自适应收扩系数的双中心协作QPSO算法

针对量子粒子群优化(QPSO)算法迭代后期种群多样性下降、收敛速度慢、易陷入局部最优的缺点,提出一种自适应收缩-扩张系数的双中心协作最子粒子群优化算法。该算法从2个方面进行改进:(1)自适应调节收缩-扩张系数,其目的是帮助粒子跳出局部最优点,提高粒子的全局搜索能力;(2)双重更新全局最优位置,即在每次迭代中,先后分别采用2种不同的方式更新全局最优位置。第1种方式与QPSO算法一致,第2种方式则引入双中心粒子,使其和当前全局最优位置在相应维度上合作,从而达到更新全局最优位置的目的。从固定迭代次数和固定精度角度分析算法性能,仿真结果表明,相比于QPSO算法,该算法在保证复杂度较低的情况下,可提高收敛速度,增强全局和局部搜索能力。

Cooperative Double-center QPSO Algorithm with Self-adaptive Contraction-expansion Coefficient

Against the problems of Quantum Particle Swarm Optimization（QPSO） algorithm in the late part of iterations, such as decline of population diversity, slow convergence rate and easy to fall into the local optima, a cooperative double-center QPSO algorithm with self-adaptive contraction-expansion coefficient is proposed in this paper. It has two characteristics： （1）The contraction-expansion coefficient is self-adaptively adjusted, which can help jump out of the local optima and improve the global search ability; （2）It renews the global best location of every iteration in two ways, and one way of them is the same as that in QPSO, the other is that double-center particles, together with the current global optimal location, which is used to improve the way of renewing the global best location by cooperating in the corresponding dimensions among them. The performance of this algorithm is analyzed from fixed iterations and fixed precisions. Experimental results show that compared with QPSO, it has higher convergence rate and better search ability.