基于改进粒子群优化的并行极限学习机

摘要

To improve the stability of extreme learning machine( ELM) , an extreme learning machine based on improved particle swarm optimization ( IPSO-ELM ) is proposed. By combining the improved particle swarm optimization with ELM, IPSO-ELM can find the optimal number of nodes in the hidden layer as well as the optimal input weights and hidden biases. Furthermore, a mutation operator is introduced into IPSO-ELM to enhance the diversity of swarm and improve the convergence speed of the random search process. Then, to handle the large-scale electrical load data, a parallel version of IPSO-ELM named PIPSO-ELM is implemented with the popular parallel computing framework Spark. Experimental results of real-life electrical load data show that PIPSO-ELM obtains better stability and scalability with higher efficiency in large-scale electrical load prediction.%为了提高极限学习机( ELM)网络的稳定性,提出基于改进粒子群优化的极限学习机( IPSO-ELM)。结合改进的粒子群优化算法寻找ELM网络中最优的输入权值、隐层偏置及隐层节点数。通过引入变异算子,增强种群的多样性,并提高收敛速度。为了处理大规模电力负荷数据,提出基于 Spark并行计算框架的并行化算法( PIPSO-ELM)。基于真实电力负荷数据的实验表明,PIPSO-ELM具有更高的稳定性及可扩展性,适合处理大规模的电力负荷数据。