摘要： 针对量子粒子群优化算法在处理一般复杂函数时可以找到函数最优解但容易陷入局部极小等问题，提出利用混沌搜索解决早熟收敛的混合量子粒子群算法CODPSO。数值实验结果表明，与量子粒子群优化算法相比，该算法效率高、优化性能好，具有较强的避免局部极小能力，对初值具有较强的鲁棒性。

关键词: 量子粒子群优化算法, 混沌优化, 早熟

Abstract: Focusing on the problem of sensitivity to local convergence when using QDPSO to handle complex functions of the best answer, this paper proposes hybrid QDPSO, named CODPSO, which using chaos searching to solve premature convergence. Numerical simulation results show that, compared with QDPSO, it is effective, with strong ability to avoid being trapped in local minima and robust to initial value.

Key words: QDPSO, chaos optimization, local convergence

中图分类号: 

• TP301.6