Abstract
Ant colony optimization (ACO) algorithm has the powerful ability of searching the global optimal solution, and backpropagation (BP) algorithm has the feature of rapid convergence on the local optima. The proper hybrid of the two algorithms (ACO-BP) may accelerate the evolving speed of neural networks and improve the forecasting precision of the well-trained networks. ACO-BP scheme adopts ACO to search the optimal combination of weights in the solution space, and then uses BP algorithm to obtain the accurate optimal solution quickly. The ACO-BP and BP algorithms were applied to the problems of function approaching and modeling quantitative structure-activity relationships of Herbicides. Experiment results show that the proposed ACO-BP scheme is more efficient and effective than BP algorithm. Furthermore, ACO-BP reliably performs well when the number of hidden nodes varies.
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Liu, YP., Wu, MG., Qian, JX. (2006). Evolving Neural Networks Using the Hybrid of Ant Colony Optimization and BP Algorithms. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_105
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DOI: https://doi.org/10.1007/11759966_105
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