粒子群优化
多群优化
计算机科学
元启发式
数学优化
无导数优化
最优化问题
适应性
光学(聚焦)
元优化
人工神经网络
算法
人工智能
数学
生态学
物理
光学
生物
作者
Magnus Erik Hvass Pedersen,A.J. Chipperfield
标识
DOI:10.1016/j.asoc.2009.08.029
摘要
The general purpose optimization method known as Particle Swarm Optimization (PSO) has received much attention in past years, with many attempts to find the variant that performs best on a wide variety of optimization problems. The focus of past research has been with making the PSO method more complex, as this is frequently believed to increase its adaptability to other optimization problems. This study takes the opposite approach and simplifies the PSO method. To compare the efficacy of the original PSO and the simplified variant here, an easy technique is presented for efficiently tuning their behavioural parameters. The technique works by employing an overlaid meta-optimizer, which is capable of simultaneously tuning parameters with regard to multiple optimization problems, whereas previous approaches to meta-optimization have tuned behavioural parameters to work well on just a single optimization problem. It is then found that not only the PSO method and its simplified variant have comparable performance for optimizing a number of Artificial Neural Network problems, but also the simplified variant appears to offer a small improvement in some cases.
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