CAJ | 학술논문

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.
침대입자군산법(PSO)재심우후기우기재고유수색공간중무법득도만의결과적문제,제출료일충이용전량대신식적개진입자군우화산법.재속도경환공식신가료일부분,해부분표시료입자전량대적신식대자기하일보행위적영향.해부분주요근거당전입자전량대위치구해출기전량대적중심위치,기작용유사우당전전국최우위치.동시심입탐토신가부분적학습인자범위급기대신개진산법적영향.방진실험결과표명,신산법재전국수색능력、수렴속도、정도화은정성방면균유료현저제고.
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.