基于平均位置学习的改进粒子群算法研究

针对粒子群算法在求解复杂的多维多峰问题时,存在着局部搜索精度不高和易陷入局部最优等不 足,提出了一种基于平均位置学习的改进粒子群算法。 该算法在学习策略上采用比粒子自身适应值更好的邻 近粒子为学习对象,将该算法分两个阶段用不同更新速度公式,阶段一在更新速度公式中引入整个种群所有粒 子位置的平均位置;阶段二在速度更新公式中引入新平均位置,采用贪心策略选择,通过粒子每次更新后选择 的个体比种群历史最优适应值更优,且储存对应个体历史最优位置,在阶段一结束后开始求它们的平均位置。 将平均位置作为学习对象,可增强粒子间的信息交流,同时可平衡算法的局部开发性能与全局搜索能力。 在 CEC2017 测试函数实验中,实验结果显示所提改进算法与另外 4 个算法相比有一定的优势。

Research on Improved Particle Swarm Algorithm Based on Average Position Learning

Particle Swarm Optimization (PSO) has some shortcomings in solving complex multi-dimensional and multi-peak problems, such as low local search accuracy and easy to fall into local optimum. Therefore, this paper presents an improved particle swarm optimization algorithm based on average position learning. In this algorithm, neighboring particles with better adaptive values than the particles themselves are adopted as learning objects in the learning strategy,and the algorithm is divided into two stages with different updating speed formulas. In stage one, the average position of all particle positions in the whole population is introduced into the updating velocity formula. In the second stage, a new average position is introduced into the velocity updating formula, and the greedy strategy is adopted to select the individuals selected after each updating of the particles to be better than the historical optimal adaptive value of the population. In addition, the historical optimal positions of the corresponding individuals are stored, and their average positions are calculated after the end of the first stage. Taking the average position as the learning object can enhance the information exchange among particles, and balance the local development performance and global search ability of the algorithm. In the CEC2017 test function experiment, the experimental results show that the proposed algorithm has certain advantages compared with the other four algorithms.