自适应混沌粒子群算法对极限学习机参数的优化

针对极限学习机（ELM）在处理非线性数据时效果不理想，并且ELM的参数随机化不利于模型泛化的特点，提出了一种改进的极限学习机算法。结合自适应混沌粒子群（ACPSO）算法对ELM的参数进行优化，以增强算法的稳定性，提高ELM对基因表达数据分类的精度。在UCI基因数据集上进行仿真实验，实验结果表明，与探测粒子群-极限学习机（DPSO-ELM）、粒子群-极限学习机（PSO-ELM）等算法相比，自适应混沌粒子群-极限学习机（ACPSO-ELM）算法具有较好的稳定性、可靠性，且能有效提高基因分类精度。

Optimization of extreme learning machine parameters by adaptive chaotic particle swarm optimization algorithm

Since it was not ideal for Extreme Learning Machine (ELM) to deal with non-linear data, and the parameter randomization of ELM was not conducive for generalizing the model, an improved version of ELM algorithm was proposed. The parameters of ELM were optimized by Adaptive Chaotic Particle Swarm Optimization (ACPSO) algorithm to increase the stability of the algorithm and improve the accuracy of ELM for gene expression data classification. The simulation experiments were carried out on the UCI gene data. The results show that Adaptive Chaotic Particle Swarm Optimization-Extreme Learning Machine (ACPSO-ELM) has good stability and reliability, and effectively improves the accuracy of gene classification over existing algorithms, such as Detecting Particle Swarm Optimization-Extreme Learning Machine (DPSO-ELM) and Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM).