采用二分策略，通过最大化模块密度，提出了基于离散量子粒子群优化进行复杂网络社区检测的算法。
With bi-partitioning strategy, by maximizing the module density, an algorithm is proposed based on discrete quantum particle swarm optimization for complex network community detection.
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。
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.
分析量子计算的特点，对量子旋转门进行研究，给出了新的量子旋转门调整策略，并与离散二进制粒子群优化算法进行组合，提出了二进制量子粒子群优化算法。
According to the analysis of the characteristics of quantum computing and the research of quantum rotation gate, a new quantum rotation gate adjustment strategy was introduced.
对于多目标、多约束条件的四连杆机构优化设计，本文提出了一种基于量子粒子群算法求解的设计方法。
This paper puts forward the design method based on Quantum Particle Swarm optimization for optimization design of four bar linkage of multi-objective, multi-constraint conditions.
对于多目标、多约束条件的四连杆机构优化设计，本文提出了一种基于量子粒子群算法求解的设计方法。
This paper puts forward the design method based on Quantum Particle Swarm optimization for optimization design of four bar linkage of multi-objective, multi-constraint conditions.
应用推荐