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 representing for the first time a Quantum-Inspired Particle Swarm Optimization Algorithm Encoded by Probability Amplitudes of Multi-Qubit cursory analysis to identify several global patterns for anti-HIV-1 cell cycle viral replication enzymes for the efficient discovery of homomultimerized HIV short linear motif-like peptide mimicking lead compound on its functional binding sites.
cursory analysis; global patterns; anti-HIV-1; cell cycle viral replication enzymes; discovery of homomultimerized; HIV short linear motif-like; peptide mimicking; lead compound; functional binding sites; Quantum-Inspired; Particle Swarm; Optimization Algorithm; Probability Amplitudes of Multi-Qubits;