论文部分内容阅读
为提高量子势阱粒子群优化算法的优化能力,通过分析目前量子势阱粒子群优化算法的设计过程,提出了改进的量子势阱粒子群优化算法.首先,分别基于Delta势阱、谐振子和方势阱提出了改进的量子势阱粒子群优化算法,并提出了基于统计量均值的控制参数设计方法.然后,在势阱中心的设计方面,为强调全局最优粒子的指导作用,提出了基于自身最优粒子加权平均和动态随机变量的两种设计策略.实验结果表明,三种势阱粒子群优化算法性能比较接近,都优于原算法,且Delta势阱模型略优于其他两种.
In order to improve the optimization ability of quantum well particle swarm optimization algorithm, an improved quantum well particle swarm optimization algorithm is proposed by analyzing the design process of the current quantum well particle swarm optimization algorithm.First, based on Delta potential well, harmonic oscillator and Square well presents an improved quantum well particle swarm optimization algorithm and proposes a control parameter design method based on the mean of the statistics.Next, in order to emphasize the guiding role of global optimal particle in the design of potential well, Two design strategies based on their own optimal particle weighted average and dynamic random variables.The experimental results show that the performance of the three potential well particle swarm optimization algorithm is relatively close, are better than the original algorithm, and the Delta potential well model slightly better than the other two .