An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy
Non-dominated sorting genetic algorithm II is a classical multi-objective optimization algorithm but it suffers from poor diversity and the tendency to fall into a local optimum. In this paper, we propose an improved non-dominated sorting genetic algorithm, which aims to address the issues of poor g...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/22/11573 |
| _version_ | 1797465981840261120 |
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| author | Jian Hao Xu Yang Chen Wang Rang Tu Tao Zhang |
| author_facet | Jian Hao Xu Yang Chen Wang Rang Tu Tao Zhang |
| author_sort | Jian Hao |
| collection | DOAJ |
| description | Non-dominated sorting genetic algorithm II is a classical multi-objective optimization algorithm but it suffers from poor diversity and the tendency to fall into a local optimum. In this paper, we propose an improved non-dominated sorting genetic algorithm, which aims to address the issues of poor global optimization ability and poor convergence ability. The improved NSGA-II algorithm not only uses Levy distribution for global search, which enables the algorithm to search a wider range, but also improves the local search capability by using the relatively concentrated search property of random walk. Moreover, an adaptive balance parameter is designed to adjust the respective contributions of the exploration and exploitation abilities, which lead to a faster search of the algorithm. It helps to expand the search area, which increases the diversity of the population and avoids getting trapped in a local optimum. The superiority of the improved NSGA-II algorithm is demonstrated through benchmark test functions and a practical application. It is shown that the improved strategy provides an effective improvement in the convergence and diversity of the traditional algorithm. |
| first_indexed | 2024-03-09T18:30:19Z |
| format | Article |
| id | doaj.art-6b284ded31ad4e678a1126077ceae8af |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T18:30:19Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6b284ded31ad4e678a1126077ceae8af2023-11-24T07:37:28ZengMDPI AGApplied Sciences2076-34172022-11-0112221157310.3390/app122211573An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching StrategyJian Hao0Xu Yang1Chen Wang2Rang Tu3Tao Zhang4Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaNorinco International Armament Research & Development Center, Beijing 100053, ChinaSchool of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaKey Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaNon-dominated sorting genetic algorithm II is a classical multi-objective optimization algorithm but it suffers from poor diversity and the tendency to fall into a local optimum. In this paper, we propose an improved non-dominated sorting genetic algorithm, which aims to address the issues of poor global optimization ability and poor convergence ability. The improved NSGA-II algorithm not only uses Levy distribution for global search, which enables the algorithm to search a wider range, but also improves the local search capability by using the relatively concentrated search property of random walk. Moreover, an adaptive balance parameter is designed to adjust the respective contributions of the exploration and exploitation abilities, which lead to a faster search of the algorithm. It helps to expand the search area, which increases the diversity of the population and avoids getting trapped in a local optimum. The superiority of the improved NSGA-II algorithm is demonstrated through benchmark test functions and a practical application. It is shown that the improved strategy provides an effective improvement in the convergence and diversity of the traditional algorithm.https://www.mdpi.com/2076-3417/12/22/11573non-dominated sorting genetic algorithmmulti-objective optimizationLevy distributionrandom walkadaptive parameter |
| spellingShingle | Jian Hao Xu Yang Chen Wang Rang Tu Tao Zhang An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy Applied Sciences non-dominated sorting genetic algorithm multi-objective optimization Levy distribution random walk adaptive parameter |
| title | An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy |
| title_full | An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy |
| title_fullStr | An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy |
| title_full_unstemmed | An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy |
| title_short | An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy |
| title_sort | improved nsga ii algorithm based on adaptive weighting and searching strategy |
| topic | non-dominated sorting genetic algorithm multi-objective optimization Levy distribution random walk adaptive parameter |
| url | https://www.mdpi.com/2076-3417/12/22/11573 |
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