摘要
With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems.Thus,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization problems.Multiple selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto Front.Thus,the accuracy,convergence,and diversity of solutions are improved.ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization algorithm.Experimental results indicate the superiority of ImMAPIO on these test functions.
基金
This work was supported by the National Key Research and Development Program of China(No.2018YFC1604000)
the National Natural Science Foundation of China(Nos.61806138,61772478,U1636220,61961160707,and 61976212)
the Key R&D Program of Shanxi Province(High Technology)(No.201903D121119)
the Key R&D Program of Shanxi Province(International Cooperation)(No.201903D421048)
the Key R&D Program(International Science and Technology Cooperation Project)of Shanxi Province,China(No.201903D421003).
