基于随机评价机制的量子粒子群优化算法及其参数控制

Improved QPSO algorithm based on random evaluation and its parameter control

为了改善量子行为粒子群优化(QPSO)算法的收敛性能,提出了一种基于随机评价策略的改进QPSO优化算法(RE-QPSO)。该算法通过使用随机因子对种群中粒子的创新性进行评价,提高了粒子摆脱局部极值的能力。提出了固定取值和线性递减两种控制策略分析RE-QPSO算法的唯一控制参数———收缩-扩张系数,通过6个标准测试函数的仿真结果给出了具有实际指导意义的控制参数选择方法。

In order to improve the convergence performance of Quantum-behaved Particle Swarm Optimization （QPSO） algorithm, this paper proposed an improved QPSO algorithm which was called RE-QPSO based on the random evaluation strategy. The new algorithm evaluated the innovation of particles by using a random factor and improved the ability of the particles to get rid of the local optima. Fixed value strategy and linear decreasing strategy were proposed for controlingthe theunique parameter of QPSO algorithm and they were tested on six benchmark functions. According to the test results, some conclusions concerning the selection of the parameter were drawn.