The particle swarm optimization (PSO) is a wide used optimization algorithm, which yet suffers from trapping in local optimum and the premature convergence. Many studies have proposed the improvements to address the drawbacks above. Most of them have implemented a single strategy for one problem or a fixed neighborhood structure during the whole search process. To further improve the PSO performance, we introduced a simple but effective method, named adaptive particle swarm optimization with Gaussian perturbation and mutation (AGMPSO), consisting of three strategies. Gaussian perturbation and mutation are incorporated to promote the exploration and exploitation capability, while the adaptive strategy is introduced to ensure dynamic implement of the former two strategies, which guarantee the balance of the searching ability and accuracy. Comparison experiments of proposed AGMPSO and existing PSO variants in solving 29 benchmark functions of CEC 2017 test suites suggest that, despite the simplicity in architecture, the proposed AGMPSO obtains a high convergence accuracy and significant robustness which are proven by conducted Wilcoxon’s rank sum test.

中文翻译：

---

高斯摄动和变异的自适应粒子群优化

粒子群优化算法（PSO）是一种广泛使用的优化算法，但存在陷入局部最优和过早收敛的问题。许多研究提出了改进措施以解决上述缺点。他们中的大多数人在整个搜索过程中都针对一个问题或固定的邻域结构实施了一种策略。为了进一步提高PSO性能，我们引入了一种简单而有效的方法，即具有高斯扰动和变异（AGMPSO）的自适应粒子群优化方法，该方法包括三种策略。引入高斯扰动和变异来提高勘探和开发能力，同时引入自适应策略以确保动态实现前两种策略，从而保证了搜索能力和准确性之间的平衡。