|Table of Contents|

An Ensemble Many-objective Optimization Algorithm Based on Voting and Dynamic Value Point(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2024年04期
Page:
59-67
Research Field:
数学
Publishing date:

Info

Title:
An Ensemble Many-objective Optimization Algorithm Based on Voting and Dynamic Value Point
Author(s):
Liu Xinping1; Sun Yuehong1; 2; Liu Foxiang2; 3
(1.School of Mathematical Sciences,Nanjing Normal University,Nanjing 210023,China)
(2.Ministry of Education Key Laboratory of NSLSCS,Nanjing 210023,China)
(3.School of Information Engineering,Nanchang University,Nanchang 330029,China)
Keywords:
ensemble framework; voting mechanism; many-objective optimization problems; solution-sorting algorithms
PACS:
TP18
DOI:
10.3969/j.issn.1001-4616.2024.04.007
Abstract:
In many-objective optimization problems,the conflicts among the objectives lead to the situation where solutions cannot optimize all objectives simultaneously,and a large number of non-dominated solutions exist in the process. An appropriate solution-sorting method plays a crucial role in evaluating the quality of solutions and the performance of the algorithm. Generally,different solution-sorting methods have their own pros and cons when handling different many-objective problems. Therefore,an ensemble many-objective evolutionary algorithm based on voting and dynamic value point(VDVP-EMEA)is proposed,which can integrate different solution-sorting methods and cooperate together. First,the value points of each expert are dynamically allocated by the voting success rate of each solution-sorting method,and the solution-sorting method with more voting success rate will be correspondingly assigned more value points. Otherwise,the value points will be punished. The last elimination system is used to cancel the votes of the most inefficient experts. The elite selection strategy is used to define fitness of individuals by voting results and value points,and the individuals with greater fitness value are preferentially selected in the process of environment selection. Finally,a large number of experiments are conducted to test the performance of VDVP-EMEA,and VDVP-EMEA is compared with five advanced many-objective evolutionary algorithms NSGA-III,SPEA2,BiGE,GrEA,and VMEF. Experimental results indicate that the overall performance of VDVP-EMEA is distinctly better than these algorithms.

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Last Update: 2024-12-15