Abstract: In order to improve the convergence speed and search accuracy of Particle Swarm Optimization algorithm, enhance the algorithm’s ability to jump out of the local extremum and find the global extremum, an improved Simple Particle Swarm Optimization algorithm is given. The algorithm takes the influence of particle inertia, the individual experience and global experience into account and improves the position update formula. The improved algorithm can overcome shortcomings of slow convergence and easy to fall into local extremum. Standard functions test results show that the algorithm’s convergence speed and search accuracy have been greatly improved.

Key words: Simple Particle Swarm Optimization（SPSO）, convergence, improve

摘要： 为了有效提高粒子群优化算法的收敛速度和搜索精度，增强算法跳出局部最优，寻得全局最优的能力，提出了一种改进的简化粒子群优化算法。该算法考虑了粒子惯性、个体经验和全局经验对于位置更新影响力的不同，改进了位置更新公式，克服了粒子群优化算法收敛速度慢和易陷入局部最优的缺点。标准函数测试结果表明该改进算法的收敛速度和搜索精度有了很大的提高。

关键词: 简化粒子群优化算法, 收敛, 改进