Portfolio optimization with cardinality constraints turns out to be a mixed-integer quadratic programming problem which is proven to be NP-Complete that limits the efficiency of exact solution approaches, often because of the long-running times. Therefore, particular attention has been given to approximate approaches such as metaheuristics which do not guarantee optimality, yet may expeditiously provide near-optimal solutions. The purpose of this study is to present an efficient hybrid metaheuristic algorithm that combines critical components from continuous ant colony optimization, artificial bee colony optimization and genetic algorithms for solving cardinality constrained portfolio optimization problem. Computational results on seven publicly available benchmark problems confirm the effectiveness of the hybrid integration mechanism. Moreover, comparisons against other methods’ results in the literature reveal that the proposed solution approach is competitive with state-of-the-art algorithms.

带有基数约束的投资组合优化原来是一个混合整数二次规划问题，被证明是NP-Complete，通常会因为运行时间长而限制了精确求解方法的效率。因此，已经特别注意了近似方法，例如元启发法，这种方法不能保证最优性，但可以迅速提供接近最佳的解决方案。本研究的目的是提出一种有效的混合元启发式算法，该算法结合了来自连续蚁群优化，人工蜂群优化和遗传算法的关键成分，以解决基数受限的投资组合优化问题。对七个公开基准问题的计算结果证实了混合整合机制的有效性。此外，