Particle swarm optimization (PSO) is a stochastic computation technique motivated by intelligent collective behavior of some animals, which has been widely used to address many hard optimization problems. However, like other evolutionary algorithms, PSO also suffers from premature convergence and entrapment into local optima when dealing with complex multimodal problems. In this paper, we propose a chaotic particle swarm optimization with sigmoid-based acceleration coefficients (abbreviated as CPSOS). On the one hand, the frequently used logistic map is applied to generate well-distributed initial particles. On the other hand, the sigmoid-based acceleration coefficients are formulated to balance the global search ability in the early stage and the global convergence in the latter stage. In particular, two sets of slowly varying function and regular varying function embedded update mechanism in conjunction with the chaos based re-initialization and Gaussian mutation strategies are employed at different evolution stages to update the particles during the whole search process, which can effectively keep the diversity of the swarm and get out of possible local optima to continue exploring the potential search regions of the solution space. To validate the performance of CPSOS, a series of experiments are conducted and the simulation results reveal that the proposed method can achieve better performance compared to several state-of-the-art PSO variants in terms of solution accuracy and effectiveness.

粒子群优化（PSO）是一种随机计算技术，受某些动物的智能集体行为的驱使，已被广泛用于解决许多困难的优化问题。但是，像其他进化算法一样，PSO在处理复杂的多峰问题时也遭受过早的收敛和陷入局部最优的困扰。在本文中，我们提出了一种基于S形加速度系数（简称为CPSOS）的混沌粒子群优化算法。一方面，将常用的逻辑图应用于生成分布均匀的初始粒子。另一方面，基于S形的加速度系数被公式化以平衡早期的全局搜索能力和后期的全局收敛。特别是，在不同的演化阶段，采用两组缓慢变化的函数和规则变化的函数嵌入的更新机制，以及基于混沌的重新初始化和高斯变异策略，可以在整个搜索过程中更新粒子，从而可以有效地保持粒子的多样性。蜂拥而至，摆脱可能的局部最优，继续探索解决方案空间的潜在搜索区域。为了验证CPSOS的性能，进行了一系列实验，仿真结果表明，与几种最新的PSO变体相比，该方法在解决方案的准确性和有效性方面可以实现更好的性能。