摘要
针对传统二进制粒子群优化(BPSO)算法未充分利用粒子位置的历史信息辅助迭代寻优,从而影响算法寻优效率的进一步提高的问题,提出一种改进的带经验因子的BPSO算法。该算法通过引入反映粒子位置历史信息的经验因子来影响粒子速度的更新,从而引导粒子寻优。为避免粒子对历史信息的过度依赖,算法通过赏罚机制和历史遗忘系数对其进行调节,最后通过经验权重决定经验因子对速度更新的影响。仿真实验结果表明,与经典BPSO算法以及相关改进算法相比,新算法无论在收敛速度还是全局搜索能力上,都能达到更好的效果。
The traditional Binary Particle Swarm Optimization （BPSO） algorithm does not make full use of the historical position information for its iterative optimization, which impedes further improvement on the efficiency of the algorithm. To deal with the problem, an improved BPSO algorithm with the experience factor was proposed. The new algorithm exploited the experience factor, which could reflect the historical information of particle＇s position, to influence the speed update of particles and therefore improved the optimization process. In order to avoid the excessive dependence on the historical experience information of particles, the algorithm regulated the historical information through the reward and punishment mechanism and a history-forgotten coefficient, and in the end, empirical weights were used to determine the final effect on the experience factor. Compared with the classic BPSO and related improved algorithm, the experimental results show that the new algorithm can achieve better effects both in convergence speed and global search ability.
出处
《计算机应用》
CSCD
北大核心
2013年第2期311-315,共5页
journal of Computer Applications
基金
江西省教育厅科技项目(GJJ12305)
江西省自然科学基金资助项目(2010GZS0025)
江西省研究生创新专项资金资助项目(YC2012-X018)
江西省科技支撑计划项目(20123BBE50093)
关键词
二进制粒子群优化
历史信息
赏罚机制
经验因子
经验权重
Binary Particle Swarm Optimization （BPSO）
historical information
reward and punishment mechanism
experience factor
empirical weight
