多策略融合的改进粒子群优化算法

为解决传统粒子群算法收敛精度低、收敛速度慢和易陷入局部最优的问题，提出了一种多策略融合的改进粒子群算法。首先，设计了一种基于中垂线算法的游离粒子位置更新方法，加快了游离粒子的收敛速度；其次，设计了一种在最优粒子附近生成爆炸粒子的策略，以增强算法的寻优精度和寻优速度，为适应前两个策略，还设计了一种仅依靠全局最优粒子位置的粒子速度更新策略；最后，将基于概率分层的简化粒子群优化算法的惯性权重和粒子位置更新方法用于本算法。与其他五种改进粒子群算法进行了对比实验，结果表明提出的改进算法无论是处理低维问题还是高维问题表现均具有较大优势，性能更优越。

Improved particle swarm optimization algorithm with multi-strategy fusion

To solve the problems of low convergence accuracy, slow convergence speed and easy to fall into local optimum of traditional particle swarm algorithm, this paper proposed an improved particle swarm algorithm with multi-strategy fusion. Firstly, in order to accelerate the convergence speed of free particles, the improved algorithm used a method of updating the position of free particles based on the midperpendicular algorithm. Secondly, the improved algorithm designed a strategy of generating exploding particles near the optimal particles to enhance the optimization-seeking accuracy and optimization-seeking speed of the algorithm, and the improved algorithm also designed a particle velocity updating strategy relying only on the global optimal particle position to accommodate the first two strategies. Finally, the algorithm also used the inertia weights and particle position update methods of the simplified particle swarm optimization algorithm based on probabilistic hierarchy. This paper designed a few comparison experiments with other five improved particle swarm algorithms, and the results show that the improved algorithm has a greater advantage and better performance whether dealing with low-dimensional problems or high-dimensional problems.