In recent years, several swarm intelligence optimization algorithms have been proposed to be applied for solving a variety of optimization problems. However, the values of several hyperparameters should be determined. For instance, although Particle Swarm Optimization (PSO) has been applied for several applications with higher optimization performance, the weights of inertial velocity, the particle's best known position and the swarm's best known position should be determined. Therefore, this study proposes an analytic framework to analyze the optimized average-fitness-function-value (AFFV) based on mathematical models for a variety of fitness functions. Furthermore, the optimized hyperparameter values could be determined with a lower AFFV for minimum cases. Experimental results show that the hyperparameter values from the proposed method can obtain higher efficiency convergences and lower AFFVs.

翻译：近年来，多种群体智能优化算法被提出以解决各类优化问题。然而，若干超参数的值仍需确定。例如，尽管粒子群优化（PSO）已应用于多个具有更高优化性能的场景，但惯性速度权重、粒子个体最佳已知位置以及群体最佳已知位置仍需设定。因此，本研究提出了一种分析框架，基于多种适应度函数的数学模型来评估优化的平均适应度函数值（AFFV）。此外，针对最小化情形，可通过获取更低的AFFV来确定优化的超参数值。实验结果表明，采用所提方法获得的超参数值能够实现更高效的收敛性并降低AFFV。