An application of quantum-inspired particle swarm optimization to function optimization problems
An application of quantum-inspired particle swarm optimization to function optimization problems
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DOI：
10.1109/ijcnn.2013.6706880
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发表时间：
2013-08
期刊：
影响因子：
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通讯作者：
Koichiro Tazuke;Noriyuki Muramoto;N. Matsui;T. Isokawa
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文献类型：
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作者：
Koichiro Tazuke;Noriyuki Muramoto;N. Matsui;T. Isokawa
量子粒子群优化算法（QPSO）是粒子群优化算法（PSO）的一种扩展，它引入了量子力学的概念。量子粒子群中粒子的状态是由薛定谔方程的波函数来描述的，而标准粒子群中的粒子的状态是其位置和速度。通过高维函数优化问题证明了QPSO的性能，并与标准PSO进行了比较。实验结果表明，量子粒子群算法比传统的粒子群算法能更快地找到（接近）最优值。
Quantum-Inspired Particle Swarm Optimization (QPSO) is an extension of Particle Swarm Optimization (PSO) methods, in which the concept of quantum mechanics is adopted. The state of a particle in QPSO is described by a wave function derived from the Schrödinfer equation, whereas a particle in standard PSOs has its location and velocity as its state. The performances of QPSOs are demonstrated through the optimization problem for higher-dimensional functions, with comparison of the standard PSO. The experimental results show that QPSOs can find (near) optimal values much faster than the conventional PSO.