Improving NSGA-III Algorithm for Solving High-dimensional Many-objective Green Flexible Job Shop Scheduling Problem
Aiming at the poor initial solution quality and low local search efficiency of NSGA-III in solving the many-objective flexible job shop scheduling model,an improved NSGA-III(NSGA-III-TV)is proposed.Based on MSOS encoding,the different mixed initialization strategies are adopted for OS and MS chromosomes to improve the quality of initial solutions.Based on the critical path,an improved N6 neighborhood structure is used for neighborhood search,which effectively reduce the completion time and reducing search randomness.Three effective mutation operators are employed to expand the search space and improve the convergence capability in the later stages.Test results show that NSGA-III-TV has good performance and practicality in solving the high-dimensional many-objective flexible job shop scheduling problems,which provides strong support for the intelligent green transformation and the upgrading of manufacturing workshops of enterprises.
green flexible job shop schedulinghigh-dimensional multi-objective optimizationcritical pathvariable neighborhood search
NETL
NSTL
万方数据
