This article introduces a robust hybrid method for solving supervised learning tasks, which uses the Echo State Network (ESN) model and the Particle Swarm Optimization (PSO) algorithm. An ESN is a Recurrent Neural Network with the hidden-hidden weights fixed in the learning process. The recurrent part of the network stores the input information in internal states of the network. Another structure forms a free-memory method used as supervised learning tool. The setting procedure for initializing the recurrent structure of the ESN model can impact on the model performance. On the other hand, the PSO has been shown to be a successful technique for finding optimal points in complex spaces. Here, we present an approach to use the PSO for finding some initial hidden-hidden weights of the ESN model. We present empirical results that compare the canonical ESN model with this hybrid method on a wide range of benchmark problems.
Dynamic Multi-Objective Lion Swarm Optimization with Multi-strategy Fusion: An application in 6R robot trajectory planning
College of control science and engineering, China University of Petroleum (East China)（中国石油大学（华东）控制科学与工程学院）；Commonwealth Scientific and Industrial Research Organisation (CSIRO)（联邦科学与工业研究组织（CSIRO））
Enhancing Discrete Particle Swarm Optimization for Hypergraph-Modeled Influence Maximization
School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education（教育部社会计算与认知智能重点实验室（大连理工大学））；International School of Information Science & Engineering, Dalian University of Technology（大连理工大学国际信息与软件工程学院）；School of Artificial Intelligence, Shanxi University（山西大学人工智能学院）；College of Software, Northeastern University（东北大学软件学院）
Structuring Multiple Simple Cycle Reservoirs with Particle Swarm Optimization
International Research Center for Neurointelligence, The University of Tokyo（国际神经智能研究中心，东京大学）；School of Computer Science, University of Birmingham（伯明翰大学计算机科学学院）；Graduate School of Medicine, The University of Tokyo（东京大学医学研究生院）；Department of Computer Science, Graduate School of Engineering, Nagoya Institute of Technology（名古屋工业大学工学研究生院计算机科学系）
Discrete Differential Evolution Particle Swarm Optimization Algorithm for Energy Saving Flexible Job Shop Scheduling Problem Considering Machine Multi States
Business School, Shandong Normal University（山东师范大学商学院）；School of Mathematics, Yunnan Normal University（云南师范大学数学学院）；School of Physics and Electronics, Shandong Normal University（山东师范大学物理与电子科学学院）
