Soft Computing Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia.
Comput Intell Neurosci. 2011;2011:121787. doi: 10.1155/2011/121787. Epub 2011 Aug 16.
Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SOM lattice structure with Particle Swarm Optimisation which is called ESOMPSO for solving various classification problems. The enhancement of SOM lattice structure is implemented by introducing a new hexagon formulation for better mapping quality in data classification and labeling. The weights of the enhanced SOM are optimised using PSO to obtain better output quality. The proposed method has been tested on various standard datasets with substantial comparisons with existing SOM network and various distance measurement. The results show that our proposed method yields a promising result with better average accuracy and quantisation errors compared to the other methods as well as convincing significant test.
自组织映射 (SOM) 和粒子群优化 (PSO) 的多策略学习由于其处理复杂数据特征的能力，通常在聚类领域中实现。然而，这些多策略学习架构中的一些存在弱点，例如收敛时间慢，总是被困在局部最小值。本文提出了一种基于粒子群优化的 SOM 格结构的多策略学习，称为 ESOMPSO，用于解决各种分类问题。通过引入新的六边形公式来增强 SOM 格结构，以在数据分类和标记中获得更好的映射质量。使用 PSO 优化增强后的 SOM 的权重，以获得更好的输出质量。该方法已在各种标准数据集上进行了测试，并与现有的 SOM 网络和各种距离测量进行了大量比较。结果表明，与其他方法相比，我们提出的方法具有更好的平均准确性和量化误差，并且具有令人信服的显著测试结果。
