This paper proposes an effective particle swarm optimization (PSO) based memetic algorithm (MA) for designing artificial neural network. In the proposed PSO-based MA (PSOMA), not only the evolutionary searching mechanism of PSO characterized by individual improvement plus population cooperation and competition is applied to perform the global search, but also several adaptive high-performance faster training algorithms are employed to enhance the local search, so that the exploration and exploitation abilities of PSOMA can be well balanced. Moreover, an effective adaptive MetaLamarckian learning strategy is employed to decide which local search method to be used so as to prevent the premature convergence and concentrate computing effort on promising neighbor solutions. Simulation results and comparisons demonstrate the effectiveness and efficiency of the proposed PSOMA. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Liu, B., Wang, L., Jin, Y., & Huang, D. (2007). Designing neural networks using PSO-based memetic algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4493 LNCS, pp. 219–224). Springer Verlag. https://doi.org/10.1007/978-3-540-72395-0_28
Mendeley helps you to discover research relevant for your work.