Abstract
In order to further improve the convergence rate of membrane computing, the membrane computing optimization method based on the catalytic factor (BCMC) is proposed from the inspiration of biological catalyzing enzymes. This algorithm is based on the standard membrane computing, and the catalytic factor is used to control the number of communication objects between membranes, so that the number of communication objects between membrane changes with the change of membrane environment. That is to say, if the average fitness value is relatively larger than the individual fitness value of the membrane, then reduce the number of communication objects of the membrane, conversely, increase the number. In order to test the feasibility and correctness of the algorithm, the simulation test functions are used to simulate, through comparing with the calculated results by using the SGA method, we can see the convergence rate of the membrane computing optimization method based on the catalytic factor is faster and the results are more accurate.
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Wang, F., Huang, Y., Shi, M., Wu, S. (2012). Membrane Computing Optimization Method Based on Catalytic Factor. In: Zhang, H., Hussain, A., Liu, D., Wang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2012. Lecture Notes in Computer Science(), vol 7366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31561-9_14
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DOI: https://doi.org/10.1007/978-3-642-31561-9_14
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