Abstract: An improved artificial bee colony algorithm is proposed for constrained optimization problems. The Pareto dominate criterion is introduced to improve the exploration ability of the algorithm and avoid the premature convergence of the algorithm. In the employed bee stage, the searching equations and constraint handling strategies are selected adaptively by identifying the current state of the population, which can guide the population into the feasible region quickly. In the onlooker bee stage, the global optimal solution is used for guiding the population searching, which can improve the exploitation ability of the algorithm. Experimental results on 20 benchmark test functions in CEC 2006 show that this algorithm can effectively solve constrained optimization problems. Furthermore, the portfolio optimization problems are solved by this algorithm. The numerical experiments show the effectiveness of this algorithm for portfolio optimization problems, which can solve this kind of financial problem effectively.

Key words: constrained optimization, improved artificial bee colony algorithm, feasibility rule, Pareto domination approach, portfolio optimization

摘要： 为了有效求解约束优化问题，提出一种改进人工蜂群算法。该算法引入Pareto支配准则提高算法探索能力，避免算法早熟。在雇佣蜂阶段，通过识别种群当前状态自适应选取搜索方程与约束处理策略，引导种群快速进入可行区域。在跟随蜂阶段，利用全局最优解引导种群进行搜索，提高算法开发能力。通过对CEC 2006中20个测试函数实验结果分析表明，该算法能够有效求解约束优化问题。进而，将该算法应用于求解投资组合优化问题，通过数值实验说明该算法是求解投资组合优化问题的有效算法，可以用于求解此类金融问题。

关键词: 约束优化, 改进人工蜂群算法, 可行性规则, Pareto支配, 投资组合优化