Velocity limit (VL) has been widely adopted in many variants of particle swarm optimization (PSO) to prevent particles from searching outside the solution space. Several adaptive VL strategies have been introduced with which the performance of PSO can be improved. However, the existing adaptive VL strategies simply adjust their VL based on iterations, leading to unsatisfactory optimization results because of the incompatibility between VL and the current searching state of particles. To deal with this problem, a novel PSO variant with state-based adaptive velocity limit strategy (PSO-SAVL) is proposed. In the proposed PSO-SAVL, VL is adaptively adjusted based on the evolutionary state estimation (ESE) in which a high value of VL is set for global searching state and a low value of VL is set for local searching state. Besides that, limit handling strategies have been modified and adopted to improve the capability of avoiding local optima. The good performance of PSO-SAVL has been experimentally validated on a wide range of benchmark functions with 50 dimensions. The satisfactory scalability of PSO-SAVL in high-dimension and large-scale problems is also verified. Besides, the merits of the strategies in PSO-SAVL are verified in experiments. Sensitivity analysis for the relevant hyper-parameters in state-based adaptive VL strategy is conducted, and insights in how to select these hyper-parameters are also discussed.

翻译：速度限制被广泛应用于粒子群优化的多种变体中，以防止粒子在解空间外搜索。已有几种自适应速度限制策略被引入，能够提升粒子群优化的性能。然而，现有自适应速度限制策略仅根据迭代次数调整速度限制，导致速度限制与粒子当前搜索状态不兼容，从而获得不理想的优化结果。针对此问题，提出了一种新型带有基于状态自适应速度限制策略的粒子群优化变体。在所提出的方法中，速度限制基于进化状态估计自适应调整：全局搜索状态设置高速度限制值，局部搜索状态设置低速度限制值。此外，修改并采用了边界处理策略以增强避免局部最优的能力。通过含50维的广泛基准函数实验验证了该方法的良好性能，并证实了其在高维及大规模问题中的可扩展性。同时，实验中验证了该方法中策略的优势。对基于状态自适应速度限制策略中相关超参数进行了敏感性分析，并讨论了如何选取这些超参数的见解。