摘要： 通过将量子粒子群优化算法和佳点集法相结合，提出一种改进的混沌量子粒子群优化算法，用于解决复杂函数问题。将佳点集融合到量子粒子群算法中，以提高解空间的遍历性，对函数实现全局寻优。用混沌序列改变惯性权重w，调节粒子群优化算法的全局和局部寻优能力。采用线性递减速度比例收缩因子η提高搜索速度，避免早熟收敛。用量子Hadamard门对量子编码进行变异，增强种群的多样性，促使粒子跳出局部极值点。对典型复杂函数的仿真结果表明，该混合算法寻优效率高、收敛速度快，能有效避免早熟收敛。

关键词: 混沌, 量子粒子群优化, 佳点集, 收缩因子, 早熟收敛, 量子Hadamard门

Abstract: To solve complex function optimization problems, by combining Quantum Particle Swarm Optimization(QPSO) algorithm with good-point set method, an Improved Chaos Quantum Particle Swarm Optimization(ICQPSO) algorithm is proposed. Good-point set inserts to QPSO algorithm, improves the solution space ergodicity, to achieve global optimization for a function. Tthrough a chaotic sequence change inertia weight(w), to adjust QPSO algorithm’s global and local optimization ability. By linear decline rate proportion contraction factors η improve search speed, to avoid premature convergence. Through quantum Hadamard gate to variation quantum code, enhance the diversity of population, and promp the particle jump out of local extreme value point. The typical complex functions’ simulation results show that the hybrid optimization algorithm has high efficiency, fast convergence speed, effectively avoid premature convergence, and the optimal performance is much better than other optimization methods.

Key words: chaos, Quantum Particle Swarm Optimization(QPSO, good-point set, contraction factor, premature convergence, quantum Hadamard gate

中图分类号: 

• TP301.6