In this paper, we present a Population Disturbing Operator based on Ranking to improve the optimization efficiency of genetic algorithm. The operator is a dynamic-adaptive operator, which can not only prevent the population from the coming early convergence but also conduct the existed early convergence. When applying the improved GA to resolve the capacity and flow assignment (CFA) problem, we use a better integer-encoding rather than normal binary-coding. The integer-encoding can reduce many constraints of the CFA optimization model. The test results indicate that the improved GA is very efficient for solving the CFA problem.
['Guangming Lin', 'Chengbo Huang', 'Shaobin Zhan', 'Xin Lu', 'Yunting Lu']
Ranking Based Selection Genetic Algorithm for Capacity Flow Assignments