Particle Swarm Optimization
Particle Swarm Optimization
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DOI：
10.1201/9780429422614-20
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发表时间：
2018-10
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影响因子：
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通讯作者：
Adam Slowik
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文献类型：
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作者：
Adam Slowik
粒子群优化（PSO）是一种基于群体智能的进化算法，是一种随机优化技术。PSO的一个主要特点是许多配置参数，这使得算法可以根据各种问题进行调整。在本文的前半部分，我们在一个类似分形的测试环境中对PSO的不同参数化的性能进行了经验基准测试，以确定收敛性和对局部最优值的鲁棒性。实验中最显着的参数：阻尼因子，邻域大小和维数。我们评估了一种拟议的技术，消除已知的问题的尺寸崩溃和位置偏差，以及提出了一种新的优化技术，重新启动粒子过早收敛，以增加对局部最优的鲁棒性。然后，PSO成功地应用到一个图像匹配算法称为Evolisa，使用爬山和PSO的组合。PSOlisa还采用了几种优化技术来提高收敛效率。
Particle Swarm Optimization (PSO) is an evolutionary algorithm based off of swarm intelligence and is used as a stochastic optimization technique. One major characteristic of PSO is many configuration parameters, which allow the algorithm to be adjusted to various problem landscapes. In the first half of this paper, we empirically benchmark performance of different parameterizations of PSO in a fractal-like test environment for convergence and robustness against local optima. Experiments were done with most salient parameters: dampening factor, neighborhood size and dimensionality. We evaluate a proposed technique of eliminating the known problem of dimensional collapse and position bias, as well as propose a novel optimization technique for restarting particles which have prematurely converged to increase robustness against local optima. PSO was then successfully applied to an image matching algorithm called Evolisa, using a combination of hill-climbing and PSO. PSOlisa also employs several optimization techniques to improve convergence efficiency.