under complex constraints. Empirical validation is conducted using historical daily returns from a stratified sample of 20 S&P 500 stocks. Results demonstrate that the enhanced ACO significantly outperforms benchmark algorithms (PSO and GA), yielding portfolios with higher Sharpe ratios (average improvement of 10–15% on test set) and superior risk control (reduced maximum drawdown by 5–7%). Robust performance is maintained across both bull and bear market regimes. This research provides an effective heuristic optimization approach for quantitative multi-factor strategy design, offering practical value for real-world asset allocation under constraints.