基于自适应加速机制的粒子群算法研究

Peng Liu, Pengjuan Liu
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引用次数: 0

摘要

针对粒子群优化算法参数调整能力差、收敛精度低、容易陷入局部极小值等问题,提出了一种基于自适应加速机制的粒子群优化算法。该算法根据当前粒子位置优先级实时调整粒子飞行加速度,使粒子跳出局部最优位置陷阱,避免过早现象的发生。对该算法的自适应加速机制、收敛精度、抗干扰能力和粒子多样性进行了测试。实验结果表明,具有自适应加速机制的粒子群优化算法不仅增强了局部和全局搜索能力,而且提高了收敛精度和收敛速度,避免了早熟问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Particle Swarm Algorithm Based on Adaptive Acceleration Mechanism
Aiming at the problems of the particle swarm optimization algorithm, such as poor parameter adjustment ability, low convergence accuracy and easy to fall into local minimum, this paper proposes a particle swarm optimization algorithm based on adaptive acceleration mechanism. According to the current particle position priority, the algorithm adjusts the particle flight acceleration in real time, so that the particles jump out of the local optimal position trap and avoid premature phenomenon. Take some tests about the adaptive acceleration mechanism, convergence accuracy, anti-interference ability and particle diversity of the proposed algorithm. The experimental results show that the particle swarm optimization algorithm with adaptive acceleration mechanism not only enhances the local and global search ability, but also improves the convergence accuracy, convergence speed and avoids the premature problem.
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