摘要
针对粒子群算法(PSO)在寻优后期尤其在高维搜索空间中无法得到满意结果的问题,提出了一种利用前两代信息的改进粒子群优化算法。在速度更换公式新加了一部分,该部分表示了粒子前两代的信息对自己下一步行为的影响。该部分主要根据当前粒子前两代位置求解出其前两代的中心位置,其作用类似于当前全局最优位置。同时深入探讨新加部分的学习因子范围及其对新改进算法的影响。仿真实验结果表明,新算法在全局搜索能力、收敛速度、精度和稳定性方面均有了显著提高。
A modified Particle Swarm Optimization （PSO） on the basis of the two latest generations was proposed to solve the problem that no satisfactory results can be reached during later period of PSO, especially in high-dimensional search space. A new part was added to the velocity of replacement formula, suggesting that the particle comprehensively utilized the information from the previous two acts to instruct its next step. Primarily based on the record of recent changes of the current particle in the two latest generations, the central location of the previous two generations of the particle was calculated, the role of which was to point out the current global optimal position. The paper, at the same time, discussed deeply a new learning factor and their impact on the new modified algorithm. The experimental simulation results show that global searching ability, convergence rate, accuracy and stability of the new algorithm have been improved significantly.
出处
《计算机应用》
CSCD
北大核心
2010年第2期472-475,共4页
journal of Computer Applications
关键词
粒子群算法
中心位置
学习因子
收敛速度
稳定性
Particle Swarm Optimization （PSO）
central location
learning factor
convergence rate
stability
