摘要
为了提高粒子群算法的优化能力,提出一种新的量子衍生粒子群优化算法.该方法采用多比特量子系统的基态概率幅对粒子编码,基于自身最优粒子和全局最优粒子确定旋转角度,采用基于张量积构造的多比特量子旋转门实施粒子的更新.在每步迭代中,只需更新粒子的一个量子比特相位,即可更新该粒子上的所有概率幅.标准函数极值优化的实验结果表明,所提出算法的单步迭代时间较长,但优化能力较同类算法有大幅度提高.
To enhance the optimization ability of the particle swarm algorithm, a novel quantum-inspired particle swarm optimization algorithm is proposed. In this method, the particles are encoded by the probability amplitudes of the basic states of the multi-qubits system. The rotation angles of multi-qubits are determined based on the local optimum particle and the global optimal particle, and the multi-qubits rotation gates are employed to update the particles. At each of iteration,updating any a qubit can lead to update all probability amplitudes of the corresponding particle. The experimental results of some benchmark functions optimization shows that, although its single step iteration consumes a long time, the optimization ability of the proposed method is significantly higher than other similar algorithms.
出处
《控制与决策》
EI
CSCD
北大核心
2015年第11期2041-2047,共7页
Control and Decision
基金
国家自然科学基金项目(61170132)
黑龙江省教育厅科学技术研究项目(12541059)
黑龙江省自然科学基金项目(F2015021)
关键词
量子计算
粒子群优化
多比特概率幅编码
算法设计
quantum computing
particle swarm optimization
multi-qubits probability amplitudes encoding
algorithm design