An algorithm with different parameter settings often performs differently on the same problem. The parameter settings are difficult to determine before the optimization process. The variants of particle swarm optimization (PSO) algorithms are studied as exemplars of swarm intelligence algorithms. Based on the concept of building block thesis, a PSO algorithm with multiple phases was proposed to analyze the relation between search strategies and the solved problems. Two variants of the PSO algorithm, which were termed as the PSO with fixed phase (PSOFP) algorithm and PSO with dynamic phase (PSODP) algorithm, were compared with six variants of the standard PSO algorithm in the experimental study. The benchmark functions for single-objective numerical optimization, which includes 12 functions in 50 and 100 dimensions, are used in the experimental study, respectively. The experimental results have verified the generalization ability of the proposed PSO variants.

中文翻译：

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用于求解连续优化问题的多阶段粒子群优化算法

具有不同参数设置的算法在同一问题上通常表现不同。在优化过程之前很难确定参数设置。粒子群优化 (PSO) 算法的变体作为群智能算法的范例进行了研究。基于积木论文的概念，提出了一种多阶段PSO算法来分析搜索策略与求解问题之间的关系。在实验研究中，将 PSO 算法的两个变体（称为具有固定相位的 PSO（PSOFP）算法和具有动态相位的 PSO（PSODP）算法）与标准 PSO 算法的六个变体进行了比较。单目标数值优化的基准函数，包括 50 和 100 维的 12 个函数，分别用于实验研究。实验结果验证了所提出的 PSO 变体的泛化能力。