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Particle Swarm Optimizers with Growing Tree Topology

机译:具有不断增长的树形拓扑的粒子群优化器

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摘要

This paper presents a new particle swarm optimizer characterized by growing tree topology. If a particle is stagnated then a new particle is born and is located away from the trap. Depending on the property of objective problems, particles are born successively and the growing swarm constitutes a tree-topology. Performing numerical experiments for typical benchmarks, the algorithm efficiency is evaluated in several key measures such as success rate, the number of iterations and the number of particles. As compared with other basic PSOs, we can suggest that the proposed algorithm has efficient performance in optimization with low-cost computation.
机译:本文提出了一种新的粒子群优化器,其特征在于树的拓扑不断增长。如果颗粒停滞,则新的颗粒将诞生并远离陷阱。根据客观问题的性质,粒子先后出生,并且不断增长的群构成树形拓扑。通过对典型基准进行数值实验,可以通过几个关键指标来评估算法效率,例如成功率,迭代次数和粒子数。与其他基本PSO相比,我们可以建议该算法在优化过程中具有低成本的高效性能。

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