The particle swarm optimization algorithm (PSO) is a meta-heuristic algorithm with swarm intelligence. It has the advantages of easy implementation, high convergence accuracy, and fast convergence speed. However, PSO suffers from falling into a local optimum or premature convergence, and a better performance of PSO is desired. Some methods adopt improvements in PSO parameters, particle initialization, or topological structure to enhance the global search ability and performance of PSO. These methods contribute to solving the problems above. Inspired by them, this paper proposes a variant of PSO with competitive performance called UCPSO. UCPSO combines three effective improvements: a cosine inertia weight, uniform initialization, and a rank-based strategy. The cosine inertia weight is an inertia weight in the form of a variable-period cosine function. It adopts a multistage strategy to balance exploration and exploitation. Uniform initialization can prevent the aggregation of initial particles. It distributes initial particles uniformly to avoid being trapped in a local optimum. A rank-based strategy is employed to adjust an individual particle’s inertia weight. It enhances the swarm’s capabilities of exploration and exploitation at the same time. Comparative experiments are conducted to validate the effectiveness of the three improvements. Experiments show that the UCPSO improvements can effectively improve global search ability and performance.

中文翻译：

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UCPSO：具有余弦惯性权重的均匀初始化粒子群优化算法

粒子群优化算法（PSO）是具有群智能的元启发式算法。具有易于实现，收敛精度高，收敛速度快的优点。但是，PSO陷入局部最佳或过早收敛的状态，因此需要PSO的更好性能。一些方法采用了PSO参数，粒子初始化或拓扑结构方面的改进，以增强PSO的全局搜索能力和性能。这些方法有助于解决上述问题。受他们的启发，本文提出了一种具有竞争性能的PSO变体，称为UCPSO。UCPSO结合了三项有效的改进：余弦惯性权重，统一初始化和基于等级的策略。余弦惯性权重是可变周期余弦函数形式的惯性权重。它采用了多阶段策略来平衡勘探和开发。均匀的初始化可以防止初始粒子的聚集。它可以均匀分布初始粒子，以避免陷入局部最优状态。采用基于等级的策略来调整单个粒子的惯性权重。它同时增强了群体的勘探和开发能力。进行比较实验以验证这三个改进的有效性。实验表明，UCPSO的改进可以有效地提高全局搜索能力和性能。它可以均匀分布初始粒子，以避免陷入局部最优状态。采用基于等级的策略来调整单个粒子的惯性权重。它同时增强了群体的勘探和开发能力。进行比较实验以验证这三种改进的有效性。实验表明，UCPSO的改进可以有效地提高全局搜索能力和性能。它可以均匀分布初始粒子，以避免陷入局部最优状态。采用基于等级的策略来调整单个粒子的惯性权重。它同时增强了群体的勘探和开发能力。进行比较实验以验证这三种改进的有效性。实验表明，UCPSO的改进可以有效地提高全局搜索能力和性能。