提出一种基于博弈论的多目标量子粒子群算法。
This paper presents a multi-objective quantum Particle Swarm Optimization(PSO) based on game theory.
针对量子粒子群算法存在的问题，设计基于公共历史的两种群并行搜索的量子粒子群算法。
According to still existing problem of Quantum-behaved Particle Swarm Optimization (QPSO), a new QPSO with two Particle Swarms based on public history researching side-by-side (TPHQPSO) is presented.
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.
为了提高汽轮机诊断系统的诊断速度与精度，提出了将量子粒子群算法和BP神经网络相结合的故障诊断方法。
To improving the diagnosed speed and accuracy of the steam turbine diagnose system, this paper proposes a method that combines QPSO with BP neural networks.
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.
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.
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