An Improved Selection Method Based on Crowded Comparison for Multi-Objective Optimization Problems in Intelligent Computing

The main method of dealing with multi-objective optimization problems (MOPs) is the improvements of non-dominated sorting genetic algorithm II (NSGA-II), which have obtained a great success for solving MOPs. It mainly uses a crowded comparison method (CCM) to select the suitable individuals for ente...

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Bibliographic Details
Published inMobile networks and applications Vol. 27; no. 5; pp. 1880 - 1890
Main Authors Gao, Ying, Song, Binjie, Zhao, Hong, Hu, Xiping, Qian, Yekui, Chen, Xinpeng
Format Journal Article
LanguageEnglish
Published New York Springer US 01.10.2022
Springer Nature B.V
Subjects
Archives & records
Communications Engineering
Computer Communication Networks
Computer science
Electrical Engineering
Engineering
Genetic algorithms
IT in Business
Methods
Multiple objective analysis
Networks
Objectives
Optimization
Population
Sorting algorithms
Test sets
Non-dominated sorting genetic algorithm II (NSGA-II)
Multi-objective optimization problems (MOPs)
Intelligent computing
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Summary:The main method of dealing with multi-objective optimization problems (MOPs) is the improvements of non-dominated sorting genetic algorithm II (NSGA-II), which have obtained a great success for solving MOPs. It mainly uses a crowded comparison method (CCM) to select the suitable individuals for enter the next generation. However, the CCM requires to need calculate the crowding distance of each individual, which needs to sort the population according to each objective function and it exhausts a lot of computational burdens. To better deal with this problem, we proposes an improved crowded comparison method (ICCM), which combines CCM with the random selection method (RSM) based on the number of selected individuals. The RSM is an operator that randomly selects the suitable individuals for the next generation according to the number of needed individuals, which can reduce the computational burdens significantly. The performance of ICCM is tested on two different benchmark sets (the ZDT test set and the UF test set). The results show that ICCM can reduce the computational burdens by controlling two different selection methods (i.e., CCM and RSM).
ISSN:1383-469X
1572-8153
DOI:10.1007/s11036-019-01403-7