An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems
Multi-modal multi-objective optimization problem (MMOPs) has attracted more and more attention in evolutionary computing recently. It is not easy to solve these problems using the existing evolutionary algorithms. The non-dominated solution sorting genetic algorithm (NSGA-II) has poor PS distributio...
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| Published in | Information sciences Vol. 585; pp. 441 - 453 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.03.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0020-0255 1872-6291 |
| DOI | 10.1016/j.ins.2021.11.052 |
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| Abstract | Multi-modal multi-objective optimization problem (MMOPs) has attracted more and more attention in evolutionary computing recently. It is not easy to solve these problems using the existing evolutionary algorithms. The non-dominated solution sorting genetic algorithm (NSGA-II) has poor PS distribution and convergence. In this paper, an enhanced fast NSGA-II based on a special congestion strategy and adaptive crossover strategy, namely ASDNSGA-II is proposed. In the ASDNSGA-II, the strategy with a special congestion degree is used to improve the selection strategy. Then a new adaptive crossover strategy is designed by evaluating the advantages and disadvantages of the SBX crossover strategy with the ability to solve high dimensions and the BLX-α with the ability of Pareto solution to produce offspring solutions. These can ensure the generation of offspring solutions around individuals with large crowding degrees and balance the convergence and diversity of decision space and object space. It can improve PS distribution and convergence and maintain PF precision. Eight functions of MMF1-MMF8 from the CEC2020 are selected to prove the effectiveness of the ASDNSGA-II. By comparing several latest multi-modal multi-objective evolutionary algorithms, the results show that the ASDNSGA-II can effectively find the global Pareto solution set and improve the distribution and convergence of PS. |
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| AbstractList | Multi-modal multi-objective optimization problem (MMOPs) has attracted more and more attention in evolutionary computing recently. It is not easy to solve these problems using the existing evolutionary algorithms. The non-dominated solution sorting genetic algorithm (NSGA-II) has poor PS distribution and convergence. In this paper, an enhanced fast NSGA-II based on a special congestion strategy and adaptive crossover strategy, namely ASDNSGA-II is proposed. In the ASDNSGA-II, the strategy with a special congestion degree is used to improve the selection strategy. Then a new adaptive crossover strategy is designed by evaluating the advantages and disadvantages of the SBX crossover strategy with the ability to solve high dimensions and the BLX-α with the ability of Pareto solution to produce offspring solutions. These can ensure the generation of offspring solutions around individuals with large crowding degrees and balance the convergence and diversity of decision space and object space. It can improve PS distribution and convergence and maintain PF precision. Eight functions of MMF1-MMF8 from the CEC2020 are selected to prove the effectiveness of the ASDNSGA-II. By comparing several latest multi-modal multi-objective evolutionary algorithms, the results show that the ASDNSGA-II can effectively find the global Pareto solution set and improve the distribution and convergence of PS. |
| Author | Zhao, Huimin Deng, Wu Zhou, Xiangbing Zhang, Xiaoxiao Zhou, Yongquan Liu, Yi Chen, Huiling |
| Author_xml | – sequence: 1 givenname: Wu surname: Deng fullname: Deng, Wu email: dw7689@163.com organization: School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China – sequence: 2 givenname: Xiaoxiao surname: Zhang fullname: Zhang, Xiaoxiao email: zhangxiaoxiao6056@163.com organization: School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China – sequence: 3 givenname: Yongquan surname: Zhou fullname: Zhou, Yongquan email: yongquanzhou@126.com organization: College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China – sequence: 4 givenname: Yi surname: Liu fullname: Liu, Yi email: liuyi@camic.cn organization: Research Center of Big Data and Information Management, Civil Aviation Management Institute of China, Beijing 100102, China – sequence: 5 givenname: Xiangbing surname: Zhou fullname: Zhou, Xiangbing email: zhouxb@uestc.edu.cn organization: School of Information and Engineering, Sichuan Tourism University, Chendu 610100, China – sequence: 6 givenname: Huiling surname: Chen fullname: Chen, Huiling email: huiling.jlu@gamil.com organization: Computer Science, Wenzhou University, Wenzhou, Zhejiang 325035, China – sequence: 7 givenname: Huimin surname: Zhao fullname: Zhao, Huimin email: hm_zhao1977@126.com organization: School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China |
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| SubjectTerms | Adaptive crossover Multi-modal multi-objective Non-dominated solutions sorting genetic algorithm Pareto solutions Special crowding distance |
| Title | An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems |
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