In the process of optimizing the mass exchange network by random walk algorithm with compulsive evolution, some individuals are in a long-term disadvantaged state in the competition and the individual structure is highly similar. RWCE algorithm with individual elimination is proposed to optimize the mass exchange network. That is, in a certain period, through the real-time monitoring of the optimal state of the individuals in the population, the network structure of the individual is first standardized, so as to identify the similar quality exchangers in the structure, and then evaluate the structural similarity of the individual according to its number. Divide the individuals in the population into several groups, and use the total annual cost as an indicator to evaluate the individual optimization performance, and eliminate the disadvantaged and similar individuals in the population. Strengthen the information exchange of individuals between populations, improve individual optimization vitality and population diversity, and effectively enhance the global search ability of the algorithm to optimize the mass exchange network. Applying this method to two examples of mass exchange networks, the optimization results are better than the best results in the literature, indicating that the method can change the evolution direction of the structure, stimulate the differential evolution of individuals between populations and maintain the optimization vitality of individuals, and effectively improve the global optimization performance of the algorithm.
