Multiobjective Topology Optimization Based on Mapping Matrix and NSGA-II for Switched Industrial Internet of Things

Abstract

With the deep integration of information and communication technology, the network performance of existing switched Ethernet cannot meet the growing control and management demands of the industrial production. To solve the time-delay and sub-network transmission balance problem, a multiobjective nondominated sorting genetic algorithm based on mapping matrix is proposed, in which a new topology model is established to reflect the flexible Ethernet topology format. Meanwhile, an adjacency mapping matrix is introduced as genetic code to embody the actual topology structure and several special properties of the mapping matrix caused by the industrial topology characteristics are proved. On the basis of these properties, an asexual crossover and double swap mutation operation is proposed to achieve the population evolution. Besides, in the genetic procedure of the proposed algorithm a self-adaptive measure and an elite reservation strategy are applied to accelerate the convergence speed. The validity of proposed algorithm is demonstrated by benchmark test and the case verification shows that the comprehensive performance of the switched Ethernet is improved.