Issue No. 03 - May-June (2013 vol. 10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.72
Yang Tang , Inst. of Phys., Humboldt Univ. of Berlin, Berlin, Germany
Huijun Gao , Res. Inst. of Intell. Control & Syst., Harbin Inst. of Technol., Harbin, China
Jurgen Kurths , Dept. of Transdisciplinary Concepts & Methods, Potsdam Inst. for Climate Impact Res., Potsdam, Germany
In this paper, we investigate the multiobjective identification of controlling areas in the neuronal network of a cat's brain by considering two measures of controllability simultaneously. By utilizing nondominated sorting mechanisms and composite differential evolution (CoDE), a reference-point-based nondominated sorting composite differential evolution (RP-NSCDE) is developed to tackle the multiobjective identification of controlling areas in the neuronal network. The proposed RP-NSCDE shows its promising performance in terms of accuracy and convergence speed, in comparison to nondominated sorting genetic algorithms II. The proposed method is also compared with other representative statistical methods in the complex network theory, single objective, and constraint optimization methods to illustrate its effectiveness and reliability. It is shown that there exists a tradeoff between minimizing two objectives, and therefore pareto fronts (PFs) can be plotted. The developed approaches and findings can also be applied to coordination control of various kinds of real-world complex networks including biological networks and social networks, and so on.
Biological neural networks, Optimization, Controllability, Complex networks, Vectors, Sorting, Evolutionary computation,controlling areas, Pareto optimisation, brain, minimisation, neurophysiology, multiobjective identification, neuronal network controlling areas, social networks, biological networks, real-world complex networks, pareto fronts, objective minimization, RP-NSCDE, reference-point-based nondominated sorting composite differential evolution, cat brain, Biological neural networks, Optimization, Controllability, Complex networks, Vectors, Sorting, Evolutionary computation, multiobjective optimization, Synchronization, neuronal networks
Yang Tang, Huijun Gao, Jurgen Kurths, "Multiobjective Identification of Controlling Areas in Neuronal Networks", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. , pp. 708-720, May-June 2013, doi:10.1109/TCBB.2013.72