As a meta-heuristic algoriTthm, particle swarm optimization (PSO) has the advantages of having a simple principle, few required parameters, easy realization and strong adaptability. However, it is easy to fall into a local optimum in the early stage of iteration. Aiming at this shortcoming, this paper presents a hybrid multi-step probability selection particle swarm optimization with sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients (MPSPSO-ST), which can strengthen the overall performance of PSO to a large extent. Firstly, we propose a hybrid multi-step probability selection update mechanism (MPSPSO), which skillfully uses a multi-step process and roulette wheel selection to improve the performance. In order to achieve a good balance between global search capability and local search capability to further enhance the performance of the method, we also design sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients inspired by chaos mechanism and trigonometric functions, which are integrated into the MPSPSO-ST algorithm. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. To evaluate the effectiveness of the MPSPSO-ST algorithm, we conducted extensive experiments with 20 classic benchmark functions. The experimental results show that the MPSPSO-ST algorithm has faster convergence speed, higher optimization accuracy and better robustness, which is competitive in solving numerical optimization problems and outperforms a lot of classical PSO variants and well-known optimization algorithms.

中文翻译：

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用于数值函数优化的动态混沌惯性权重和加速度系数的混合多步概率选择粒子群优化

粒子群优化（PSO）作为一种元启发式算法，具有原理简单、所需参数少、易于实现和适应性强的优点。但是，在迭代初期很容易陷入局部最优。针对这一不足，本文提出了一种具有正弦混沌惯性权重和对称切线混沌加速系数的混合多步概率选择粒子群优化算法（MPSPSO-ST），可以在很大程度上增强粒子群算法的整体性能。首先，我们提出了一种混合多步概率选择更新机制（MPSPSO），它巧妙地使用多步过程和轮盘赌轮选择来提高性能。为了在全局搜索能力和局部搜索能力之间取得良好的平衡以进一步提高方法的性能，我们还受混沌机制和三角函数的启发，设计了正弦混沌惯性权重和对称切线混沌加速度系数，将它们集成到算法中。 MPSPSO-ST 算法。这种策略可以保持群的多样性，以防止过早收敛。为了评估 MPSPSO-ST 算法的有效性，我们对 20 个经典基准函数进行了大量实验。实验结果表明，MPSPSO-ST算法具有更快的收敛速度、更高的优化精度和更好的鲁棒性，