Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic
algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the
fitn...Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic
algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the
fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is
improved and a new self-adjustment scheme of σshare is proposed. This algorithm is proved to be very efficient both
computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA
difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.展开更多
基金国家自然科学基金(No.11901189)湖南省教育厅科研项目(No.19A191)支持partially supported by Center for Computational Mathematics and Applications,The Pennsylvania State University。
文摘Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic
algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the
fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is
improved and a new self-adjustment scheme of σshare is proposed. This algorithm is proved to be very efficient both
computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA
difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.