<p>Population cycles refer to the regular fluctuations in population size over time. Inspired by this phenomenon, we introduce a period-regulating factor into the particle swarm optimization (PSO) algorithm and propose a modified period-regulated particle swarm optimization (PPSO) algorithm. This algorithm allows for dynamic parameter adjustments in each particle, maintaining a balance between exploitation and exploration. To enhance the diversity of the swarm, we introduce a selection mechanism that enables particles to choose between the global optimum and a new learning object called the mean optimum. Additionally, we incorporate a multi-particle mutation mechanism to improve the particles’ ability to escape local optima. A set of benchmark functions and classical engineering problems are used to verify the superiority of the PPSO algorithm. The results show that the PPSO can provide a very competitive performance compared to some popular PSO variants and meta-heuristic algorithms. Furthermore, this algorithm retains the advantages of simplicity of implementation inherent in PSO.</p>