The particle swarm - Explosion, stability, and convergence in a multidimensional complex space
The particle swarm - Explosion, stability, and convergence in a multidimensional complex space
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DOI：
10.1109/4235.985692
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发表时间：
2002-02-01
影响因子：
14.3
通讯作者：
Kennedy, J
中科院分区：
文献类型：
--
作者：
Clerc, M;Kennedy, J
粒子群是一种通过粒子群中个体的相互作用来寻找复杂搜索空间的最优区域的算法。尽管这种基于社交互动隐喻的算法表现良好，但研究人员还没有充分解释它是如何工作的。此外，该算法的传统版本具有一些不期望的动力学特性，特别是需要限制粒子的速度以控制它们的轨迹。本文首先分析了粒子在离散时间中运动的轨迹（代数观点），然后发展到在连续时间中运动的轨迹（解析观点）。一个五维的描绘，它描述了系统的完整。这些分析导致一个广义模型的算法，包含一组系数来控制系统的收敛趋势。粒子群优化算法的一些结果，实施修改来自分析，建议改变原始算法的方式，消除问题，并增加粒子群的能力，找到最优的一些研究良好的测试功能的方法。
The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. Even though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately explained how it works. Further, traditional versions of the algorithm have had some undesirable dynamical properties, notably the particles' velocities needed to be limited in order to control their trajectories. The present paper analyzes a particle's trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system completely. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the ability of the particle swarm to find optima of some well-studied test functions.