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http://dx.doi.org/10.1016/j.ins.2012.01.005

has been cited by the following article:

• TITLE: Quantum-Inspired Particle Swarm Optimization Algorithm Encoded by Probability Amplitudes of Multi-Qubits

  AUTHORS: Xin Li, Huangfu Xu, Xuezhong Guan

  KEYWORDS: Quantum Computing, Particle Swarm Optimization, Multi-Qubits Probability Amplitudes Encoding, Algorithm Design

  JOURNAL NAME: Open Journal of Optimization, Vol.4 No.2, May 14, 2015

  ABSTRACT: To enhance the optimization ability of 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 qubit can lead to updating all probability amplitudes of the corresponding particle. The experimental results of some benchmark functions optimization show that, although its single step iteration consumes long time, the optimization ability of the proposed method is significantly higher than other similar algorithms.