An Improved Multiobjective Algorithm: DNSGA2-PSA
In general, the proximities to a certain diversity along the front and the Pareto front have the equal importance for solving multiobjective optimization problems (MOPs). However, most of the existing evolutionary algorithms give priority to the proximity over the diversity. To improve the diversity...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2018/9697104 |
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| author | Dan Qu Xianfeng Ding Hongmei Wang |
| author_facet | Dan Qu Xianfeng Ding Hongmei Wang |
| author_sort | Dan Qu |
| collection | DOAJ |
| description | In general, the proximities to a certain diversity along the front and the Pareto front have the equal importance for solving multiobjective optimization problems (MOPs). However, most of the existing evolutionary algorithms give priority to the proximity over the diversity. To improve the diversity and decrease execution time of the nondominated sorting genetic algorithm II (NSGA-II), an improved algorithm is presented in this paper, which adopts a new vector ranking scheme to decrease the whole runtime and utilize Part and Select Algorithm (PSA) to maintain the diversity. In this algorithm, a more efficient implementation of nondominated sorting, namely, dominance degree approach for nondominated sorting (DDA-NS), is presented. Moreover, an improved diversity preservation mechanism is proposed to select a well-diversified set out of an arbitrary given set. By embedding PSA and DDA-NS into NSGA-II, denoted as DNSGA2-PSA, the whole runtime of the algorithm is decreased significantly and the exploitation of diversity is enhanced. The computational experiments show that the combination of both (DDA-NS, PSA) to NSGA-II is better than the isolated use cases, and DNSGA2-PSA still performs well in the high-dimensional cases. |
| format | Article |
| id | doaj-art-65394f1f88b94ed2969aa2cfc41b38c9 |
| institution | Kabale University |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-65394f1f88b94ed2969aa2cfc41b38c92025-02-03T01:11:16ZengWileyJournal of Robotics1687-96001687-96192018-01-01201810.1155/2018/96971049697104An Improved Multiobjective Algorithm: DNSGA2-PSADan Qu0Xianfeng Ding1Hongmei Wang2School of Science, Southwest Petroleum University, Sichuan, Chengdu 610500, ChinaSchool of Science, Southwest Petroleum University, Sichuan, Chengdu 610500, ChinaSchool of Science, Southwest Petroleum University, Sichuan, Chengdu 610500, ChinaIn general, the proximities to a certain diversity along the front and the Pareto front have the equal importance for solving multiobjective optimization problems (MOPs). However, most of the existing evolutionary algorithms give priority to the proximity over the diversity. To improve the diversity and decrease execution time of the nondominated sorting genetic algorithm II (NSGA-II), an improved algorithm is presented in this paper, which adopts a new vector ranking scheme to decrease the whole runtime and utilize Part and Select Algorithm (PSA) to maintain the diversity. In this algorithm, a more efficient implementation of nondominated sorting, namely, dominance degree approach for nondominated sorting (DDA-NS), is presented. Moreover, an improved diversity preservation mechanism is proposed to select a well-diversified set out of an arbitrary given set. By embedding PSA and DDA-NS into NSGA-II, denoted as DNSGA2-PSA, the whole runtime of the algorithm is decreased significantly and the exploitation of diversity is enhanced. The computational experiments show that the combination of both (DDA-NS, PSA) to NSGA-II is better than the isolated use cases, and DNSGA2-PSA still performs well in the high-dimensional cases.http://dx.doi.org/10.1155/2018/9697104 |
| spellingShingle | Dan Qu Xianfeng Ding Hongmei Wang An Improved Multiobjective Algorithm: DNSGA2-PSA Journal of Robotics |
| title | An Improved Multiobjective Algorithm: DNSGA2-PSA |
| title_full | An Improved Multiobjective Algorithm: DNSGA2-PSA |
| title_fullStr | An Improved Multiobjective Algorithm: DNSGA2-PSA |
| title_full_unstemmed | An Improved Multiobjective Algorithm: DNSGA2-PSA |
| title_short | An Improved Multiobjective Algorithm: DNSGA2-PSA |
| title_sort | improved multiobjective algorithm dnsga2 psa |
| url | http://dx.doi.org/10.1155/2018/9697104 |
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