Letters: An improved PSO-based ANN with simulated annealing technique

Authors:
Yi Da;Ge Xiurun
Affiliations:
School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University, Box A0210091 Shanghai 200240, People's Republic of China;School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University, Box A0210091 Shanghai 200240, People's Republic of China and Institute of Rock and Soil Mechanics, The Chin ...
Venue:
Neurocomputing
Year:
2005

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Abstract

This paper presents a modified particle swarm optimization (PSO) with simulated annealing (SA) technique. An improved PSO-based artificial neural network (ANN) is developed. The results show that the proposed SAPSO-based ANN has a better ability to escape from a local optimum and is more effective than the conventional PSO-based ANN.