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.
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.
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.
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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