Discrete degree is used as an index to the measure of population diversity. In the initialization phase of particle swarm, the discrete degree of swarm must meet certain requirements before its iteration. In the iterative process, the adjustment of the inertia weight and the acceleration coefficient is related to the current discrete degree of particle swarm. When the discrete degree is smaller than a certain value, it should reinitialize in urder to retain high quality, stretch the fitness function and reiterate.As the algorithm is limited based on the discrete degree in the initialization phase, even particle distribution is demanded. In the running process, the discrete degree can reflect the current state of population distribution in a better way and associates itself with the parameters relevant to the algorithm. Thus,the good performance of the algorithm is ensured in theory. Based on five different benchmark functions, the simulation results show that the performance of the algorithm has an optimal convergence rate, and can avoid early convergence effectively while dealing with the multi-modal and flat functions.%采用离散度作为衡量种群多样性的指标.在粒子群初始化阶段,种群的离散度必须满足一定的要求才能开始迭代;在算法迭代过程中,惯性权重、加速系数的调整都与当前粒子群的离散度相关;当种群的离散度小于一定数值时,进行保优重初始化,适应度函数拉伸操作,重新迭代.由于算法在初始化阶段依据离散度进行了限定,要求粒子尽量平均分布,算法运行过程中离散度能够更加真实地反映当前种群的分布状态,并将算法的相关参数与之关联,在理论上保证了算法将具有良好的性能.经过在5个基准函数上的仿真实验表明,该算法在处理复杂多峰、平坦函数优化问题时,收敛速度快且能有效避免早熟问题.
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