Enhancing network cluster synchronization capability based on artificial immune algorithm

With the deeper study on complex networks, more and more attention has been paid to the research on the cluster synchronization phenomena based on complex networks. In the real world, synchronization phenomena or cluster synchronous behaviors occur frequently, some of which may result in larger negative impacts to the society, such as “cadmium rice event,” while others bring significant economic benefits to the society, such as the synchronization of the propaganda for “black Friday.” Therefore, research on cluster synchronism has great values for theoretical study and social applications. Currently, the study of cluster synchronicity is focused on the solution of the synchronization threshold and the analysis of the synchronization phenomenon, etc. However, the optimization to enhance the synchronous evolutionary effect is rarely presented in literatures. To overcome these limitations of current work, we explore the optimization of network structure with artificial immune algorithms under the condition of a constant network scale and finally realize the promotion of synchronous evolution effect in this paper. Moreover, the relevant research results are applied to real cases. On one hand, for the positive synchronous behaviors, the network structure with good synchronization capability is created to achieve better synchronization. On the other hand, the connection between nodes and edges in the network is cut off to avoid the occurrence of negative synchronous behaviors.

[1]  Tinggui Chen,et al.  Library personalized recommendation service method based on improved association rules , 2017, Libr. Hi Tech.

[2]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[3]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[4]  Chanchan Li,et al.  Modeling and analysis of epidemic spreading on community networks with heterogeneity , 2018, Journal of Parallel and Distributed Computing.

[5]  K. H. Chong,et al.  Transform of Artificial Immune System algorithm optimization based on mathematical test function , 2016, 2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE).

[6]  Tugrul Cavdar,et al.  PSO tuned ANFIS equalizer based on fuzzy C-means clustering algorithm , 2016 .

[7]  Mohammad Reza Parsaei,et al.  A new adaptive traffic engineering method for telesurgery using ACO algorithm over Software Defined Networks , 2017 .

[8]  P. Borne,et al.  Lyapunov analysis of sliding motions: Application to bounded control , 1996 .

[9]  Li Tao,et al.  Parameter analysis of negative selection algorithm , 2017, Inf. Sci..

[10]  Chen Yang,et al.  Impact of informal networks on opinion dynamics in hierarchically formal organization , 2015 .

[11]  Georgi S. Medvedev,et al.  The Kuramoto Model on Power Law Graphs: Synchronization and Contrast States , 2018, J. Nonlinear Sci..

[12]  Qian Shen,et al.  A Public Opinion Simulation Framework Based on the Multilayer Synchronization Network , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[13]  Thomas K. D. M. Peron,et al.  The Kuramoto model in complex networks , 2015, 1511.07139.

[14]  Wenan Tan,et al.  The Analysis of Key Nodes in Complex Social Networks , 2017, ICCCS.

[15]  Jean-Louis Deneubourg,et al.  Self-Organization in Primates: Understanding the Rules Underlying Collective Movements , 2011, International Journal of Primatology.

[16]  Chunhua Ju,et al.  Simplifying Multiproject Scheduling Problem Based on Design Structure Matrix and Its Solution by an Improved aiNet Algorithm , 2012 .

[17]  Louis Pecora,et al.  Symmetry- and input-cluster synchronization in networks. , 2018, Physical review. E.

[18]  Ka-Chun Wong,et al.  An adaptive immune-inspired multi-objective algorithm with multiple differential evolution strategies , 2018, Inf. Sci..

[19]  Vito Latora,et al.  Compromise and synchronization in opinion dynamics , 2006 .

[20]  Xu Ye,et al.  Researches on Evaluations of Large-scale Complex Networks Topologies , 2017 .

[21]  D. K. Lobiyal,et al.  Performance evaluation of data aggregation for cluster-based wireless sensor network , 2013, Human-centric Computing and Information Sciences.

[22]  Mohammad Sadegh Helfroush,et al.  A robust gene clustering algorithm based on clonal selection in multiobjective optimization framework , 2018, Expert Syst. Appl..

[23]  Jurgen Kurths,et al.  Rewiring hierarchical scale-free networks: Influence on synchronizability and topology , 2017, 1707.04057.

[24]  Bhekisipho Twala,et al.  Optimising latent features using artificial immune system in collaborative filtering for recommender systems , 2018, Appl. Soft Comput..

[25]  Beom Jun Kim,et al.  Factors that predict better synchronizability on complex networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Tsuyoshi Usagawa,et al.  Dynamic content synchronization between learning management systems over limited bandwidth network , 2011, Human-centric Computing and Information Sciences.

[27]  Mohammad Malkawi,et al.  Artificial neuro fuzzy logic system for detecting human emotions , 2012 .

[28]  A. Pluchino,et al.  CHANGING OPINIONS IN A CHANGING WORLD: A NEW PERSPECTIVE IN SOCIOPHYSICS , 2004 .

[29]  Vito Latora,et al.  Opinion dynamics and synchronization in a network of scientific collaborations , 2006, physics/0607210.

[30]  George Lekakos,et al.  Analysis of Social Network Dynamics with Models from the Theory of Complex Adaptive Systems , 2013, I3E.

[31]  Yu Xue,et al.  An Artificial Immune System Algorithm with Social Learning and Its Application in Industrial PID Controller Design , 2017 .

[32]  Renbin Xiao,et al.  Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization , 2014, TheScientificWorldJournal.

[33]  Tao Zhou,et al.  Relations between average distance, heterogeneity and network synchronizability , 2006 .