Abstract: A Parallel Particle Swarm Optimization algorithm based on Cultural Evolution (PPSOCE) is proposed to improve the performance of particle swarm optimization for application to large-scale problem. The detailed realization of the method is illustrated. The example of other literatures is computed. By comparison result, it can be found that this proposed algorithm illustrates its higher searching efficiency and better stability.

Key words: cultural evolution, parallel, particle swarm optimization, knapsack problems

摘要： 为了改善粒子群算法对大规模问题求解的性能，提出一种基于文化进化的并行粒子群算法，阐述了该算法的原理和具体实施方案。选取背包问题作为算法的应用对象，通过对仿真实例进行计算和结果比较，表明该算法在最优值、求解速度、稳定性等方面具有较好的 效果。

关键词: 文化进化, 并行, 粒子群算法, 背包问题

CLC Number: 

• TP18