NSGA-II With Simple Modification Works Well on a Wide Variety of Many-Objective Problems
In the last two decades, the non-dominated sorting genetic algorithm II (NSGA-II) has been the most widely-used evolutionary multi-objective optimization (EMO) algorithm. However, its performance on a wide variety of many-objective test problems has not been examined in the literature. It has been i...
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doaj-aca1338f13cb46e284e0246d2a0f22122021-03-30T04:53:35ZengIEEEIEEE Access2169-35362020-01-01819024019025010.1109/ACCESS.2020.30322409229403NSGA-II With Simple Modification Works Well on a Wide Variety of Many-Objective ProblemsLie Meng Pang0https://orcid.org/0000-0001-7037-1630Hisao Ishibuchi1https://orcid.org/0000-0001-9186-6472Ke Shang2https://orcid.org/0000-0003-2363-9504Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, ChinaDepartment of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, ChinaDepartment of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, ChinaIn the last two decades, the non-dominated sorting genetic algorithm II (NSGA-II) has been the most widely-used evolutionary multi-objective optimization (EMO) algorithm. However, its performance on a wide variety of many-objective test problems has not been examined in the literature. It has been implicitly assumed by EMO researchers that NSGA-II does not work well on many-objective problems. As a result, NSGA-II has always been excluded from performance comparison with recently proposed many-objective EMO algorithms. Recently, it was pointed out that the performance of NSGA-II on many-objective problems is not always bad. In fact, the poor performance of NSGA-II on many-objective problems is mainly due to the existence of dominance resistant solutions. In this article, we show that the negative effect of the dominance resistant solutions can be remedied by slightly modifying objective values of many-objective problems in NSGA-II. Experimental results show that the modified NSGA-II works well on a wide variety of many-objective test problems.https://ieeexplore.ieee.org/document/9229403/Many-objective problemsdominance resistant solutionsevolutionary multi-objective optimizationNSGA-II |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lie Meng Pang Hisao Ishibuchi Ke Shang |
spellingShingle |
Lie Meng Pang Hisao Ishibuchi Ke Shang NSGA-II With Simple Modification Works Well on a Wide Variety of Many-Objective Problems IEEE Access Many-objective problems dominance resistant solutions evolutionary multi-objective optimization NSGA-II |
author_facet |
Lie Meng Pang Hisao Ishibuchi Ke Shang |
author_sort |
Lie Meng Pang |
title |
NSGA-II With Simple Modification Works Well on a Wide Variety of Many-Objective Problems |
title_short |
NSGA-II With Simple Modification Works Well on a Wide Variety of Many-Objective Problems |
title_full |
NSGA-II With Simple Modification Works Well on a Wide Variety of Many-Objective Problems |
title_fullStr |
NSGA-II With Simple Modification Works Well on a Wide Variety of Many-Objective Problems |
title_full_unstemmed |
NSGA-II With Simple Modification Works Well on a Wide Variety of Many-Objective Problems |
title_sort |
nsga-ii with simple modification works well on a wide variety of many-objective problems |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
In the last two decades, the non-dominated sorting genetic algorithm II (NSGA-II) has been the most widely-used evolutionary multi-objective optimization (EMO) algorithm. However, its performance on a wide variety of many-objective test problems has not been examined in the literature. It has been implicitly assumed by EMO researchers that NSGA-II does not work well on many-objective problems. As a result, NSGA-II has always been excluded from performance comparison with recently proposed many-objective EMO algorithms. Recently, it was pointed out that the performance of NSGA-II on many-objective problems is not always bad. In fact, the poor performance of NSGA-II on many-objective problems is mainly due to the existence of dominance resistant solutions. In this article, we show that the negative effect of the dominance resistant solutions can be remedied by slightly modifying objective values of many-objective problems in NSGA-II. Experimental results show that the modified NSGA-II works well on a wide variety of many-objective test problems. |
topic |
Many-objective problems dominance resistant solutions evolutionary multi-objective optimization NSGA-II |
url |
https://ieeexplore.ieee.org/document/9229403/ |
work_keys_str_mv |
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