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A new PSO algorithm with Random C/D Switchings

机译：具有随机C / D切换的新PSO算法

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摘要

This paper investigates the overall convergence analysis and proposes a novel Random C/D Switchings PSO algorithm with random switchings between convergence operator and divergence operator. With respect to the standard PSO algorithm, its convergence analysis provides a fundamental theory of selecting convergence operator and divergence operator. During the process of finding the suboptimal or global solution, the random switchings between two typical operators, namely Operator C and Operator D, which are two different ways to update velocities of all particles, are conducted by a so-called convergence ratio parameter, which can determine the tradeoff between exploration ability and exploitation ability from the quantitative perspective. Numerical results on several benchmark functions demonstrate the following observations: (1) The proper convergence ratio is closely related to the landscape of objective function, the dimension of solution space and the number of local optimums. (2) Small convergence ratio, setting to 0.60 or 0.65, may benefit the optimization problem which has many local optimums in the high dimensional space; while large convergence ratio, setting to 0.85 or 0.9, is probably helpful for the optimization problem with few local optimums or flat landscape.
机译：本文研究了整体收敛性分析，并提出了一种在收敛算子和发散算子之间随机切换的新型随机C / D切换PSO算法。对于标准PSO算法，其收敛分析提供了选择收敛算子和散度算子的基础理论。在寻找次优或整体解的过程中，两个典型算子（算子C和算子D）之间的随机切换是更新所有粒子速度的两种不同方式，这是通过所谓的收敛比参数进行的，可以从定量角度确定勘探能力与开发能力之间的权衡。在几个基准函数上的数值结果证明了以下发现：（1）适当的收敛比率与目标函数的范围，解空间的大小和局部最优数目密切相关。 （2）收敛速度较小，设置为0.60或0.65可能会有利于优化问题，该问题在高维空间中具有很多局部最优值;而较大的收敛比率（设置为0.85或0.9）可能有助于解决局部最优值或平坦景观很少的优化问题。

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